From 7694b8b53ecda63235a53bc5018371b33e2478a7 Mon Sep 17 00:00:00 2001 From: colinmixonn <109253437+colinmixonn@users.noreply.github.com> Date: Wed, 10 Dec 2025 15:33:45 -0800 Subject: [PATCH 001/164] Add blog post on AKS Configurable Scheduler Profiles This blog post introduces AKS Configurable Scheduler Profiles, highlighting their benefits for optimizing resource utilization and improving scheduling strategies for web-distributed and AI workloads. It covers configuration examples for GPU utilization, pod distribution across topology domains, and memory-optimized scheduling. --- .../index.md | 209 ++++++++++++++++++ 1 file changed, 209 insertions(+) create mode 100644 website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md diff --git a/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md b/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md new file mode 100644 index 000000000..0b5445719 --- /dev/null +++ b/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md @@ -0,0 +1,209 @@ +--- +title: "Announcing Azure Configurable Scheduler Profiles: Optimize resource ROI with fine grained scheduling strategies." +description: "Avoid scheduling inefficiencies and improve GPU utilization with AKS Configurable Scheduler Profiles" +date: 2025-12-16 +authors: + - Colin Mixon +categories: +- operations +- ai +tags: + - AI + - performance + - Scheduler +--- +# Avoid scheduling inefficiencies and improve GPU utilizaiton with AKS Configurable Scheduler Profiles (Preview) +Thoughtful scheduling strategies can resolve pervasive challenges across web-distributed workloads and AI workloads like resiliency and resource utilization. But the default scheduler was primarily designed for general-purpose workloads and out-of-box pod scheduling. The scheduler ultimately selects the optimal node for pod(s) based on several criteria, including (but not limited to): + +1. Resource requirements (CPU, memory) +2. Node affinity/anti-affinity +3. Pod affinity/anti-affinity +4. Taints and tolerations + +The criteria, and their respective priority in the scheduling cycle, are not suitable for advanced use cases that might require custom scheduling strategies. Nor, does the default scheduler enable user customization for fine-grain pod placement control while avoiding managing a second custom scheduler. For example, users running batch jobs might prefer collocating on a few nodes for better performance or cost-sensitive workloads might benefit from node binpacking to minimize idle node costs. + +To support these advanced use cases, and to give users more control, use [AKS Configurable Scheduler Profiles][https://learn.microsoft.com/en-us/azure/aks/concepts-scheduler-configuration] to tailor a sheduler to their specific workload requirements using node bin-packing, preemption, and 16 other scheduling plugins that can optimize ROI​, improve gpu utilization, improve data locality, or increase resliency. + +In this blog you will learn how to configure the AKS Configurable Scheduler Profiles for [bin packing GPU-backed nodes][#increase-gpu-utilization-by-bin-packing-gpu-backed-nodes], [distributing pods across topologies][#increase-reselieince-by-distributing-pods-across-topology-domains], and [placing jobs on memory-optimized, pvc-ready nodes][#reduce-latency-with-memory‑optimized-pvc‑aware-scheduling]. Lastly, you will find [Best Practices and Configuration Considerations][#best-practice-and-configuration-considerations] to help guide how you consider both individual plugin configurations, your custom scheduler configuration, and your Deployment design holistically. + +## AKS Configurable Scheduler Profiles +A scheduler profile is a set of one or more in-tree scheduling plugins and configurations that dictate how to schedule a pod. Previously, the scheduler configuration wasn't accessible to users. Starting from Kubernetes version 1.33, you can now configure and set a scheduler profile for the AKS scheduler on your cluster. + +AKS supports 18 in-tree Kubernetes scheduling plugins that allow pods to be placed on user-specified nodes, ensure pods are matched with specific storage resources, and more. The plugins can be generally grouped into the following categories: + +1. Scheduling constraints and order-based plugins +2. Node selection constraints scheduling plugins +3. Resource and topology optimization scheduling plugins + +Below you will find example configurations for some of the most common workload objectives. +:::note +Adjust VM SKUs in NodeAffinity, shift utilization curves or weights, and use the right zones for your cluster(s) in the configurations below. +::: + +### Increse GPU Utilization by Bin Packing GPU-backed Nodes +You can use `NodeResourceFit` to control how pods are assigned to nodes based on available resources (CPU, memory, etc.), including favoring nodes with high resource utilization, within the set configuration. + +For example, scheduling pending jobs on nodes with a higher relative GPU utilization, users can reduce costs and increase GPU Utilization while maintaining performance. + +**This scheduler configuration maximizes GPU efficiency for larger batch jobs by cobsolidating smaller jobs onto fewer nodes and lowering the operational cost of underutilized resources without sacrificing performance.** +```yaml +apiVersion: aks.azure.com/v1alpha1 +kind: SchedulerConfiguration +metadata: + name: upstream +spec: + rawConfig: | + apiVersion: kubescheduler.config.k8s.io/v1 + kind: KubeSchedulerConfiguration + - schedulerName: gpu-node-binpacking-scheduler + plugins: + multiPoint: + enabled: + - name: ImageLocality + - name: NodeResourceFit + - name: NodeResourcesBalancedAllocation + pluginConfig: + - name: NodeResourcesFit + args: + scoringStrategy: + type: MostAllocated + resources: + - name: cpu + weight: 1 + - name: nvidia.com/gpu + weight: 3 + - name: NodeResourcesBalancedAllocation + args: + resources: + - name: nvidia.com/gpu + weight: 1 +``` +### Increase reselieince by distributing pods across topology domains +`PodTopologySpread` is a scheduling strategy that seeks to distribute pods evenly across failure domains (such as availability zones or regions) to ensure high availability and fault tolerance in the event of zone or node failures. + +For example, spreading replicas across distinct zones safeguards availability during an AZ outage, while a softer host‑level rule prevents scheduling deadlocks when cluster capacity is uneven. + +**This configuration is effective for highly‑available stateless services (web/API, gateways) or distributed messaging clusters, like Kafka brokers, that rely on the availability of multipe replicas.** +```yaml +apiVersion: aks.azure.com/v1alpha1 +kind: SchedulerConfiguration +metadata: + name: upstream +spec: + rawConfig: | + apiVersion: kubescheduler.config.k8s.io/v1 + kind: KubeSchedulerConfiguration + profiles: + - schedulerName: pod-distribution-scheduler + pluginConfig: + - name: PodTopologySpread + args: + defaultingType: List + defaultConstraints: + - maxSkew: 2 + topologyKey: topology.kubernetes.io/zone + whenUnsatisfiable: DoNotSchedule + - maxSkew: 1 + topologyKey: kubernetes.io/hostname + whenUnsatisfiable: ScheduleAnyway +``` +### Reduce latency with memory‑optimized, PVC‑aware scheduling +Use `VolumeBinding` to ensure pods are placed on nodes where _PersistentVolumeClaim's_ (PVC) can bind to _PersistentVolume's_ (PV). `VolumeZone` validates that nodes and volumes satisfy zonal requirements to avoid cross-zone storage access. + +For example, combine `VolumeBinding` and `VolumeZone` plugins, with `NodeAffinity` and `NodeResourcesFit` with `RequestedToCapacityRatio`, to influence pod placement on [Azure memory-optimized SKUs][https://learn.microsoft.com/en-us/azure/virtual-machines/sizes/overview?tabs=breakdownseries%2Cgeneralsizelist%2Ccomputesizelist%2Cmemorysizelist%2Cstoragesizelist%2Cgpusizelist%2Cfpgasizelist%2Chpcsizelist#memory-optimized], while ensuring PVC's bind quickly in the target zone to minimize cross‑zone traffic and latency. + +**This scheduler configuration ensures workloads needing large memory footprints are placed on nodes that provide sufficient RAM and maintain proximity to their volumes, enabling fast, zone‑aligned PVC binding for optimal data locality.** +```yaml +apiVersion: aks.azure.com/v1alpha1 +kind: SchedulerConfiguration +metadata: + name: upstream +spec: + rawConfig: | + apiVersion: kubescheduler.config.k8s.io/v1 + kind: KubeSchedulerConfiguration + profiles: + - schedulerName: mem-optimized-node-scheduler + plugins: + multiPoint: + enabled: + - name: NodeAffinity + - name: NodeResourcesFit + - name: VolumeBinding + - name: VolumeZone + pluginConfig: + - name: NodeAffinity + args: + apiVersion: kubescheduler.config.k8s.io/v1 + kind: NodeAffinityArgs + addedAffinity: + preferredDuringSchedulingIgnoredDuringExecution: + - weight: 100 + preference: + matchExpressions: + - key: topology.kubernetes.io/zone + operator: In + values: [westus3-1, westus3-2, westus3-3] + - weight: 80 + preference: + matchExpressions: + - key: node.kubernetes.io/instance-type + operator: In + values: + - Standard_E16s_v5 + - Standard_E32s_v5 + - name: VolumeBinding + args: + apiVersion: kubescheduler.config.k8s.io/v1 + kind: VolumeBindingArgs + bindTimeoutSeconds: 300 + - name: NodeResourcesFit + args: + apiVersion: kubescheduler.config.k8s.io/v1 + kind: NodeResourcesFitArgs + scoringStrategy: + type: RequestedToCapacityRatio + resources: + - name: memory + weight: 5 + - name: cpu + weight: 1 + - name: ephemeral-storage + weight: 1 + requestedToCapacityRatio: + shape: + - utilization: 0 + score: 0 + - utilization: 30 + score: 4 + - utilization: 60 + score: 8 + - utilization: 80 + score: 10 + - utilization: 90 + score: 6 + - utilization: 100 + score: 0 +``` + +## Best Practices and Configuration Considerations +As a reminder, there are many parameters the scheduler considers across the [scheduling cycle][https://kubernetes.io/docs/concepts/scheduling-eviction/scheduling-framework/#interfaces] before a pod is placed on a node that impacts how a pod is assigned. This section is meant to help guide how you consider both individual plugin configurations, your custom scheduler configuration, and your Deployment design holistically. + +1. Ensure the intended deployment is assigned to the _correct_ scheduler profile. +2. Ensure the customer scheduler profile complements the implementation of Deployments, StorageClasses, and PersistentVolumeClaim's. Misalignment can lead to pending pods and degraded workload performance, even when the scheduler is functioning as expected. +3. Ensure their are enough nodes in each zone to accommodate your deployment replicas and ensure your AKS node pool spans the right availability zones. If not, pods may remain in a pending state. +4. Use namespaces to separate workloads which improves your ability to validate or troubleshoot. +5. Assign `priorityClassName` for workloads that should preempt others, this is critical if you use the DefaultPreemption plugin. +6. If you use the `ImageLocality` plugin, use DaemonSets or node pre-pulling for latency-sensitive images, otherwise the benefit may be minimal. +7. If your cluster is large, a low `PercentageOfNodesToScore` speeds scheduling by reducing the number of nodes scored, _but_ it may reduce optimal placement. +8. If you enable a plugin in the `plugins:multipoint` section but do not define it in `pluginConfig:`, AKS uses the default configuration for that plugin. +9. For `NodeResourcesFit`, the ratio matters more than absolute values. So CPU:Memory:Storage = 3:1:2, which means CPU is 3× more influential than memory, and storage is 2x more influential than memory in the scoring phase. +10. Pair `PodTopologySpread` with pod disruption budget's (PDB) and multi‑replica strategies for HA during upgrades. + +## Next Steps: Try out AKS Configurable Scheduler + +With AKS Configurable Scheduler Profiles, teams gain fine-grained control over pod placement strategies like bin-packing, topology distribution, and resource-based scoring that directly address the challenges of resielince and resource utilization for web-distributed workloads and AI workloads. By leveraging these advanced scheduling plugins, AKS users can ensure their workloads make full use of available GPU capacity, reduce idle time, and avoid costly overprovisioning. This not only improves ROI but also accelerates innovation by allowing more jobs to run concurrently and reliably. + +- For best practices using the kube-scheduler visit [kube-scheduler best practices][https://learn.microsoft.com/en-us/azure/aks/operator-best-practices-advanced-scheduler] +- Configure your workload specific scheduler using the [AKS Configurable Scheduler][https://learn.microsoft.com/en-us/azure/aks/concepts-scheduler-configuration] +- If additional capabilities or ML frameworks are needed to schedule and queue batch workloads, you can [install and configure Kueue on AKS][https://learn.microsoft.com/en-us/azure/aks/kueue-overview] to ensure efficient, policy-driven scheduling in AKS clusters. From 8103dda5ff0b16ff7baf19bdc3ac520408c8c498 Mon Sep 17 00:00:00 2001 From: colinmixonn <109253437+colinmixonn@users.noreply.github.com> Date: Wed, 10 Dec 2025 15:40:22 -0800 Subject: [PATCH 002/164] Add Scheduler tag to blog tags configuration Added a new tag for Scheduler with relevant details. --- website/blog/tags.yml | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/website/blog/tags.yml b/website/blog/tags.yml index c4d4c4117..bf7a4cbc3 100644 --- a/website/blog/tags.yml +++ b/website/blog/tags.yml @@ -272,6 +272,11 @@ scaling: permalink: /scaling description: Horizontal and vertical scaling strategies, autoscaler tuning, and capacity planning. +scheduler: + label: Scheduler + permalink: /scheduler + description: Techniques and capabilities to control pod placement, improve resource efficiency, and tune scheduling for diverse AKS workloads. + security: label: Security permalink: /security From 6c035be3048edc88625d2147aa39cfb728774f18 Mon Sep 17 00:00:00 2001 From: colinmixonn <109253437+colinmixonn@users.noreply.github.com> Date: Wed, 10 Dec 2025 15:42:27 -0800 Subject: [PATCH 003/164] Update index.md --- .../2025-12-16-aks-config-scheduler-profiles-preview/index.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md b/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md index 0b5445719..e947798d4 100644 --- a/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md +++ b/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md @@ -22,7 +22,7 @@ Thoughtful scheduling strategies can resolve pervasive challenges across web-dis The criteria, and their respective priority in the scheduling cycle, are not suitable for advanced use cases that might require custom scheduling strategies. Nor, does the default scheduler enable user customization for fine-grain pod placement control while avoiding managing a second custom scheduler. For example, users running batch jobs might prefer collocating on a few nodes for better performance or cost-sensitive workloads might benefit from node binpacking to minimize idle node costs. -To support these advanced use cases, and to give users more control, use [AKS Configurable Scheduler Profiles][https://learn.microsoft.com/en-us/azure/aks/concepts-scheduler-configuration] to tailor a sheduler to their specific workload requirements using node bin-packing, preemption, and 16 other scheduling plugins that can optimize ROI​, improve gpu utilization, improve data locality, or increase resliency. +To support these advanced use cases, and to give users more control, use [AKS Configurable Scheduler Profiles][https://learn.microsoft.com/en-us/azure/aks/concepts-scheduler-configuration] to tailor a scheduler to their specific workload requirements using node bin-packing, preemption, and 16 other scheduling plugins that can optimize ROI​, improve gpu utilization, improve data locality, or increase resliency. In this blog you will learn how to configure the AKS Configurable Scheduler Profiles for [bin packing GPU-backed nodes][#increase-gpu-utilization-by-bin-packing-gpu-backed-nodes], [distributing pods across topologies][#increase-reselieince-by-distributing-pods-across-topology-domains], and [placing jobs on memory-optimized, pvc-ready nodes][#reduce-latency-with-memory‑optimized-pvc‑aware-scheduling]. Lastly, you will find [Best Practices and Configuration Considerations][#best-practice-and-configuration-considerations] to help guide how you consider both individual plugin configurations, your custom scheduler configuration, and your Deployment design holistically. From 4d7a3ccb984ee46d7340fdf0c7350a8144a5fe7a Mon Sep 17 00:00:00 2001 From: colinmixonn <109253437+colinmixonn@users.noreply.github.com> Date: Wed, 10 Dec 2025 15:46:19 -0800 Subject: [PATCH 004/164] Fix typos in AKS configuration blog post --- .../2025-12-16-aks-config-scheduler-profiles-preview/index.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md b/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md index e947798d4..d596337e6 100644 --- a/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md +++ b/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md @@ -40,7 +40,7 @@ Below you will find example configurations for some of the most common workload Adjust VM SKUs in NodeAffinity, shift utilization curves or weights, and use the right zones for your cluster(s) in the configurations below. ::: -### Increse GPU Utilization by Bin Packing GPU-backed Nodes +### Increase GPU Utilization by Bin Packing GPU-backed Nodes You can use `NodeResourceFit` to control how pods are assigned to nodes based on available resources (CPU, memory, etc.), including favoring nodes with high resource utilization, within the set configuration. For example, scheduling pending jobs on nodes with a higher relative GPU utilization, users can reduce costs and increase GPU Utilization while maintaining performance. @@ -83,7 +83,7 @@ spec: For example, spreading replicas across distinct zones safeguards availability during an AZ outage, while a softer host‑level rule prevents scheduling deadlocks when cluster capacity is uneven. -**This configuration is effective for highly‑available stateless services (web/API, gateways) or distributed messaging clusters, like Kafka brokers, that rely on the availability of multipe replicas.** +**This configuration is effective for highly‑available stateless services (web/API, gateways) or distributed messaging clusters, like Kafka brokers, that rely on the availability of multiple replicas.** ```yaml apiVersion: aks.azure.com/v1alpha1 kind: SchedulerConfiguration From 0b3b6b9bf7db61b0fa641fad050a888dfb67b58d Mon Sep 17 00:00:00 2001 From: colinmixonn <109253437+colinmixonn@users.noreply.github.com> Date: Wed, 10 Dec 2025 15:51:16 -0800 Subject: [PATCH 005/164] Revise AKS Configurable Scheduler Profiles blog post Updated blog post on AKS Configurable Scheduler Profiles to improve clarity and correctness, including sections on GPU utilization, pod distribution, and memory-optimized scheduling. --- .../index.md | 10 ++++++++++ 1 file changed, 10 insertions(+) diff --git a/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md b/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md index d596337e6..074e0f638 100644 --- a/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md +++ b/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md @@ -13,6 +13,7 @@ tags: - Scheduler --- # Avoid scheduling inefficiencies and improve GPU utilizaiton with AKS Configurable Scheduler Profiles (Preview) + Thoughtful scheduling strategies can resolve pervasive challenges across web-distributed workloads and AI workloads like resiliency and resource utilization. But the default scheduler was primarily designed for general-purpose workloads and out-of-box pod scheduling. The scheduler ultimately selects the optimal node for pod(s) based on several criteria, including (but not limited to): 1. Resource requirements (CPU, memory) @@ -27,6 +28,7 @@ To support these advanced use cases, and to give users more control, use [AKS Co In this blog you will learn how to configure the AKS Configurable Scheduler Profiles for [bin packing GPU-backed nodes][#increase-gpu-utilization-by-bin-packing-gpu-backed-nodes], [distributing pods across topologies][#increase-reselieince-by-distributing-pods-across-topology-domains], and [placing jobs on memory-optimized, pvc-ready nodes][#reduce-latency-with-memory‑optimized-pvc‑aware-scheduling]. Lastly, you will find [Best Practices and Configuration Considerations][#best-practice-and-configuration-considerations] to help guide how you consider both individual plugin configurations, your custom scheduler configuration, and your Deployment design holistically. ## AKS Configurable Scheduler Profiles + A scheduler profile is a set of one or more in-tree scheduling plugins and configurations that dictate how to schedule a pod. Previously, the scheduler configuration wasn't accessible to users. Starting from Kubernetes version 1.33, you can now configure and set a scheduler profile for the AKS scheduler on your cluster. AKS supports 18 in-tree Kubernetes scheduling plugins that allow pods to be placed on user-specified nodes, ensure pods are matched with specific storage resources, and more. The plugins can be generally grouped into the following categories: @@ -36,16 +38,19 @@ AKS supports 18 in-tree Kubernetes scheduling plugins that allow pods to be plac 3. Resource and topology optimization scheduling plugins Below you will find example configurations for some of the most common workload objectives. + :::note Adjust VM SKUs in NodeAffinity, shift utilization curves or weights, and use the right zones for your cluster(s) in the configurations below. ::: ### Increase GPU Utilization by Bin Packing GPU-backed Nodes + You can use `NodeResourceFit` to control how pods are assigned to nodes based on available resources (CPU, memory, etc.), including favoring nodes with high resource utilization, within the set configuration. For example, scheduling pending jobs on nodes with a higher relative GPU utilization, users can reduce costs and increase GPU Utilization while maintaining performance. **This scheduler configuration maximizes GPU efficiency for larger batch jobs by cobsolidating smaller jobs onto fewer nodes and lowering the operational cost of underutilized resources without sacrificing performance.** + ```yaml apiVersion: aks.azure.com/v1alpha1 kind: SchedulerConfiguration @@ -78,12 +83,15 @@ spec: - name: nvidia.com/gpu weight: 1 ``` + ### Increase reselieince by distributing pods across topology domains + `PodTopologySpread` is a scheduling strategy that seeks to distribute pods evenly across failure domains (such as availability zones or regions) to ensure high availability and fault tolerance in the event of zone or node failures. For example, spreading replicas across distinct zones safeguards availability during an AZ outage, while a softer host‑level rule prevents scheduling deadlocks when cluster capacity is uneven. **This configuration is effective for highly‑available stateless services (web/API, gateways) or distributed messaging clusters, like Kafka brokers, that rely on the availability of multiple replicas.** + ```yaml apiVersion: aks.azure.com/v1alpha1 kind: SchedulerConfiguration @@ -107,12 +115,14 @@ spec: topologyKey: kubernetes.io/hostname whenUnsatisfiable: ScheduleAnyway ``` + ### Reduce latency with memory‑optimized, PVC‑aware scheduling Use `VolumeBinding` to ensure pods are placed on nodes where _PersistentVolumeClaim's_ (PVC) can bind to _PersistentVolume's_ (PV). `VolumeZone` validates that nodes and volumes satisfy zonal requirements to avoid cross-zone storage access. For example, combine `VolumeBinding` and `VolumeZone` plugins, with `NodeAffinity` and `NodeResourcesFit` with `RequestedToCapacityRatio`, to influence pod placement on [Azure memory-optimized SKUs][https://learn.microsoft.com/en-us/azure/virtual-machines/sizes/overview?tabs=breakdownseries%2Cgeneralsizelist%2Ccomputesizelist%2Cmemorysizelist%2Cstoragesizelist%2Cgpusizelist%2Cfpgasizelist%2Chpcsizelist#memory-optimized], while ensuring PVC's bind quickly in the target zone to minimize cross‑zone traffic and latency. **This scheduler configuration ensures workloads needing large memory footprints are placed on nodes that provide sufficient RAM and maintain proximity to their volumes, enabling fast, zone‑aligned PVC binding for optimal data locality.** + ```yaml apiVersion: aks.azure.com/v1alpha1 kind: SchedulerConfiguration From 4fd375d7ab785d42bbfca919268380ed2f4e59ef Mon Sep 17 00:00:00 2001 From: colinmixonn <109253437+colinmixonn@users.noreply.github.com> Date: Wed, 10 Dec 2025 15:55:38 -0800 Subject: [PATCH 006/164] Fix typos and enhance clarity in AKS blog post Corrected typos and improved clarity in the blog post about AKS Configurable Scheduler Profiles. --- .../index.md | 11 ++++++----- 1 file changed, 6 insertions(+), 5 deletions(-) diff --git a/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md b/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md index 074e0f638..8235cb539 100644 --- a/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md +++ b/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md @@ -29,7 +29,7 @@ In this blog you will learn how to configure the AKS Configurable Scheduler Prof ## AKS Configurable Scheduler Profiles -A scheduler profile is a set of one or more in-tree scheduling plugins and configurations that dictate how to schedule a pod. Previously, the scheduler configuration wasn't accessible to users. Starting from Kubernetes version 1.33, you can now configure and set a scheduler profile for the AKS scheduler on your cluster. +A scheduler profile is a set of one or more in-tree scheduling plugins and configurations that dictate how to schedule a pod. Previously, the scheduler configuration wasn't accessible to users. Starting from Kubernetes version 1.33, you can now configure and set a scheduler profile for the AKS scheduler on your cluster. AKS supports 18 in-tree Kubernetes scheduling plugins that allow pods to be placed on user-specified nodes, ensure pods are matched with specific storage resources, and more. The plugins can be generally grouped into the following categories: @@ -45,9 +45,9 @@ Adjust VM SKUs in NodeAffinity, shift utilization curves or weights, and use the ### Increase GPU Utilization by Bin Packing GPU-backed Nodes -You can use `NodeResourceFit` to control how pods are assigned to nodes based on available resources (CPU, memory, etc.), including favoring nodes with high resource utilization, within the set configuration. +You can use `NodeResourceFit` to control how pods are assigned to nodes based on available resources (CPU, memory, etc.), including favoring nodes with high resource utilization, within the set configuration. -For example, scheduling pending jobs on nodes with a higher relative GPU utilization, users can reduce costs and increase GPU Utilization while maintaining performance. +For example, scheduling pending jobs on nodes with a higher relative GPU utilization, users can reduce costs and increase GPU Utilization while maintaining performance. **This scheduler configuration maximizes GPU efficiency for larger batch jobs by cobsolidating smaller jobs onto fewer nodes and lowering the operational cost of underutilized resources without sacrificing performance.** @@ -86,7 +86,7 @@ spec: ### Increase reselieince by distributing pods across topology domains -`PodTopologySpread` is a scheduling strategy that seeks to distribute pods evenly across failure domains (such as availability zones or regions) to ensure high availability and fault tolerance in the event of zone or node failures. +`PodTopologySpread` is a scheduling strategy that seeks to distribute pods evenly across failure domains (such as availability zones or regions) to ensure high availability and fault tolerance in the event of zone or node failures. For example, spreading replicas across distinct zones safeguards availability during an AZ outage, while a softer host‑level rule prevents scheduling deadlocks when cluster capacity is uneven. @@ -197,6 +197,7 @@ spec: ``` ## Best Practices and Configuration Considerations + As a reminder, there are many parameters the scheduler considers across the [scheduling cycle][https://kubernetes.io/docs/concepts/scheduling-eviction/scheduling-framework/#interfaces] before a pod is placed on a node that impacts how a pod is assigned. This section is meant to help guide how you consider both individual plugin configurations, your custom scheduler configuration, and your Deployment design holistically. 1. Ensure the intended deployment is assigned to the _correct_ scheduler profile. @@ -212,7 +213,7 @@ As a reminder, there are many parameters the scheduler considers across the [sch ## Next Steps: Try out AKS Configurable Scheduler -With AKS Configurable Scheduler Profiles, teams gain fine-grained control over pod placement strategies like bin-packing, topology distribution, and resource-based scoring that directly address the challenges of resielince and resource utilization for web-distributed workloads and AI workloads. By leveraging these advanced scheduling plugins, AKS users can ensure their workloads make full use of available GPU capacity, reduce idle time, and avoid costly overprovisioning. This not only improves ROI but also accelerates innovation by allowing more jobs to run concurrently and reliably. +With AKS Configurable Scheduler Profiles, teams gain fine-grained control over pod placement strategies like bin-packing, topology distribution, and resource-based scoring that directly address the challenges of resielince and resource utilization for web-distributed workloads and AI workloads. By leveraging these advanced scheduling plugins, AKS users can ensure their workloads make full use of available GPU capacity, reduce idle time, and avoid costly overprovisioning. This not only improves ROI but also accelerates innovation by allowing more jobs to run concurrently and reliably. - For best practices using the kube-scheduler visit [kube-scheduler best practices][https://learn.microsoft.com/en-us/azure/aks/operator-best-practices-advanced-scheduler] - Configure your workload specific scheduler using the [AKS Configurable Scheduler][https://learn.microsoft.com/en-us/azure/aks/concepts-scheduler-configuration] From 5d34e5036e36a3d3bee307b25a1cf19735b033c8 Mon Sep 17 00:00:00 2001 From: colinmixonn <109253437+colinmixonn@users.noreply.github.com> Date: Wed, 10 Dec 2025 16:01:21 -0800 Subject: [PATCH 007/164] Fix links and typos in AKS Configurable Scheduler blog --- .../index.md | 17 +++++++++++------ 1 file changed, 11 insertions(+), 6 deletions(-) diff --git a/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md b/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md index 8235cb539..6099ccfb2 100644 --- a/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md +++ b/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md @@ -23,9 +23,9 @@ Thoughtful scheduling strategies can resolve pervasive challenges across web-dis The criteria, and their respective priority in the scheduling cycle, are not suitable for advanced use cases that might require custom scheduling strategies. Nor, does the default scheduler enable user customization for fine-grain pod placement control while avoiding managing a second custom scheduler. For example, users running batch jobs might prefer collocating on a few nodes for better performance or cost-sensitive workloads might benefit from node binpacking to minimize idle node costs. -To support these advanced use cases, and to give users more control, use [AKS Configurable Scheduler Profiles][https://learn.microsoft.com/en-us/azure/aks/concepts-scheduler-configuration] to tailor a scheduler to their specific workload requirements using node bin-packing, preemption, and 16 other scheduling plugins that can optimize ROI​, improve gpu utilization, improve data locality, or increase resliency. +To support these advanced use cases, and to give users more control, use [AKS Configurable Scheduler Profiles][concepts-scheduler-configuration] to tailor a scheduler to their specific workload requirements using node bin-packing, preemption, and 16 other scheduling plugins that can optimize ROI​, improve gpu utilization, improve data locality, or increase resliency. -In this blog you will learn how to configure the AKS Configurable Scheduler Profiles for [bin packing GPU-backed nodes][#increase-gpu-utilization-by-bin-packing-gpu-backed-nodes], [distributing pods across topologies][#increase-reselieince-by-distributing-pods-across-topology-domains], and [placing jobs on memory-optimized, pvc-ready nodes][#reduce-latency-with-memory‑optimized-pvc‑aware-scheduling]. Lastly, you will find [Best Practices and Configuration Considerations][#best-practice-and-configuration-considerations] to help guide how you consider both individual plugin configurations, your custom scheduler configuration, and your Deployment design holistically. +In this blog you will learn how to configure the AKS Configurable Scheduler Profiles for [bin packing GPU-backed nodes](#increase-gpu-utilization-by-bin-packing-gpu-backed-nodes), [distributing pods across topologies](#increase-reselieince-by-distributing-pods-across-topology-domains), and [placing jobs on memory-optimized, pvc-ready nodes](#reduce-latency-with-memory‑optimized-pvc‑aware-scheduling). Lastly, you will find [best practices](#best-practice-and-configuration-considerations) to help guide how you consider both individual plugin configurations, your custom scheduler configuration, and your Deployment design holistically. ## AKS Configurable Scheduler Profiles @@ -198,7 +198,7 @@ spec: ## Best Practices and Configuration Considerations -As a reminder, there are many parameters the scheduler considers across the [scheduling cycle][https://kubernetes.io/docs/concepts/scheduling-eviction/scheduling-framework/#interfaces] before a pod is placed on a node that impacts how a pod is assigned. This section is meant to help guide how you consider both individual plugin configurations, your custom scheduler configuration, and your Deployment design holistically. +As a reminder, there are many parameters the scheduler considers across the [scheduling cycle][scheduling-framework/#interfaces] before a pod is placed on a node that impacts how a pod is assigned. This section is meant to help guide how you consider both individual plugin configurations, your custom scheduler configuration, and your Deployment design holistically. 1. Ensure the intended deployment is assigned to the _correct_ scheduler profile. 2. Ensure the customer scheduler profile complements the implementation of Deployments, StorageClasses, and PersistentVolumeClaim's. Misalignment can lead to pending pods and degraded workload performance, even when the scheduler is functioning as expected. @@ -215,6 +215,11 @@ As a reminder, there are many parameters the scheduler considers across the [sch With AKS Configurable Scheduler Profiles, teams gain fine-grained control over pod placement strategies like bin-packing, topology distribution, and resource-based scoring that directly address the challenges of resielince and resource utilization for web-distributed workloads and AI workloads. By leveraging these advanced scheduling plugins, AKS users can ensure their workloads make full use of available GPU capacity, reduce idle time, and avoid costly overprovisioning. This not only improves ROI but also accelerates innovation by allowing more jobs to run concurrently and reliably. -- For best practices using the kube-scheduler visit [kube-scheduler best practices][https://learn.microsoft.com/en-us/azure/aks/operator-best-practices-advanced-scheduler] -- Configure your workload specific scheduler using the [AKS Configurable Scheduler][https://learn.microsoft.com/en-us/azure/aks/concepts-scheduler-configuration] -- If additional capabilities or ML frameworks are needed to schedule and queue batch workloads, you can [install and configure Kueue on AKS][https://learn.microsoft.com/en-us/azure/aks/kueue-overview] to ensure efficient, policy-driven scheduling in AKS clusters. +- For best practices using the kube-scheduler visit [kube-scheduler best practices][best-practices-advanced-scheduler] +- Configure your workload specific scheduler using the [AKS Configurable Scheduler][concepts-scheduler-configuration] +- If additional capabilities or ML frameworks are needed to schedule and queue batch workloads, you can [install and configure Kueue on AKS][kueue-overview] to ensure efficient, policy-driven scheduling in AKS clusters. + +[concepts-scheduler-configuration]: https://learn.microsoft.com/en-us/azure/aks/concepts-scheduler-configuration +[kueue-overview]: https://learn.microsoft.com/en-us/azure/aks/kueue-overview +[best-practices-advanced-scheduler]:https://learn.microsoft.com/en-us/azure/aks/operator-best-practices-advanced-scheduler +[scheduling-framework/#interfaces]: https://kubernetes.io/docs/concepts/scheduling-eviction/scheduling-framework/#interfaces From 0760340b0ffdf6e8c52f4ea937b5504562d4a4cb Mon Sep 17 00:00:00 2001 From: colinmixonn <109253437+colinmixonn@users.noreply.github.com> Date: Wed, 10 Dec 2025 16:28:59 -0800 Subject: [PATCH 008/164] Clarify objectives and improve section titles in blog Updated the blog to clarify the objectives of configuring AKS Configurable Scheduler Profiles, improved section titles, and ensured consistency in terminology. --- .../index.md | 13 ++++++++++--- 1 file changed, 10 insertions(+), 3 deletions(-) diff --git a/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md b/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md index 6099ccfb2..f381a189b 100644 --- a/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md +++ b/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md @@ -25,7 +25,12 @@ The criteria, and their respective priority in the scheduling cycle, are not sui To support these advanced use cases, and to give users more control, use [AKS Configurable Scheduler Profiles][concepts-scheduler-configuration] to tailor a scheduler to their specific workload requirements using node bin-packing, preemption, and 16 other scheduling plugins that can optimize ROI​, improve gpu utilization, improve data locality, or increase resliency. -In this blog you will learn how to configure the AKS Configurable Scheduler Profiles for [bin packing GPU-backed nodes](#increase-gpu-utilization-by-bin-packing-gpu-backed-nodes), [distributing pods across topologies](#increase-reselieince-by-distributing-pods-across-topology-domains), and [placing jobs on memory-optimized, pvc-ready nodes](#reduce-latency-with-memory‑optimized-pvc‑aware-scheduling). Lastly, you will find [best practices](#best-practice-and-configuration-considerations) to help guide how you consider both individual plugin configurations, your custom scheduler configuration, and your Deployment design holistically. +In this blog you will learn how to configure the AKS Configurable Scheduler Profiles for three workloads objectives: +1. [Bin packing GPU-backed nodes](#increase-gpu-utilization-by-bin-packing-gpu-backed-nodes) +2. [Distributing pods across topologies](#increase-reselieince-by-distributing-pods-across-topology-domains) +3. [Placing jobs on memory-optimized, pvc-ready nodes](#optimize-data-locality-with-memory-and-pvc-aware-scheduling) + +Lastly, you will find [best practices](#best-practices-and-configuration-considerations) to help guide how you consider both individual plugin configurations, your custom scheduler configuration, and your Deployment design holistically. ## AKS Configurable Scheduler Profiles @@ -116,10 +121,11 @@ spec: whenUnsatisfiable: ScheduleAnyway ``` -### Reduce latency with memory‑optimized, PVC‑aware scheduling +### Optimize data locality with Memory and PVC-Aware Scheduling + Use `VolumeBinding` to ensure pods are placed on nodes where _PersistentVolumeClaim's_ (PVC) can bind to _PersistentVolume's_ (PV). `VolumeZone` validates that nodes and volumes satisfy zonal requirements to avoid cross-zone storage access. -For example, combine `VolumeBinding` and `VolumeZone` plugins, with `NodeAffinity` and `NodeResourcesFit` with `RequestedToCapacityRatio`, to influence pod placement on [Azure memory-optimized SKUs][https://learn.microsoft.com/en-us/azure/virtual-machines/sizes/overview?tabs=breakdownseries%2Cgeneralsizelist%2Ccomputesizelist%2Cmemorysizelist%2Cstoragesizelist%2Cgpusizelist%2Cfpgasizelist%2Chpcsizelist#memory-optimized], while ensuring PVC's bind quickly in the target zone to minimize cross‑zone traffic and latency. +For example, combine `VolumeBinding` and `VolumeZone` plugins, with `NodeAffinity` and `NodeResourcesFit` with `RequestedToCapacityRatio`, to influence pod placement on [Azure memory-optimized SKUs][memory-optimized-vm], while ensuring PVC's bind quickly in the target zone to minimize cross‑zone traffic and latency. **This scheduler configuration ensures workloads needing large memory footprints are placed on nodes that provide sufficient RAM and maintain proximity to their volumes, enabling fast, zone‑aligned PVC binding for optimal data locality.** @@ -223,3 +229,4 @@ With AKS Configurable Scheduler Profiles, teams gain fine-grained control over p [kueue-overview]: https://learn.microsoft.com/en-us/azure/aks/kueue-overview [best-practices-advanced-scheduler]:https://learn.microsoft.com/en-us/azure/aks/operator-best-practices-advanced-scheduler [scheduling-framework/#interfaces]: https://kubernetes.io/docs/concepts/scheduling-eviction/scheduling-framework/#interfaces +[memory-optimized-vm]: https://learn.microsoft.com/en-us/azure/virtual-machines/sizes/overview?tabs=breakdownseries%2Cgeneralsizelist%2Ccomputesizelist%2Cmemorysizelist%2Cstoragesizelist%2Cgpusizelist%2Cfpgasizelist%2Chpcsizelist#memory-optimized From 92ff663c129d082f7a423a26d45d3005548276d2 Mon Sep 17 00:00:00 2001 From: colinmixonn <109253437+colinmixonn@users.noreply.github.com> Date: Wed, 10 Dec 2025 16:38:34 -0800 Subject: [PATCH 009/164] Enhance clarity in AKS Configurable Scheduler blog Clarified the objectives and improved the wording in the blog post about AKS Configurable Scheduler Profiles. --- .../2025-12-16-aks-config-scheduler-profiles-preview/index.md | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md b/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md index f381a189b..fe67422c8 100644 --- a/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md +++ b/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md @@ -26,6 +26,7 @@ The criteria, and their respective priority in the scheduling cycle, are not sui To support these advanced use cases, and to give users more control, use [AKS Configurable Scheduler Profiles][concepts-scheduler-configuration] to tailor a scheduler to their specific workload requirements using node bin-packing, preemption, and 16 other scheduling plugins that can optimize ROI​, improve gpu utilization, improve data locality, or increase resliency. In this blog you will learn how to configure the AKS Configurable Scheduler Profiles for three workloads objectives: + 1. [Bin packing GPU-backed nodes](#increase-gpu-utilization-by-bin-packing-gpu-backed-nodes) 2. [Distributing pods across topologies](#increase-reselieince-by-distributing-pods-across-topology-domains) 3. [Placing jobs on memory-optimized, pvc-ready nodes](#optimize-data-locality-with-memory-and-pvc-aware-scheduling) @@ -42,7 +43,7 @@ AKS supports 18 in-tree Kubernetes scheduling plugins that allow pods to be plac 2. Node selection constraints scheduling plugins 3. Resource and topology optimization scheduling plugins -Below you will find example configurations for some of the most common workload objectives. +Below you will find example configurations for some of the most common workload objectives. :::note Adjust VM SKUs in NodeAffinity, shift utilization curves or weights, and use the right zones for your cluster(s) in the configurations below. From ef5c000979a47f8a6419a874264370598acd1846 Mon Sep 17 00:00:00 2001 From: colinmixonn <109253437+colinmixonn@users.noreply.github.com> Date: Thu, 11 Dec 2025 10:00:34 -0800 Subject: [PATCH 010/164] Update index.md --- .../index.md | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md b/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md index fe67422c8..a7f523b70 100644 --- a/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md +++ b/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md @@ -25,11 +25,11 @@ The criteria, and their respective priority in the scheduling cycle, are not sui To support these advanced use cases, and to give users more control, use [AKS Configurable Scheduler Profiles][concepts-scheduler-configuration] to tailor a scheduler to their specific workload requirements using node bin-packing, preemption, and 16 other scheduling plugins that can optimize ROI​, improve gpu utilization, improve data locality, or increase resliency. -In this blog you will learn how to configure the AKS Configurable Scheduler Profiles for three workloads objectives: +In this blog you will learn how to configure the AKS Configurable Scheduler Profiles for three workload objectives: -1. [Bin packing GPU-backed nodes](#increase-gpu-utilization-by-bin-packing-gpu-backed-nodes) -2. [Distributing pods across topologies](#increase-reselieince-by-distributing-pods-across-topology-domains) -3. [Placing jobs on memory-optimized, pvc-ready nodes](#optimize-data-locality-with-memory-and-pvc-aware-scheduling) +1. [Increase GPU utilization by bin packing GPU-backed nodes](#increase-gpu-utilization-by-bin-packing-gpu-backed-nodes) +2. [Increase reselieince by distributing pods across topology domains](#increase-reselieince-by-distributing-pods-across-topology-domains) +3. [Optimize data locality with memory and PVC-aware scheduling](#optimize-data-locality-with-memory-and-pvc-aware-scheduling) Lastly, you will find [best practices](#best-practices-and-configuration-considerations) to help guide how you consider both individual plugin configurations, your custom scheduler configuration, and your Deployment design holistically. From a488c12f1db0db28c851bf362f15c63e0f7d0458 Mon Sep 17 00:00:00 2001 From: colinmixonn <109253437+colinmixonn@users.noreply.github.com> Date: Thu, 11 Dec 2025 11:14:51 -0800 Subject: [PATCH 011/164] Update website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md Co-authored-by: Diego Casati --- .../2025-12-16-aks-config-scheduler-profiles-preview/index.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md b/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md index a7f523b70..301430a79 100644 --- a/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md +++ b/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md @@ -46,7 +46,7 @@ AKS supports 18 in-tree Kubernetes scheduling plugins that allow pods to be plac Below you will find example configurations for some of the most common workload objectives. :::note -Adjust VM SKUs in NodeAffinity, shift utilization curves or weights, and use the right zones for your cluster(s) in the configurations below. +Adjust VM SKUs in `NodeAffinity`, shift utilization curves or weights, and use the right zones for your cluster(s) in the configurations below. ::: ### Increase GPU Utilization by Bin Packing GPU-backed Nodes From 3999b23db4fcc1ba05334154e137096e9fb8a0d0 Mon Sep 17 00:00:00 2001 From: colinmixonn <109253437+colinmixonn@users.noreply.github.com> Date: Thu, 11 Dec 2025 11:38:46 -0800 Subject: [PATCH 012/164] Update website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> --- .../2025-12-16-aks-config-scheduler-profiles-preview/index.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md b/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md index 301430a79..66955895c 100644 --- a/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md +++ b/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md @@ -209,7 +209,7 @@ As a reminder, there are many parameters the scheduler considers across the [sch 1. Ensure the intended deployment is assigned to the _correct_ scheduler profile. 2. Ensure the customer scheduler profile complements the implementation of Deployments, StorageClasses, and PersistentVolumeClaim's. Misalignment can lead to pending pods and degraded workload performance, even when the scheduler is functioning as expected. -3. Ensure their are enough nodes in each zone to accommodate your deployment replicas and ensure your AKS node pool spans the right availability zones. If not, pods may remain in a pending state. +3. Ensure there are enough nodes in each zone to accommodate your deployment replicas and ensure your AKS node pool spans the right availability zones. If not, pods may remain in a pending state. 4. Use namespaces to separate workloads which improves your ability to validate or troubleshoot. 5. Assign `priorityClassName` for workloads that should preempt others, this is critical if you use the DefaultPreemption plugin. 6. If you use the `ImageLocality` plugin, use DaemonSets or node pre-pulling for latency-sensitive images, otherwise the benefit may be minimal. From 37ced5b2a4e20e71ee76a55983fe44ad40833b48 Mon Sep 17 00:00:00 2001 From: colinmixonn <109253437+colinmixonn@users.noreply.github.com> Date: Thu, 11 Dec 2025 11:38:56 -0800 Subject: [PATCH 013/164] Update website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> --- .../2025-12-16-aks-config-scheduler-profiles-preview/index.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md b/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md index 66955895c..b4b09ae46 100644 --- a/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md +++ b/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md @@ -1,5 +1,5 @@ --- -title: "Announcing Azure Configurable Scheduler Profiles: Optimize resource ROI with fine grained scheduling strategies." +title: "Announcing Azure Configurable Scheduler Profiles: Optimize resource ROI with fine grained scheduling strategies" description: "Avoid scheduling inefficiencies and improve GPU utilization with AKS Configurable Scheduler Profiles" date: 2025-12-16 authors: From ef30f6eafee937fe5a684c06a516d02ce026a296 Mon Sep 17 00:00:00 2001 From: colinmixonn <109253437+colinmixonn@users.noreply.github.com> Date: Thu, 11 Dec 2025 11:39:31 -0800 Subject: [PATCH 014/164] Update website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> --- .../2025-12-16-aks-config-scheduler-profiles-preview/index.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md b/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md index b4b09ae46..a89131af4 100644 --- a/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md +++ b/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md @@ -12,7 +12,7 @@ tags: - performance - Scheduler --- -# Avoid scheduling inefficiencies and improve GPU utilizaiton with AKS Configurable Scheduler Profiles (Preview) +# Avoid scheduling inefficiencies and improve GPU utilization with AKS Configurable Scheduler Profiles (Preview) Thoughtful scheduling strategies can resolve pervasive challenges across web-distributed workloads and AI workloads like resiliency and resource utilization. But the default scheduler was primarily designed for general-purpose workloads and out-of-box pod scheduling. The scheduler ultimately selects the optimal node for pod(s) based on several criteria, including (but not limited to): From 4dd4ee0b569be9fb5c02ef9841619b47a3c05065 Mon Sep 17 00:00:00 2001 From: colinmixonn <109253437+colinmixonn@users.noreply.github.com> Date: Thu, 11 Dec 2025 12:01:48 -0800 Subject: [PATCH 015/164] Update website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> --- .../2025-12-16-aks-config-scheduler-profiles-preview/index.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md b/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md index a89131af4..a04b84f1f 100644 --- a/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md +++ b/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md @@ -23,7 +23,7 @@ Thoughtful scheduling strategies can resolve pervasive challenges across web-dis The criteria, and their respective priority in the scheduling cycle, are not suitable for advanced use cases that might require custom scheduling strategies. Nor, does the default scheduler enable user customization for fine-grain pod placement control while avoiding managing a second custom scheduler. For example, users running batch jobs might prefer collocating on a few nodes for better performance or cost-sensitive workloads might benefit from node binpacking to minimize idle node costs. -To support these advanced use cases, and to give users more control, use [AKS Configurable Scheduler Profiles][concepts-scheduler-configuration] to tailor a scheduler to their specific workload requirements using node bin-packing, preemption, and 16 other scheduling plugins that can optimize ROI​, improve gpu utilization, improve data locality, or increase resliency. +To support these advanced use cases, and to give users more control, use [AKS Configurable Scheduler Profiles][concepts-scheduler-configuration] to tailor a scheduler to their specific workload requirements using node bin-packing, preemption, and 16 other scheduling plugins that can optimize ROI​, improve gpu utilization, improve data locality, or increase resiliency. In this blog you will learn how to configure the AKS Configurable Scheduler Profiles for three workload objectives: From 0cf3a813ae0574f29787d123d1ff0e8f7602d798 Mon Sep 17 00:00:00 2001 From: colinmixonn <109253437+colinmixonn@users.noreply.github.com> Date: Thu, 11 Dec 2025 12:02:01 -0800 Subject: [PATCH 016/164] Update website/blog/tags.yml Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> --- website/blog/tags.yml | 1 - 1 file changed, 1 deletion(-) diff --git a/website/blog/tags.yml b/website/blog/tags.yml index bf7a4cbc3..0bf18cb2a 100644 --- a/website/blog/tags.yml +++ b/website/blog/tags.yml @@ -276,7 +276,6 @@ scheduler: label: Scheduler permalink: /scheduler description: Techniques and capabilities to control pod placement, improve resource efficiency, and tune scheduling for diverse AKS workloads. - security: label: Security permalink: /security From 4aa8cb9c5e03c39c1c1153a851368b56b8d1d9ab Mon Sep 17 00:00:00 2001 From: colinmixonn <109253437+colinmixonn@users.noreply.github.com> Date: Thu, 11 Dec 2025 12:03:27 -0800 Subject: [PATCH 017/164] Refactor author and tags in blog post metadata Updated author format and adjusted tag casing in the blog post. --- .../index.md | 9 +++------ 1 file changed, 3 insertions(+), 6 deletions(-) diff --git a/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md b/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md index a04b84f1f..0bc18b3d9 100644 --- a/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md +++ b/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md @@ -3,14 +3,11 @@ title: "Announcing Azure Configurable Scheduler Profiles: Optimize resource ROI description: "Avoid scheduling inefficiencies and improve GPU utilization with AKS Configurable Scheduler Profiles" date: 2025-12-16 authors: - - Colin Mixon -categories: -- operations -- ai + - colin-mixon tags: - - AI + - ai - performance - - Scheduler + - scheduler --- # Avoid scheduling inefficiencies and improve GPU utilization with AKS Configurable Scheduler Profiles (Preview) From 0b458e91b08a125f5c1eb985351cb3ec10292cad Mon Sep 17 00:00:00 2001 From: colinmixonn <109253437+colinmixonn@users.noreply.github.com> Date: Thu, 11 Dec 2025 13:55:39 -0800 Subject: [PATCH 018/164] Update website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> --- .../2025-12-16-aks-config-scheduler-profiles-preview/index.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md b/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md index 0bc18b3d9..8f312503c 100644 --- a/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md +++ b/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md @@ -52,7 +52,7 @@ You can use `NodeResourceFit` to control how pods are assigned to nodes based on For example, scheduling pending jobs on nodes with a higher relative GPU utilization, users can reduce costs and increase GPU Utilization while maintaining performance. -**This scheduler configuration maximizes GPU efficiency for larger batch jobs by cobsolidating smaller jobs onto fewer nodes and lowering the operational cost of underutilized resources without sacrificing performance.** +**This scheduler configuration maximizes GPU efficiency for larger batch jobs by consolidating smaller jobs onto fewer nodes and lowering the operational cost of underutilized resources without sacrificing performance.** ```yaml apiVersion: aks.azure.com/v1alpha1 From 2bfef0ab1d50e159973fbf61db30656b8d5d9e65 Mon Sep 17 00:00:00 2001 From: colinmixonn <109253437+colinmixonn@users.noreply.github.com> Date: Thu, 11 Dec 2025 13:58:39 -0800 Subject: [PATCH 019/164] Fix typo in blog title for AKS Scheduler Profiles --- .../2025-12-16-aks-config-scheduler-profiles-preview/index.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md b/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md index 8f312503c..bd5d29db8 100644 --- a/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md +++ b/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md @@ -1,5 +1,5 @@ --- -title: "Announcing Azure Configurable Scheduler Profiles: Optimize resource ROI with fine grained scheduling strategies" +title: "Announcing Azure Configurable Scheduler Profiles (Preivew): Optimize resource ROI with fine grained scheduling strategies" description: "Avoid scheduling inefficiencies and improve GPU utilization with AKS Configurable Scheduler Profiles" date: 2025-12-16 authors: @@ -8,8 +8,8 @@ tags: - ai - performance - scheduler + - best-practices --- -# Avoid scheduling inefficiencies and improve GPU utilization with AKS Configurable Scheduler Profiles (Preview) Thoughtful scheduling strategies can resolve pervasive challenges across web-distributed workloads and AI workloads like resiliency and resource utilization. But the default scheduler was primarily designed for general-purpose workloads and out-of-box pod scheduling. The scheduler ultimately selects the optimal node for pod(s) based on several criteria, including (but not limited to): From 581a58aff10ee408f68ba944a8ad70903fc56f20 Mon Sep 17 00:00:00 2001 From: colinmixonn <109253437+colinmixonn@users.noreply.github.com> Date: Thu, 11 Dec 2025 13:59:20 -0800 Subject: [PATCH 020/164] Update website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> --- .../2025-12-16-aks-config-scheduler-profiles-preview/index.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md b/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md index bd5d29db8..7b0466d5f 100644 --- a/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md +++ b/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md @@ -217,7 +217,7 @@ As a reminder, there are many parameters the scheduler considers across the [sch ## Next Steps: Try out AKS Configurable Scheduler -With AKS Configurable Scheduler Profiles, teams gain fine-grained control over pod placement strategies like bin-packing, topology distribution, and resource-based scoring that directly address the challenges of resielince and resource utilization for web-distributed workloads and AI workloads. By leveraging these advanced scheduling plugins, AKS users can ensure their workloads make full use of available GPU capacity, reduce idle time, and avoid costly overprovisioning. This not only improves ROI but also accelerates innovation by allowing more jobs to run concurrently and reliably. +With AKS Configurable Scheduler Profiles, teams gain fine-grained control over pod placement strategies like bin-packing, topology distribution, and resource-based scoring that directly address the challenges of resilience and resource utilization for web-distributed workloads and AI workloads. By leveraging these advanced scheduling plugins, AKS users can ensure their workloads make full use of available GPU capacity, reduce idle time, and avoid costly overprovisioning. This not only improves ROI but also accelerates innovation by allowing more jobs to run concurrently and reliably. - For best practices using the kube-scheduler visit [kube-scheduler best practices][best-practices-advanced-scheduler] - Configure your workload specific scheduler using the [AKS Configurable Scheduler][concepts-scheduler-configuration] From 92f516b2b5158b1a6a6d0d8bd59e457cb6112527 Mon Sep 17 00:00:00 2001 From: colinmixonn <109253437+colinmixonn@users.noreply.github.com> Date: Thu, 11 Dec 2025 14:00:39 -0800 Subject: [PATCH 021/164] Fix typo in blog title for AKS Scheduler Profiles --- .../2025-12-16-aks-config-scheduler-profiles-preview/index.md | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md b/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md index 7b0466d5f..d539226d8 100644 --- a/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md +++ b/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md @@ -1,5 +1,5 @@ --- -title: "Announcing Azure Configurable Scheduler Profiles (Preivew): Optimize resource ROI with fine grained scheduling strategies" +title: "Announcing Azure Configurable Scheduler Profiles (Preview): Optimize resource ROI with fine grained scheduling strategies" description: "Avoid scheduling inefficiencies and improve GPU utilization with AKS Configurable Scheduler Profiles" date: 2025-12-16 authors: @@ -30,6 +30,8 @@ In this blog you will learn how to configure the AKS Configurable Scheduler Prof Lastly, you will find [best practices](#best-practices-and-configuration-considerations) to help guide how you consider both individual plugin configurations, your custom scheduler configuration, and your Deployment design holistically. + + ## AKS Configurable Scheduler Profiles A scheduler profile is a set of one or more in-tree scheduling plugins and configurations that dictate how to schedule a pod. Previously, the scheduler configuration wasn't accessible to users. Starting from Kubernetes version 1.33, you can now configure and set a scheduler profile for the AKS scheduler on your cluster. From 2330ca1b2282fb91ed80e620eaa3ff362aaefac5 Mon Sep 17 00:00:00 2001 From: colinmixonn <109253437+colinmixonn@users.noreply.github.com> Date: Thu, 11 Dec 2025 17:07:40 -0800 Subject: [PATCH 022/164] Apply suggestions from code review Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> --- .../2025-12-16-aks-config-scheduler-profiles-preview/index.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md b/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md index d539226d8..47a28c236 100644 --- a/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md +++ b/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md @@ -25,7 +25,7 @@ To support these advanced use cases, and to give users more control, use [AKS Co In this blog you will learn how to configure the AKS Configurable Scheduler Profiles for three workload objectives: 1. [Increase GPU utilization by bin packing GPU-backed nodes](#increase-gpu-utilization-by-bin-packing-gpu-backed-nodes) -2. [Increase reselieince by distributing pods across topology domains](#increase-reselieince-by-distributing-pods-across-topology-domains) +2. [Increase resilience by distributing pods across topology domains](#increase-resilience-by-distributing-pods-across-topology-domains) 3. [Optimize data locality with memory and PVC-aware scheduling](#optimize-data-locality-with-memory-and-pvc-aware-scheduling) Lastly, you will find [best practices](#best-practices-and-configuration-considerations) to help guide how you consider both individual plugin configurations, your custom scheduler configuration, and your Deployment design holistically. @@ -89,7 +89,7 @@ spec: weight: 1 ``` -### Increase reselieince by distributing pods across topology domains +### Increase resilience by distributing pods across topology domains `PodTopologySpread` is a scheduling strategy that seeks to distribute pods evenly across failure domains (such as availability zones or regions) to ensure high availability and fault tolerance in the event of zone or node failures. From 14b044a9a84f8a54479c695b0e2dc098338c2056 Mon Sep 17 00:00:00 2001 From: colinmixonn <109253437+colinmixonn@users.noreply.github.com> Date: Thu, 11 Dec 2025 17:08:28 -0800 Subject: [PATCH 023/164] Apply suggestions from code review Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> --- .../index.md | 43 ++++++++++--------- 1 file changed, 22 insertions(+), 21 deletions(-) diff --git a/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md b/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md index 47a28c236..3e45dcd30 100644 --- a/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md +++ b/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md @@ -65,28 +65,29 @@ spec: rawConfig: | apiVersion: kubescheduler.config.k8s.io/v1 kind: KubeSchedulerConfiguration - - schedulerName: gpu-node-binpacking-scheduler - plugins: - multiPoint: - enabled: - - name: ImageLocality - - name: NodeResourceFit + profiles: + - schedulerName: gpu-node-binpacking-scheduler + plugins: + multiPoint: + enabled: + - name: ImageLocality + - name: NodeResourceFit + - name: NodeResourcesBalancedAllocation + pluginConfig: + - name: NodeResourcesFit + args: + scoringStrategy: + type: MostAllocated + resources: + - name: cpu + weight: 1 + - name: nvidia.com/gpu + weight: 3 - name: NodeResourcesBalancedAllocation - pluginConfig: - - name: NodeResourcesFit - args: - scoringStrategy: - type: MostAllocated - resources: - - name: cpu - weight: 1 - - name: nvidia.com/gpu - weight: 3 - - name: NodeResourcesBalancedAllocation - args: - resources: - - name: nvidia.com/gpu - weight: 1 + args: + resources: + - name: nvidia.com/gpu + weight: 1 ``` ### Increase resilience by distributing pods across topology domains From 434d7f79cad97f52badcbb7e9974665f07561249 Mon Sep 17 00:00:00 2001 From: colinmixonn <109253437+colinmixonn@users.noreply.github.com> Date: Thu, 11 Dec 2025 17:16:22 -0800 Subject: [PATCH 024/164] Apply suggestions from code review Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> --- .../2025-12-16-aks-config-scheduler-profiles-preview/index.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md b/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md index 3e45dcd30..808367f17 100644 --- a/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md +++ b/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md @@ -228,6 +228,6 @@ With AKS Configurable Scheduler Profiles, teams gain fine-grained control over p [concepts-scheduler-configuration]: https://learn.microsoft.com/en-us/azure/aks/concepts-scheduler-configuration [kueue-overview]: https://learn.microsoft.com/en-us/azure/aks/kueue-overview -[best-practices-advanced-scheduler]:https://learn.microsoft.com/en-us/azure/aks/operator-best-practices-advanced-scheduler +[best-practices-advanced-scheduler]: https://learn.microsoft.com/en-us/azure/aks/operator-best-practices-advanced-scheduler [scheduling-framework/#interfaces]: https://kubernetes.io/docs/concepts/scheduling-eviction/scheduling-framework/#interfaces [memory-optimized-vm]: https://learn.microsoft.com/en-us/azure/virtual-machines/sizes/overview?tabs=breakdownseries%2Cgeneralsizelist%2Ccomputesizelist%2Cmemorysizelist%2Cstoragesizelist%2Cgpusizelist%2Cfpgasizelist%2Chpcsizelist#memory-optimized From 7b8d9f807fe9cf9b47e86097a09dfac2106fa0a6 Mon Sep 17 00:00:00 2001 From: colinmixonn <109253437+colinmixonn@users.noreply.github.com> Date: Thu, 11 Dec 2025 19:59:40 -0800 Subject: [PATCH 025/164] Update links in AKS Config Scheduler blog post --- .../index.md | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md b/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md index 808367f17..1bef2fc37 100644 --- a/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md +++ b/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md @@ -226,8 +226,8 @@ With AKS Configurable Scheduler Profiles, teams gain fine-grained control over p - Configure your workload specific scheduler using the [AKS Configurable Scheduler][concepts-scheduler-configuration] - If additional capabilities or ML frameworks are needed to schedule and queue batch workloads, you can [install and configure Kueue on AKS][kueue-overview] to ensure efficient, policy-driven scheduling in AKS clusters. -[concepts-scheduler-configuration]: https://learn.microsoft.com/en-us/azure/aks/concepts-scheduler-configuration -[kueue-overview]: https://learn.microsoft.com/en-us/azure/aks/kueue-overview -[best-practices-advanced-scheduler]: https://learn.microsoft.com/en-us/azure/aks/operator-best-practices-advanced-scheduler +[concepts-scheduler-configuration]: https://learn.microsoft.com/azure/aks/concepts-scheduler-configuration +[kueue-overview]: https://learn.microsoft.com/azure/aks/kueue-overview +[best-practices-advanced-scheduler]: https://learn.microsoft.com/azure/aks/operator-best-practices-advanced-scheduler [scheduling-framework/#interfaces]: https://kubernetes.io/docs/concepts/scheduling-eviction/scheduling-framework/#interfaces -[memory-optimized-vm]: https://learn.microsoft.com/en-us/azure/virtual-machines/sizes/overview?tabs=breakdownseries%2Cgeneralsizelist%2Ccomputesizelist%2Cmemorysizelist%2Cstoragesizelist%2Cgpusizelist%2Cfpgasizelist%2Chpcsizelist#memory-optimized +[memory-optimized-vm]: https://learn.microsoft.com/azure/virtual-machines/sizes/overview?tabs=breakdownseries%2Cgeneralsizelist%2Ccomputesizelist%2Cmemorysizelist%2Cstoragesizelist%2Cgpusizelist%2Cfpgasizelist%2Chpcsizelist#memory-optimized From 7b813e2196b8e886a8209fdf0c1b17fd427b2e23 Mon Sep 17 00:00:00 2001 From: colinmixonn <109253437+colinmixonn@users.noreply.github.com> Date: Thu, 11 Dec 2025 20:00:18 -0800 Subject: [PATCH 026/164] Apply suggestions from code review Co-authored-by: Diego Casati --- .../2025-12-16-aks-config-scheduler-profiles-preview/index.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md b/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md index 1bef2fc37..56998f7b9 100644 --- a/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md +++ b/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md @@ -214,9 +214,9 @@ As a reminder, there are many parameters the scheduler considers across the [sch 5. Assign `priorityClassName` for workloads that should preempt others, this is critical if you use the DefaultPreemption plugin. 6. If you use the `ImageLocality` plugin, use DaemonSets or node pre-pulling for latency-sensitive images, otherwise the benefit may be minimal. 7. If your cluster is large, a low `PercentageOfNodesToScore` speeds scheduling by reducing the number of nodes scored, _but_ it may reduce optimal placement. -8. If you enable a plugin in the `plugins:multipoint` section but do not define it in `pluginConfig:`, AKS uses the default configuration for that plugin. +8. If you enable a plugin in the `plugins:multipoint` section but do not define it in `pluginConfig`, AKS uses the default configuration for that plugin. 9. For `NodeResourcesFit`, the ratio matters more than absolute values. So CPU:Memory:Storage = 3:1:2, which means CPU is 3× more influential than memory, and storage is 2x more influential than memory in the scoring phase. -10. Pair `PodTopologySpread` with pod disruption budget's (PDB) and multi‑replica strategies for HA during upgrades. +10. Pair `PodTopologySpread` with Pod Disruption Budget's (PDB) and multi‑replica strategies for HA during upgrades. ## Next Steps: Try out AKS Configurable Scheduler From c8182032289acac848e70f3dc87f671efd928536 Mon Sep 17 00:00:00 2001 From: colinmixonn <109253437+colinmixonn@users.noreply.github.com> Date: Fri, 12 Dec 2025 11:18:34 -0800 Subject: [PATCH 027/164] Revise blog on AKS Configurable Scheduler Profiles Updated the blog post to clarify the advantages of AKS Configurable Scheduler Profiles over the default kube-scheduler, emphasizing flexibility and control for advanced use cases. Enhanced explanations of scheduling strategies and their impact on resource utilization and performance. --- .../index.md | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md b/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md index 56998f7b9..159d209cf 100644 --- a/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md +++ b/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md @@ -11,18 +11,18 @@ tags: - best-practices --- -Thoughtful scheduling strategies can resolve pervasive challenges across web-distributed workloads and AI workloads like resiliency and resource utilization. But the default scheduler was primarily designed for general-purpose workloads and out-of-box pod scheduling. The scheduler ultimately selects the optimal node for pod(s) based on several criteria, including (but not limited to): +Thoughtful scheduling strategies can resolve pervasive challenges across web-distributed workloads and AI workloads like resiliency and resource utilization. But the default scheduler was primarily designed for general-purpose workloads and out-of-box pod scheduling that could be restrictive if you needed more fine-grain control. The scheduler selects the optimal node for newly created pod(s) based on several criteria, including (but not limited to): 1. Resource requirements (CPU, memory) 2. Node affinity/anti-affinity 3. Pod affinity/anti-affinity 4. Taints and tolerations -The criteria, and their respective priority in the scheduling cycle, are not suitable for advanced use cases that might require custom scheduling strategies. Nor, does the default scheduler enable user customization for fine-grain pod placement control while avoiding managing a second custom scheduler. For example, users running batch jobs might prefer collocating on a few nodes for better performance or cost-sensitive workloads might benefit from node binpacking to minimize idle node costs. +Out of the available nodes, the scheduler then filters out nodes that don't meet the requirements to identify the node that is most optimal for the pod(s). Today, the AKS default scheduler lacks the flexibility for users to change which criteria should be prioritized, and ignored, in the scheduling cycle on a per workload basis. This means the default scheduling criteria, and their fixed priority order, are not suitable for workloads that demand co-locating pods with their persistent volumes for increased data locality, optimizing GPU utilization for machine learning, or enforcing strict zone-level distribution for microservices. -To support these advanced use cases, and to give users more control, use [AKS Configurable Scheduler Profiles][concepts-scheduler-configuration] to tailor a scheduler to their specific workload requirements using node bin-packing, preemption, and 16 other scheduling plugins that can optimize ROI​, improve gpu utilization, improve data locality, or increase resiliency. +**Why use the AKS Configurable Scheduler Profiles versus the default kube-scheduler?** For extensability and configuration control. Now, customers can use the [AKS Configurable Scheduler Profiles][concepts-scheduler-configuration] to define their own scheduling logic by selecting specific policies, altering parameter weight, changing policy priority, adding additional policy paramaters, and changing policy evaluation point (i.e. pre-Filter, Filter, Score) to provide fine-grain control as unique as the workload's themselves. Now the criteria, and their respective priority in the scheduling cycle, are suitable for advanced use cases that would have otherwise required managing a second custom scheduler. The [AKS Configurable Scheduler Profiles][concepts-scheduler-configuration] enables users to optimize ROI​, improve gpu utilization, improve data locality, or increase resiliency. -In this blog you will learn how to configure the AKS Configurable Scheduler Profiles for three workload objectives: +In this blog you will learn how to configure AKS Configurable Scheduler Profiles for three workload objectives: 1. [Increase GPU utilization by bin packing GPU-backed nodes](#increase-gpu-utilization-by-bin-packing-gpu-backed-nodes) 2. [Increase resilience by distributing pods across topology domains](#increase-resilience-by-distributing-pods-across-topology-domains) @@ -50,7 +50,7 @@ Adjust VM SKUs in `NodeAffinity`, shift utilization curves or weights, and use t ### Increase GPU Utilization by Bin Packing GPU-backed Nodes -You can use `NodeResourceFit` to control how pods are assigned to nodes based on available resources (CPU, memory, etc.), including favoring nodes with high resource utilization, within the set configuration. +The AKS default scheduler scores nodes for workload placement based upon a _LeastAllocated_ strategy, to spread across the nodes in a cluster. However, this behavior does not result in efficient resource utilization, as nodes with higher allocation are not favored and cannot be used to their optimal capacity. You can use `NodeResourceFit` to control how pods are assigned to nodes based on available resources (CPU, GPU, memory, etc.), including favoring nodes with high resource utilization, within the set configuration. For example, scheduling pending jobs on nodes with a higher relative GPU utilization, users can reduce costs and increase GPU Utilization while maintaining performance. From cae92822b0470370394e1fb077554de1418c44d3 Mon Sep 17 00:00:00 2001 From: colinmixonn <109253437+colinmixonn@users.noreply.github.com> Date: Fri, 12 Dec 2025 11:27:02 -0800 Subject: [PATCH 028/164] Revise AKS Configurable Scheduler Profiles section Updated the explanation of the AKS Configurable Scheduler Profiles, emphasizing the problem it solves over the default kube-scheduler --- .../index.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md b/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md index 159d209cf..02a0d7f48 100644 --- a/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md +++ b/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md @@ -11,16 +11,16 @@ tags: - best-practices --- -Thoughtful scheduling strategies can resolve pervasive challenges across web-distributed workloads and AI workloads like resiliency and resource utilization. But the default scheduler was primarily designed for general-purpose workloads and out-of-box pod scheduling that could be restrictive if you needed more fine-grain control. The scheduler selects the optimal node for newly created pod(s) based on several criteria, including (but not limited to): +Thoughtful scheduling strategies can resolve pervasive challenges across web-distributed workloads and AI workloads like resiliency and resource utilization. But the default scheduler was primarily designed for general-purpose workloads and out-of-box pod scheduling that could be restrictive if you needed more fine-grain control since it applies a set of criteria in a fixed priority order. The scheduler selects the optimal node for newly created pod(s) based on several criteria, including (but not limited to): 1. Resource requirements (CPU, memory) 2. Node affinity/anti-affinity 3. Pod affinity/anti-affinity 4. Taints and tolerations -Out of the available nodes, the scheduler then filters out nodes that don't meet the requirements to identify the node that is most optimal for the pod(s). Today, the AKS default scheduler lacks the flexibility for users to change which criteria should be prioritized, and ignored, in the scheduling cycle on a per workload basis. This means the default scheduling criteria, and their fixed priority order, are not suitable for workloads that demand co-locating pods with their persistent volumes for increased data locality, optimizing GPU utilization for machine learning, or enforcing strict zone-level distribution for microservices. +Out of the available nodes, the scheduler then filters out nodes that don't meet the requirements to identify the node that is most optimal for the pod(s). Today, the AKS default scheduler lacks the flexibility for users to change which criteria should be prioritized, and ignored, in the scheduling cycle on a per workload basis. This means the default scheduling criteria, and their fixed priority order, are not suitable for workloads that demand co-locating pods with their persistent volumes for increased data locality, optimizing GPU utilization for machine learning, or enforcing strict zone-level distribution for microservices. This rigidity often forces users to either accept suboptimal placement or manage a separate custom scheduler, both of which increase operational complexity. -**Why use the AKS Configurable Scheduler Profiles versus the default kube-scheduler?** For extensability and configuration control. Now, customers can use the [AKS Configurable Scheduler Profiles][concepts-scheduler-configuration] to define their own scheduling logic by selecting specific policies, altering parameter weight, changing policy priority, adding additional policy paramaters, and changing policy evaluation point (i.e. pre-Filter, Filter, Score) to provide fine-grain control as unique as the workload's themselves. Now the criteria, and their respective priority in the scheduling cycle, are suitable for advanced use cases that would have otherwise required managing a second custom scheduler. The [AKS Configurable Scheduler Profiles][concepts-scheduler-configuration] enables users to optimize ROI​, improve gpu utilization, improve data locality, or increase resiliency. +**[AKS Configurable Scheduler Profiles][concepts-scheduler-configuration] reduces operational complexity by providing extensibility and control.** Now, customers can define their own scheduling logic by selecting specific policies, altering parameter weight, changing policy priority, adding additional policy paramaters, and changing policy evaluation point (i.e. pre-Filter, Filter, Score) without deploying a second scheduler. The AKS Configurable Scheduler Profiles enables users to optimize resource ROI​, improve GPU utilization, reduce data fragmentation, or increase resiliency. In this blog you will learn how to configure AKS Configurable Scheduler Profiles for three workload objectives: From f824195fbe30b2a8ef1286d38d64e1fb69d64cb1 Mon Sep 17 00:00:00 2001 From: colinmixonn <109253437+colinmixonn@users.noreply.github.com> Date: Fri, 12 Dec 2025 12:09:36 -0800 Subject: [PATCH 029/164] Fix typo in AKS Configurable Scheduler Profiles description Corrected a typo in the description of AKS Configurable Scheduler Profiles, changing 'paramaters' to 'parameters'. --- .../2025-12-16-aks-config-scheduler-profiles-preview/index.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md b/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md index 02a0d7f48..91122558f 100644 --- a/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md +++ b/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md @@ -20,7 +20,7 @@ Thoughtful scheduling strategies can resolve pervasive challenges across web-dis Out of the available nodes, the scheduler then filters out nodes that don't meet the requirements to identify the node that is most optimal for the pod(s). Today, the AKS default scheduler lacks the flexibility for users to change which criteria should be prioritized, and ignored, in the scheduling cycle on a per workload basis. This means the default scheduling criteria, and their fixed priority order, are not suitable for workloads that demand co-locating pods with their persistent volumes for increased data locality, optimizing GPU utilization for machine learning, or enforcing strict zone-level distribution for microservices. This rigidity often forces users to either accept suboptimal placement or manage a separate custom scheduler, both of which increase operational complexity. -**[AKS Configurable Scheduler Profiles][concepts-scheduler-configuration] reduces operational complexity by providing extensibility and control.** Now, customers can define their own scheduling logic by selecting specific policies, altering parameter weight, changing policy priority, adding additional policy paramaters, and changing policy evaluation point (i.e. pre-Filter, Filter, Score) without deploying a second scheduler. The AKS Configurable Scheduler Profiles enables users to optimize resource ROI​, improve GPU utilization, reduce data fragmentation, or increase resiliency. +**[AKS Configurable Scheduler Profiles][concepts-scheduler-configuration] reduces operational complexity by providing extensibility and control.** Now, customers can define their own scheduling logic by selecting specific policies, altering parameter weight, changing policy priority, adding additional policy parameters, and changing policy evaluation point (i.e. pre-Filter, Filter, Score) without deploying a second scheduler. The AKS Configurable Scheduler Profiles enables users to optimize resource ROI​, improve GPU utilization, reduce data fragmentation, or increase resiliency. In this blog you will learn how to configure AKS Configurable Scheduler Profiles for three workload objectives: From 80fafbc3b691d9c75c9715d800d57890f01a8459 Mon Sep 17 00:00:00 2001 From: colinmixonn <109253437+colinmixonn@users.noreply.github.com> Date: Fri, 12 Dec 2025 14:58:24 -0800 Subject: [PATCH 030/164] Enhance AKS Configurable Scheduler Profiles blog content Updated the blog post to clarify the benefits of AKS Configurable Scheduler Profiles, including user feedback on increased resiliency and reduced operational overhead. Adjusted language for improved clarity and conciseness throughout the article. --- .../index.md | 14 ++++++++------ 1 file changed, 8 insertions(+), 6 deletions(-) diff --git a/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md b/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md index 91122558f..ec603b306 100644 --- a/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md +++ b/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md @@ -20,7 +20,7 @@ Thoughtful scheduling strategies can resolve pervasive challenges across web-dis Out of the available nodes, the scheduler then filters out nodes that don't meet the requirements to identify the node that is most optimal for the pod(s). Today, the AKS default scheduler lacks the flexibility for users to change which criteria should be prioritized, and ignored, in the scheduling cycle on a per workload basis. This means the default scheduling criteria, and their fixed priority order, are not suitable for workloads that demand co-locating pods with their persistent volumes for increased data locality, optimizing GPU utilization for machine learning, or enforcing strict zone-level distribution for microservices. This rigidity often forces users to either accept suboptimal placement or manage a separate custom scheduler, both of which increase operational complexity. -**[AKS Configurable Scheduler Profiles][concepts-scheduler-configuration] reduces operational complexity by providing extensibility and control.** Now, customers can define their own scheduling logic by selecting specific policies, altering parameter weight, changing policy priority, adding additional policy parameters, and changing policy evaluation point (i.e. pre-Filter, Filter, Score) without deploying a second scheduler. The AKS Configurable Scheduler Profiles enables users to optimize resource ROI​, improve GPU utilization, reduce data fragmentation, or increase resiliency. +**[AKS Configurable Scheduler Profiles][concepts-scheduler-configuration] reduces operational complexity by providing extensibility and control.** Now, customers can define their own scheduling logic by selecting specific policies, altering parameter weight, changing policy priority, adding additional policy parameters, and changing policy evaluation point (i.e. pre-Filter, Filter, Score) without deploying a second scheduler. On AKS, customers have mentioned that AKS Configurable Scheduler Profiles allows them to increase resiliency without operational overhead of YAML wrangling or reduce cluster costs without adopting a secondary scheduler. Additionally, our AI and HPC customers have batch workloads that have benefitted from improved bin-packing and increased GPU utilization. In this blog you will learn how to configure AKS Configurable Scheduler Profiles for three workload objectives: @@ -34,15 +34,15 @@ Lastly, you will find [best practices](#best-practices-and-configuration-conside ## AKS Configurable Scheduler Profiles -A scheduler profile is a set of one or more in-tree scheduling plugins and configurations that dictate how to schedule a pod. Previously, the scheduler configuration wasn't accessible to users. Starting from Kubernetes version 1.33, you can now configure and set a scheduler profile for the AKS scheduler on your cluster. +AKS Configurable Scheduler Profiles uses a Custom Resource Definition (CRD) that lets users define custom scheduler profiles. A dedicated controller continuously reconciles these user-defined configurations with the underlying kube-scheduler deployment, validating changes and applying them transparently. If a configuration causes the scheduler to become unhealthy, the controller automatically rolls back to the last known good state to ensure cluster stability. -AKS supports 18 in-tree Kubernetes scheduling plugins that allow pods to be placed on user-specified nodes, ensure pods are matched with specific storage resources, and more. The plugins can be generally grouped into the following categories: +A scheduler profile is a set of one or more in-tree scheduling plugins and configurations that dictate how to schedule a pod. Previously, the scheduler configuration wasn't accessible to users. Starting from Kubernetes version 1.33, you can now configure and set a scheduler profile for the AKS scheduler on your cluster. AKS supports 18 in-tree Kubernetes [scheduling plugins][supported-in-tree-scheduling-plugins]. The plugins can be generally grouped into three categories: 1. Scheduling constraints and order-based plugins 2. Node selection constraints scheduling plugins 3. Resource and topology optimization scheduling plugins -Below you will find example configurations for some of the most common workload objectives. +Below you will find example configurations for common workload objectives. :::note Adjust VM SKUs in `NodeAffinity`, shift utilization curves or weights, and use the right zones for your cluster(s) in the configurations below. @@ -50,7 +50,7 @@ Adjust VM SKUs in `NodeAffinity`, shift utilization curves or weights, and use t ### Increase GPU Utilization by Bin Packing GPU-backed Nodes -The AKS default scheduler scores nodes for workload placement based upon a _LeastAllocated_ strategy, to spread across the nodes in a cluster. However, this behavior does not result in efficient resource utilization, as nodes with higher allocation are not favored and cannot be used to their optimal capacity. You can use `NodeResourceFit` to control how pods are assigned to nodes based on available resources (CPU, GPU, memory, etc.), including favoring nodes with high resource utilization, within the set configuration. +The AKS default scheduler scores nodes for workload placement based on a _LeastAllocated_ strategy, to spread across the nodes in a cluster. However, this behavior results in inefficient resource utilization, as nodes with higher allocation are not favored. You can use `NodeResourceFit` to control how pods are assigned to nodes based on available resources (CPU, GPU, memory, etc.), including favoring nodes with high resource utilization, within the set configuration. For example, scheduling pending jobs on nodes with a higher relative GPU utilization, users can reduce costs and increase GPU Utilization while maintaining performance. @@ -92,7 +92,7 @@ spec: ### Increase resilience by distributing pods across topology domains -`PodTopologySpread` is a scheduling strategy that seeks to distribute pods evenly across failure domains (such as availability zones or regions) to ensure high availability and fault tolerance in the event of zone or node failures. +If you don't configure any cluster-level default constraints for pod topology spreading, then the default kube-scheduler can unevenly spread pods across zones and hosts unless specified in each deployments, every time. Use `PodTopologySpread` to control how pods are distributed across failure domains to ensure high availability and fault tolerance in the event of zone or node failures, without overprovisioning. For example, spreading replicas across distinct zones safeguards availability during an AZ outage, while a softer host‑level rule prevents scheduling deadlocks when cluster capacity is uneven. @@ -231,3 +231,5 @@ With AKS Configurable Scheduler Profiles, teams gain fine-grained control over p [best-practices-advanced-scheduler]: https://learn.microsoft.com/azure/aks/operator-best-practices-advanced-scheduler [scheduling-framework/#interfaces]: https://kubernetes.io/docs/concepts/scheduling-eviction/scheduling-framework/#interfaces [memory-optimized-vm]: https://learn.microsoft.com/azure/virtual-machines/sizes/overview?tabs=breakdownseries%2Cgeneralsizelist%2Ccomputesizelist%2Cmemorysizelist%2Cstoragesizelist%2Cgpusizelist%2Cfpgasizelist%2Chpcsizelist#memory-optimized +[#internal-default-constraints]: https://kubernetes.io/docs/concepts/scheduling-eviction/topology-spread-constraints/#internal-default-constraints +[supported-in-tree-scheduling-plugins]: https://learn.microsoft.com/en-us/azure/aks/concepts-scheduler-configuration#supported-in-tree-scheduling-plugins From df42499662a6fcba6908e2e0588766ca643cd679 Mon Sep 17 00:00:00 2001 From: colinmixonn <109253437+colinmixonn@users.noreply.github.com> Date: Fri, 12 Dec 2025 15:03:55 -0800 Subject: [PATCH 031/164] Enhance GPU utilization explanation in AKS scheduler docs Clarified the explanation of the AKS default scheduler and its resource allocation strategy, emphasizing the benefits of using NodeResourceFit for GPU utilization. --- .../2025-12-16-aks-config-scheduler-profiles-preview/index.md | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md b/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md index ec603b306..d29a685ae 100644 --- a/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md +++ b/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md @@ -50,7 +50,7 @@ Adjust VM SKUs in `NodeAffinity`, shift utilization curves or weights, and use t ### Increase GPU Utilization by Bin Packing GPU-backed Nodes -The AKS default scheduler scores nodes for workload placement based on a _LeastAllocated_ strategy, to spread across the nodes in a cluster. However, this behavior results in inefficient resource utilization, as nodes with higher allocation are not favored. You can use `NodeResourceFit` to control how pods are assigned to nodes based on available resources (CPU, GPU, memory, etc.), including favoring nodes with high resource utilization, within the set configuration. +The AKS default scheduler scores nodes for workload placement based on a _LeastAllocated_ strategy, to spread across the nodes in a cluster. However, this behavior results in inefficient resource utilization, as nodes with higher allocation are not favored. You can use `NodeResourceFit` to control how pods are assigned to nodes based on available resources (CPU, GPU, memory, etc.), including favoring nodes with high resource utilization, within the set configuration. For example, scheduling pending jobs on nodes with a higher relative GPU utilization, users can reduce costs and increase GPU Utilization while maintaining performance. @@ -231,5 +231,4 @@ With AKS Configurable Scheduler Profiles, teams gain fine-grained control over p [best-practices-advanced-scheduler]: https://learn.microsoft.com/azure/aks/operator-best-practices-advanced-scheduler [scheduling-framework/#interfaces]: https://kubernetes.io/docs/concepts/scheduling-eviction/scheduling-framework/#interfaces [memory-optimized-vm]: https://learn.microsoft.com/azure/virtual-machines/sizes/overview?tabs=breakdownseries%2Cgeneralsizelist%2Ccomputesizelist%2Cmemorysizelist%2Cstoragesizelist%2Cgpusizelist%2Cfpgasizelist%2Chpcsizelist#memory-optimized -[#internal-default-constraints]: https://kubernetes.io/docs/concepts/scheduling-eviction/topology-spread-constraints/#internal-default-constraints [supported-in-tree-scheduling-plugins]: https://learn.microsoft.com/en-us/azure/aks/concepts-scheduler-configuration#supported-in-tree-scheduling-plugins From aedfcc4551a6fbeac57354c28803443c624f6232 Mon Sep 17 00:00:00 2001 From: colinmixonn <109253437+colinmixonn@users.noreply.github.com> Date: Mon, 15 Dec 2025 12:27:44 -0800 Subject: [PATCH 032/164] Correct typo in best practices --- .../2025-12-16-aks-config-scheduler-profiles-preview/index.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md b/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md index d29a685ae..107bd69a5 100644 --- a/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md +++ b/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md @@ -208,7 +208,7 @@ spec: As a reminder, there are many parameters the scheduler considers across the [scheduling cycle][scheduling-framework/#interfaces] before a pod is placed on a node that impacts how a pod is assigned. This section is meant to help guide how you consider both individual plugin configurations, your custom scheduler configuration, and your Deployment design holistically. 1. Ensure the intended deployment is assigned to the _correct_ scheduler profile. -2. Ensure the customer scheduler profile complements the implementation of Deployments, StorageClasses, and PersistentVolumeClaim's. Misalignment can lead to pending pods and degraded workload performance, even when the scheduler is functioning as expected. +2. Ensure the custom scheduler profile complements the implementation of Deployments, StorageClasses, and PersistentVolumeClaim's. Misalignment can lead to pending pods and degraded workload performance, even when the scheduler is functioning as expected. 3. Ensure there are enough nodes in each zone to accommodate your deployment replicas and ensure your AKS node pool spans the right availability zones. If not, pods may remain in a pending state. 4. Use namespaces to separate workloads which improves your ability to validate or troubleshoot. 5. Assign `priorityClassName` for workloads that should preempt others, this is critical if you use the DefaultPreemption plugin. From ab2dcc1be19c13b72c72240aa84d7becdd5fc205 Mon Sep 17 00:00:00 2001 From: colinmixonn <109253437+colinmixonn@users.noreply.github.com> Date: Mon, 15 Dec 2025 12:43:11 -0800 Subject: [PATCH 033/164] Apply suggestions from code review Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> --- .../2025-12-16-aks-config-scheduler-profiles-preview/index.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md b/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md index 107bd69a5..b3e171139 100644 --- a/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md +++ b/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md @@ -71,7 +71,7 @@ spec: multiPoint: enabled: - name: ImageLocality - - name: NodeResourceFit + - name: NodeResourcesFit - name: NodeResourcesBalancedAllocation pluginConfig: - name: NodeResourcesFit @@ -231,4 +231,4 @@ With AKS Configurable Scheduler Profiles, teams gain fine-grained control over p [best-practices-advanced-scheduler]: https://learn.microsoft.com/azure/aks/operator-best-practices-advanced-scheduler [scheduling-framework/#interfaces]: https://kubernetes.io/docs/concepts/scheduling-eviction/scheduling-framework/#interfaces [memory-optimized-vm]: https://learn.microsoft.com/azure/virtual-machines/sizes/overview?tabs=breakdownseries%2Cgeneralsizelist%2Ccomputesizelist%2Cmemorysizelist%2Cstoragesizelist%2Cgpusizelist%2Cfpgasizelist%2Chpcsizelist#memory-optimized -[supported-in-tree-scheduling-plugins]: https://learn.microsoft.com/en-us/azure/aks/concepts-scheduler-configuration#supported-in-tree-scheduling-plugins +[supported-in-tree-scheduling-plugins]: https://learn.microsoft.com/azure/aks/concepts-scheduler-configuration#supported-in-tree-scheduling-plugins From 065d1b4965044368e13ff19da680cb9269f3fd48 Mon Sep 17 00:00:00 2001 From: colinmixonn <109253437+colinmixonn@users.noreply.github.com> Date: Mon, 15 Dec 2025 12:45:02 -0800 Subject: [PATCH 034/164] Apply suggestions from code review Co-authored-by: Ahmed Sabbour <103856+sabbour@users.noreply.github.com> --- .../2025-12-16-aks-config-scheduler-profiles-preview/index.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md b/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md index b3e171139..b7592e39f 100644 --- a/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md +++ b/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md @@ -50,7 +50,7 @@ Adjust VM SKUs in `NodeAffinity`, shift utilization curves or weights, and use t ### Increase GPU Utilization by Bin Packing GPU-backed Nodes -The AKS default scheduler scores nodes for workload placement based on a _LeastAllocated_ strategy, to spread across the nodes in a cluster. However, this behavior results in inefficient resource utilization, as nodes with higher allocation are not favored. You can use `NodeResourceFit` to control how pods are assigned to nodes based on available resources (CPU, GPU, memory, etc.), including favoring nodes with high resource utilization, within the set configuration. +The AKS default scheduler scores nodes for workload placement based on a _LeastAllocated_ strategy, to spread across the nodes in a cluster. However, this behavior can result in inefficient resource utilization, as nodes with higher allocation are not favored. You can use `NodeResourceFit` to control how pods are assigned to nodes based on available resources (CPU, GPU, memory, etc.), including favoring nodes with high resource utilization, within the set configuration. For example, scheduling pending jobs on nodes with a higher relative GPU utilization, users can reduce costs and increase GPU Utilization while maintaining performance. From 63821dd894c69d3af6ef28d5d6d8998170eff98a Mon Sep 17 00:00:00 2001 From: Ahmed Sabbour <103856+sabbour@users.noreply.github.com> Date: Fri, 23 Jan 2026 12:42:25 -0800 Subject: [PATCH 035/164] Update website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> --- .../2025-12-16-aks-config-scheduler-profiles-preview/index.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md b/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md index b7592e39f..ba88abb15 100644 --- a/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md +++ b/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md @@ -124,7 +124,7 @@ spec: ### Optimize data locality with Memory and PVC-Aware Scheduling -Use `VolumeBinding` to ensure pods are placed on nodes where _PersistentVolumeClaim's_ (PVC) can bind to _PersistentVolume's_ (PV). `VolumeZone` validates that nodes and volumes satisfy zonal requirements to avoid cross-zone storage access. +Use `VolumeBinding` to ensure pods are placed on nodes where _PersistentVolumeClaims_ (PVC) can bind to _PersistentVolumes_ (PV). `VolumeZone` validates that nodes and volumes satisfy zonal requirements to avoid cross-zone storage access. For example, combine `VolumeBinding` and `VolumeZone` plugins, with `NodeAffinity` and `NodeResourcesFit` with `RequestedToCapacityRatio`, to influence pod placement on [Azure memory-optimized SKUs][memory-optimized-vm], while ensuring PVC's bind quickly in the target zone to minimize cross‑zone traffic and latency. From 2d0f77582e7af43e15be3c2761d8708eb7264056 Mon Sep 17 00:00:00 2001 From: Ahmed Sabbour <103856+sabbour@users.noreply.github.com> Date: Fri, 23 Jan 2026 12:48:46 -0800 Subject: [PATCH 036/164] Update website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> --- .../2025-12-16-aks-config-scheduler-profiles-preview/index.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md b/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md index ba88abb15..d21e0f0ce 100644 --- a/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md +++ b/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md @@ -208,7 +208,7 @@ spec: As a reminder, there are many parameters the scheduler considers across the [scheduling cycle][scheduling-framework/#interfaces] before a pod is placed on a node that impacts how a pod is assigned. This section is meant to help guide how you consider both individual plugin configurations, your custom scheduler configuration, and your Deployment design holistically. 1. Ensure the intended deployment is assigned to the _correct_ scheduler profile. -2. Ensure the custom scheduler profile complements the implementation of Deployments, StorageClasses, and PersistentVolumeClaim's. Misalignment can lead to pending pods and degraded workload performance, even when the scheduler is functioning as expected. +2. Ensure the custom scheduler profile complements the implementation of Deployments, StorageClasses, and PersistentVolumeClaims. Misalignment can lead to pending pods and degraded workload performance, even when the scheduler is functioning as expected. 3. Ensure there are enough nodes in each zone to accommodate your deployment replicas and ensure your AKS node pool spans the right availability zones. If not, pods may remain in a pending state. 4. Use namespaces to separate workloads which improves your ability to validate or troubleshoot. 5. Assign `priorityClassName` for workloads that should preempt others, this is critical if you use the DefaultPreemption plugin. From 6f08f29b283a9d7814af225c1e60d9fd10bf9431 Mon Sep 17 00:00:00 2001 From: Ahmed Sabbour <103856+sabbour@users.noreply.github.com> Date: Fri, 23 Jan 2026 12:49:57 -0800 Subject: [PATCH 037/164] Update website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> --- .../2025-12-16-aks-config-scheduler-profiles-preview/index.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md b/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md index d21e0f0ce..4fbed961f 100644 --- a/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md +++ b/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md @@ -216,7 +216,7 @@ As a reminder, there are many parameters the scheduler considers across the [sch 7. If your cluster is large, a low `PercentageOfNodesToScore` speeds scheduling by reducing the number of nodes scored, _but_ it may reduce optimal placement. 8. If you enable a plugin in the `plugins:multipoint` section but do not define it in `pluginConfig`, AKS uses the default configuration for that plugin. 9. For `NodeResourcesFit`, the ratio matters more than absolute values. So CPU:Memory:Storage = 3:1:2, which means CPU is 3× more influential than memory, and storage is 2x more influential than memory in the scoring phase. -10. Pair `PodTopologySpread` with Pod Disruption Budget's (PDB) and multi‑replica strategies for HA during upgrades. +10. Pair `PodTopologySpread` with Pod Disruption Budgets (PDBs) and multi‑replica strategies for HA during upgrades. ## Next Steps: Try out AKS Configurable Scheduler From 1052ca6b3ab825e5ee4b27ae2db270e5c470aa41 Mon Sep 17 00:00:00 2001 From: colinmixonn <109253437+colinmixonn@users.noreply.github.com> Date: Mon, 9 Mar 2026 16:07:09 -0700 Subject: [PATCH 038/164] Apply suggestions from code review Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> --- .../index.md | 16 ++++++++-------- 1 file changed, 8 insertions(+), 8 deletions(-) diff --git a/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md b/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md index 4fbed961f..4cee8fd5e 100644 --- a/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md +++ b/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md @@ -1,7 +1,7 @@ --- -title: "Announcing Azure Configurable Scheduler Profiles (Preview): Optimize resource ROI with fine grained scheduling strategies" -description: "Avoid scheduling inefficiencies and improve GPU utilization with AKS Configurable Scheduler Profiles" -date: 2025-12-16 +title: "AKS configurable scheduler profiles (preview)" +description: "Optimize AKS scheduling with configurable scheduler profiles that improve GPU utilization and align pod placement to your critical workloads at scale." +date: 2026-01-23 authors: - colin-mixon tags: @@ -11,7 +11,7 @@ tags: - best-practices --- -Thoughtful scheduling strategies can resolve pervasive challenges across web-distributed workloads and AI workloads like resiliency and resource utilization. But the default scheduler was primarily designed for general-purpose workloads and out-of-box pod scheduling that could be restrictive if you needed more fine-grain control since it applies a set of criteria in a fixed priority order. The scheduler selects the optimal node for newly created pod(s) based on several criteria, including (but not limited to): +Thoughtful scheduling strategies can resolve pervasive challenges across web-distributed workloads and AI workloads like resiliency and resource utilization. But the default scheduler was primarily designed for general-purpose workloads and out-of-box pod scheduling that could be restrictive if you needed more fine-grained control since it applies a set of criteria in a fixed priority order. The scheduler selects the optimal node for newly created pod(s) based on several criteria, including (but not limited to): 1. Resource requirements (CPU, memory) 2. Node affinity/anti-affinity @@ -20,7 +20,7 @@ Thoughtful scheduling strategies can resolve pervasive challenges across web-dis Out of the available nodes, the scheduler then filters out nodes that don't meet the requirements to identify the node that is most optimal for the pod(s). Today, the AKS default scheduler lacks the flexibility for users to change which criteria should be prioritized, and ignored, in the scheduling cycle on a per workload basis. This means the default scheduling criteria, and their fixed priority order, are not suitable for workloads that demand co-locating pods with their persistent volumes for increased data locality, optimizing GPU utilization for machine learning, or enforcing strict zone-level distribution for microservices. This rigidity often forces users to either accept suboptimal placement or manage a separate custom scheduler, both of which increase operational complexity. -**[AKS Configurable Scheduler Profiles][concepts-scheduler-configuration] reduces operational complexity by providing extensibility and control.** Now, customers can define their own scheduling logic by selecting specific policies, altering parameter weight, changing policy priority, adding additional policy parameters, and changing policy evaluation point (i.e. pre-Filter, Filter, Score) without deploying a second scheduler. On AKS, customers have mentioned that AKS Configurable Scheduler Profiles allows them to increase resiliency without operational overhead of YAML wrangling or reduce cluster costs without adopting a secondary scheduler. Additionally, our AI and HPC customers have batch workloads that have benefitted from improved bin-packing and increased GPU utilization. +**[AKS Configurable Scheduler Profiles][concepts-scheduler-configuration] reduces operational complexity by providing extensibility and control.** Now, customers can define their own scheduling logic by selecting specific policies, altering parameter weight, changing policy priority, adding additional policy parameters, and changing policy evaluation point (i.e. PreFilter, Filter, Score) without deploying a second scheduler. On AKS, customers have mentioned that AKS Configurable Scheduler Profiles allows them to increase resiliency without operational overhead of YAML wrangling or reduce cluster costs without adopting a secondary scheduler. Additionally, our AI and HPC customers have batch workloads that have benefitted from improved bin-packing and increased GPU utilization. In this blog you will learn how to configure AKS Configurable Scheduler Profiles for three workload objectives: @@ -50,7 +50,7 @@ Adjust VM SKUs in `NodeAffinity`, shift utilization curves or weights, and use t ### Increase GPU Utilization by Bin Packing GPU-backed Nodes -The AKS default scheduler scores nodes for workload placement based on a _LeastAllocated_ strategy, to spread across the nodes in a cluster. However, this behavior can result in inefficient resource utilization, as nodes with higher allocation are not favored. You can use `NodeResourceFit` to control how pods are assigned to nodes based on available resources (CPU, GPU, memory, etc.), including favoring nodes with high resource utilization, within the set configuration. +The AKS default scheduler scores nodes for workload placement based on a _LeastAllocated_ strategy, to spread across the nodes in a cluster. However, this behavior can result in inefficient resource utilization, as nodes with higher allocation are not favored. You can use `NodeResourcesFit` to control how pods are assigned to nodes based on available resources (CPU, GPU, memory, etc.), including favoring nodes with high resource utilization, within the set configuration. For example, scheduling pending jobs on nodes with a higher relative GPU utilization, users can reduce costs and increase GPU Utilization while maintaining performance. @@ -92,7 +92,7 @@ spec: ### Increase resilience by distributing pods across topology domains -If you don't configure any cluster-level default constraints for pod topology spreading, then the default kube-scheduler can unevenly spread pods across zones and hosts unless specified in each deployments, every time. Use `PodTopologySpread` to control how pods are distributed across failure domains to ensure high availability and fault tolerance in the event of zone or node failures, without overprovisioning. +If you don't configure any cluster-level default constraints for pod topology spreading, then the default kube-scheduler can unevenly spread pods across zones and hosts unless specified in each deployment, every time. Use `PodTopologySpread` to control how pods are distributed across failure domains to ensure high availability and fault tolerance in the event of zone or node failures, without overprovisioning. For example, spreading replicas across distinct zones safeguards availability during an AZ outage, while a softer host‑level rule prevents scheduling deadlocks when cluster capacity is uneven. @@ -126,7 +126,7 @@ spec: Use `VolumeBinding` to ensure pods are placed on nodes where _PersistentVolumeClaims_ (PVC) can bind to _PersistentVolumes_ (PV). `VolumeZone` validates that nodes and volumes satisfy zonal requirements to avoid cross-zone storage access. -For example, combine `VolumeBinding` and `VolumeZone` plugins, with `NodeAffinity` and `NodeResourcesFit` with `RequestedToCapacityRatio`, to influence pod placement on [Azure memory-optimized SKUs][memory-optimized-vm], while ensuring PVC's bind quickly in the target zone to minimize cross‑zone traffic and latency. +For example, combine `VolumeBinding` and `VolumeZone` plugins, with `NodeAffinity` and `NodeResourcesFit` with `RequestedToCapacityRatio`, to influence pod placement on [Azure memory-optimized SKUs][memory-optimized-vm], while ensuring PVCs bind quickly in the target zone to minimize cross‑zone traffic and latency. **This scheduler configuration ensures workloads needing large memory footprints are placed on nodes that provide sufficient RAM and maintain proximity to their volumes, enabling fast, zone‑aligned PVC binding for optimal data locality.** From e649e06a04bc17d8f4e4c15c5ed6260a9b2fa7ee Mon Sep 17 00:00:00 2001 From: colinmixonn <109253437+colinmixonn@users.noreply.github.com> Date: Mon, 9 Mar 2026 16:10:52 -0700 Subject: [PATCH 039/164] Revise AKS configurable scheduler profiles preview Updated the AKS scheduler profiles preview with new date and refined content. Removed outdated sections and optimized descriptions for clarity. --- .../index.md | 79 ++----------------- 1 file changed, 8 insertions(+), 71 deletions(-) diff --git a/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md b/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md index 4cee8fd5e..110e46e01 100644 --- a/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md +++ b/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md @@ -1,7 +1,7 @@ --- title: "AKS configurable scheduler profiles (preview)" description: "Optimize AKS scheduling with configurable scheduler profiles that improve GPU utilization and align pod placement to your critical workloads at scale." -date: 2026-01-23 +date: 2026-04-23 authors: - colin-mixon tags: @@ -90,43 +90,9 @@ spec: weight: 1 ``` -### Increase resilience by distributing pods across topology domains -If you don't configure any cluster-level default constraints for pod topology spreading, then the default kube-scheduler can unevenly spread pods across zones and hosts unless specified in each deployment, every time. Use `PodTopologySpread` to control how pods are distributed across failure domains to ensure high availability and fault tolerance in the event of zone or node failures, without overprovisioning. +### ResourceToCapacity -For example, spreading replicas across distinct zones safeguards availability during an AZ outage, while a softer host‑level rule prevents scheduling deadlocks when cluster capacity is uneven. - -**This configuration is effective for highly‑available stateless services (web/API, gateways) or distributed messaging clusters, like Kafka brokers, that rely on the availability of multiple replicas.** - -```yaml -apiVersion: aks.azure.com/v1alpha1 -kind: SchedulerConfiguration -metadata: - name: upstream -spec: - rawConfig: | - apiVersion: kubescheduler.config.k8s.io/v1 - kind: KubeSchedulerConfiguration - profiles: - - schedulerName: pod-distribution-scheduler - pluginConfig: - - name: PodTopologySpread - args: - defaultingType: List - defaultConstraints: - - maxSkew: 2 - topologyKey: topology.kubernetes.io/zone - whenUnsatisfiable: DoNotSchedule - - maxSkew: 1 - topologyKey: kubernetes.io/hostname - whenUnsatisfiable: ScheduleAnyway -``` - -### Optimize data locality with Memory and PVC-Aware Scheduling - -Use `VolumeBinding` to ensure pods are placed on nodes where _PersistentVolumeClaims_ (PVC) can bind to _PersistentVolumes_ (PV). `VolumeZone` validates that nodes and volumes satisfy zonal requirements to avoid cross-zone storage access. - -For example, combine `VolumeBinding` and `VolumeZone` plugins, with `NodeAffinity` and `NodeResourcesFit` with `RequestedToCapacityRatio`, to influence pod placement on [Azure memory-optimized SKUs][memory-optimized-vm], while ensuring PVCs bind quickly in the target zone to minimize cross‑zone traffic and latency. **This scheduler configuration ensures workloads needing large memory footprints are placed on nodes that provide sufficient RAM and maintain proximity to their volumes, enabling fast, zone‑aligned PVC binding for optimal data locality.** @@ -140,40 +106,12 @@ spec: apiVersion: kubescheduler.config.k8s.io/v1 kind: KubeSchedulerConfiguration profiles: - - schedulerName: mem-optimized-node-scheduler + - schedulerName: cpu-binpacking-scheduler plugins: multiPoint: enabled: - - name: NodeAffinity - name: NodeResourcesFit - - name: VolumeBinding - - name: VolumeZone pluginConfig: - - name: NodeAffinity - args: - apiVersion: kubescheduler.config.k8s.io/v1 - kind: NodeAffinityArgs - addedAffinity: - preferredDuringSchedulingIgnoredDuringExecution: - - weight: 100 - preference: - matchExpressions: - - key: topology.kubernetes.io/zone - operator: In - values: [westus3-1, westus3-2, westus3-3] - - weight: 80 - preference: - matchExpressions: - - key: node.kubernetes.io/instance-type - operator: In - values: - - Standard_E16s_v5 - - Standard_E32s_v5 - - name: VolumeBinding - args: - apiVersion: kubescheduler.config.k8s.io/v1 - kind: VolumeBindingArgs - bindTimeoutSeconds: 300 - name: NodeResourcesFit args: apiVersion: kubescheduler.config.k8s.io/v1 @@ -192,13 +130,13 @@ spec: - utilization: 0 score: 0 - utilization: 30 - score: 4 - - utilization: 60 score: 8 - - utilization: 80 + - utilization: 50 + score: 10 + - utilization: 85 score: 10 - utilization: 90 - score: 6 + score: 3 - utilization: 100 score: 0 ``` @@ -208,10 +146,9 @@ spec: As a reminder, there are many parameters the scheduler considers across the [scheduling cycle][scheduling-framework/#interfaces] before a pod is placed on a node that impacts how a pod is assigned. This section is meant to help guide how you consider both individual plugin configurations, your custom scheduler configuration, and your Deployment design holistically. 1. Ensure the intended deployment is assigned to the _correct_ scheduler profile. -2. Ensure the custom scheduler profile complements the implementation of Deployments, StorageClasses, and PersistentVolumeClaims. Misalignment can lead to pending pods and degraded workload performance, even when the scheduler is functioning as expected. +2. Ensure the custom scheduler profile compliments the implementation of Deployments, StorageClasses, and PersistentVolumeClaims. Misalignment can lead to pending pods and degraded workload performance, even when the scheduler is functioning as expected. 3. Ensure there are enough nodes in each zone to accommodate your deployment replicas and ensure your AKS node pool spans the right availability zones. If not, pods may remain in a pending state. 4. Use namespaces to separate workloads which improves your ability to validate or troubleshoot. -5. Assign `priorityClassName` for workloads that should preempt others, this is critical if you use the DefaultPreemption plugin. 6. If you use the `ImageLocality` plugin, use DaemonSets or node pre-pulling for latency-sensitive images, otherwise the benefit may be minimal. 7. If your cluster is large, a low `PercentageOfNodesToScore` speeds scheduling by reducing the number of nodes scored, _but_ it may reduce optimal placement. 8. If you enable a plugin in the `plugins:multipoint` section but do not define it in `pluginConfig`, AKS uses the default configuration for that plugin. From 0b3fd861e76923048f8932293e644956e0af3afa Mon Sep 17 00:00:00 2001 From: colinmixonn <109253437+colinmixonn@users.noreply.github.com> Date: Mon, 9 Mar 2026 16:14:05 -0700 Subject: [PATCH 040/164] Apply suggestions from code review Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> --- .../2025-12-16-aks-config-scheduler-profiles-preview/index.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md b/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md index 110e46e01..06c598c21 100644 --- a/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md +++ b/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md @@ -2,8 +2,8 @@ title: "AKS configurable scheduler profiles (preview)" description: "Optimize AKS scheduling with configurable scheduler profiles that improve GPU utilization and align pod placement to your critical workloads at scale." date: 2026-04-23 -authors: - - colin-mixon +authors: + - colin-mixon tags: - ai - performance From 6f1a43aebf10b608ad6650118b959f7557dd094e Mon Sep 17 00:00:00 2001 From: colinmixonn <109253437+colinmixonn@users.noreply.github.com> Date: Tue, 10 Mar 2026 13:36:13 -0700 Subject: [PATCH 041/164] Revise section on AKS Configurable Scheduler Profiles Updated the section title and enhanced the description of AKS Configurable Scheduler Profiles to emphasize optimization and testing. --- .../2025-12-16-aks-config-scheduler-profiles-preview/index.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md b/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md index 06c598c21..0e5cd8642 100644 --- a/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md +++ b/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md @@ -155,7 +155,7 @@ As a reminder, there are many parameters the scheduler considers across the [sch 9. For `NodeResourcesFit`, the ratio matters more than absolute values. So CPU:Memory:Storage = 3:1:2, which means CPU is 3× more influential than memory, and storage is 2x more influential than memory in the scoring phase. 10. Pair `PodTopologySpread` with Pod Disruption Budgets (PDBs) and multi‑replica strategies for HA during upgrades. -## Next Steps: Try out AKS Configurable Scheduler +## Next Steps: Optimize and test with AKS Configurable Scheduler Profiles With AKS Configurable Scheduler Profiles, teams gain fine-grained control over pod placement strategies like bin-packing, topology distribution, and resource-based scoring that directly address the challenges of resilience and resource utilization for web-distributed workloads and AI workloads. By leveraging these advanced scheduling plugins, AKS users can ensure their workloads make full use of available GPU capacity, reduce idle time, and avoid costly overprovisioning. This not only improves ROI but also accelerates innovation by allowing more jobs to run concurrently and reliably. From 6b61379cb82cf90ccca243cec94fc5947da50227 Mon Sep 17 00:00:00 2001 From: colinmixonn <109253437+colinmixonn@users.noreply.github.com> Date: Tue, 10 Mar 2026 13:36:58 -0700 Subject: [PATCH 042/164] Remove best practices section from AKS blog post Removed the 'Best Practices and Configuration Considerations' section to streamline content and focus on next steps for AKS Configurable Scheduler Profiles. --- .../index.md | 14 -------------- 1 file changed, 14 deletions(-) diff --git a/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md b/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md index 0e5cd8642..95eaa4499 100644 --- a/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md +++ b/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md @@ -141,20 +141,6 @@ spec: score: 0 ``` -## Best Practices and Configuration Considerations - -As a reminder, there are many parameters the scheduler considers across the [scheduling cycle][scheduling-framework/#interfaces] before a pod is placed on a node that impacts how a pod is assigned. This section is meant to help guide how you consider both individual plugin configurations, your custom scheduler configuration, and your Deployment design holistically. - -1. Ensure the intended deployment is assigned to the _correct_ scheduler profile. -2. Ensure the custom scheduler profile compliments the implementation of Deployments, StorageClasses, and PersistentVolumeClaims. Misalignment can lead to pending pods and degraded workload performance, even when the scheduler is functioning as expected. -3. Ensure there are enough nodes in each zone to accommodate your deployment replicas and ensure your AKS node pool spans the right availability zones. If not, pods may remain in a pending state. -4. Use namespaces to separate workloads which improves your ability to validate or troubleshoot. -6. If you use the `ImageLocality` plugin, use DaemonSets or node pre-pulling for latency-sensitive images, otherwise the benefit may be minimal. -7. If your cluster is large, a low `PercentageOfNodesToScore` speeds scheduling by reducing the number of nodes scored, _but_ it may reduce optimal placement. -8. If you enable a plugin in the `plugins:multipoint` section but do not define it in `pluginConfig`, AKS uses the default configuration for that plugin. -9. For `NodeResourcesFit`, the ratio matters more than absolute values. So CPU:Memory:Storage = 3:1:2, which means CPU is 3× more influential than memory, and storage is 2x more influential than memory in the scoring phase. -10. Pair `PodTopologySpread` with Pod Disruption Budgets (PDBs) and multi‑replica strategies for HA during upgrades. - ## Next Steps: Optimize and test with AKS Configurable Scheduler Profiles With AKS Configurable Scheduler Profiles, teams gain fine-grained control over pod placement strategies like bin-packing, topology distribution, and resource-based scoring that directly address the challenges of resilience and resource utilization for web-distributed workloads and AI workloads. By leveraging these advanced scheduling plugins, AKS users can ensure their workloads make full use of available GPU capacity, reduce idle time, and avoid costly overprovisioning. This not only improves ROI but also accelerates innovation by allowing more jobs to run concurrently and reliably. From 7da937d1dd1b8f385f7e2b346ce512cca6fa887a Mon Sep 17 00:00:00 2001 From: colinmixonn <109253437+colinmixonn@users.noreply.github.com> Date: Tue, 10 Mar 2026 13:53:09 -0700 Subject: [PATCH 043/164] Refactor authors and tags in blog post metadata Updated authors and tags formatting in the blog post. --- .../index.md | 9 ++------- 1 file changed, 2 insertions(+), 7 deletions(-) diff --git a/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md b/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md index 95eaa4499..0015e3234 100644 --- a/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md +++ b/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md @@ -2,13 +2,8 @@ title: "AKS configurable scheduler profiles (preview)" description: "Optimize AKS scheduling with configurable scheduler profiles that improve GPU utilization and align pod placement to your critical workloads at scale." date: 2026-04-23 -authors: - - colin-mixon -tags: - - ai - - performance - - scheduler - - best-practices +authors: [colin-mixon] +tags: [ai, performance, scheduler, best-practices] --- Thoughtful scheduling strategies can resolve pervasive challenges across web-distributed workloads and AI workloads like resiliency and resource utilization. But the default scheduler was primarily designed for general-purpose workloads and out-of-box pod scheduling that could be restrictive if you needed more fine-grained control since it applies a set of criteria in a fixed priority order. The scheduler selects the optimal node for newly created pod(s) based on several criteria, including (but not limited to): From eed9353135c61bdebc76a0a2f4189afb88ed188c Mon Sep 17 00:00:00 2001 From: colinmixonn <109253437+colinmixonn@users.noreply.github.com> Date: Tue, 10 Mar 2026 15:33:44 -0700 Subject: [PATCH 044/164] Revise blog content on AKS Configurable Scheduler Profiles Updated the blog post to clarify the objectives of configuring AKS Configurable Scheduler Profiles, changing the focus from general workload objectives to increased node utilization. Adjusted the list of objectives to reflect the new focus and improved clarity. --- .../index.md | 7 +++---- 1 file changed, 3 insertions(+), 4 deletions(-) diff --git a/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md b/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md index 0015e3234..4adbc0b3e 100644 --- a/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md +++ b/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md @@ -17,11 +17,10 @@ Out of the available nodes, the scheduler then filters out nodes that don't meet **[AKS Configurable Scheduler Profiles][concepts-scheduler-configuration] reduces operational complexity by providing extensibility and control.** Now, customers can define their own scheduling logic by selecting specific policies, altering parameter weight, changing policy priority, adding additional policy parameters, and changing policy evaluation point (i.e. PreFilter, Filter, Score) without deploying a second scheduler. On AKS, customers have mentioned that AKS Configurable Scheduler Profiles allows them to increase resiliency without operational overhead of YAML wrangling or reduce cluster costs without adopting a secondary scheduler. Additionally, our AI and HPC customers have batch workloads that have benefitted from improved bin-packing and increased GPU utilization. -In this blog you will learn how to configure AKS Configurable Scheduler Profiles for three workload objectives: +In this blog you will learn how to configure AKS Configurable Scheduler Profiles for three increased node utilization: -1. [Increase GPU utilization by bin packing GPU-backed nodes](#increase-gpu-utilization-by-bin-packing-gpu-backed-nodes) -2. [Increase resilience by distributing pods across topology domains](#increase-resilience-by-distributing-pods-across-topology-domains) -3. [Optimize data locality with memory and PVC-aware scheduling](#optimize-data-locality-with-memory-and-pvc-aware-scheduling) +1. [How to increase CPU utilization](#increase-gpu-utilization-by-bin-packing-gpu-backed-nodes) +2. [How to increase GPU utilization](#increase-gpu-utilization-by-bin-packing-gpu-backed-nodes) Lastly, you will find [best practices](#best-practices-and-configuration-considerations) to help guide how you consider both individual plugin configurations, your custom scheduler configuration, and your Deployment design holistically. From 42db1bd9a37c6957cfc34644391a1d4672019ded Mon Sep 17 00:00:00 2001 From: colinmixonn <109253437+colinmixonn@users.noreply.github.com> Date: Thu, 19 Mar 2026 10:10:45 -0700 Subject: [PATCH 045/164] Update tags for AKS configurable scheduler profiles post Added 'cost' tag to enhance categorization of the blog post. --- .../2025-12-16-aks-config-scheduler-profiles-preview/index.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md b/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md index 4adbc0b3e..03615020c 100644 --- a/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md +++ b/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md @@ -3,7 +3,7 @@ title: "AKS configurable scheduler profiles (preview)" description: "Optimize AKS scheduling with configurable scheduler profiles that improve GPU utilization and align pod placement to your critical workloads at scale." date: 2026-04-23 authors: [colin-mixon] -tags: [ai, performance, scheduler, best-practices] +tags: [ai, performance, scheduler, best-practices, cost] --- Thoughtful scheduling strategies can resolve pervasive challenges across web-distributed workloads and AI workloads like resiliency and resource utilization. But the default scheduler was primarily designed for general-purpose workloads and out-of-box pod scheduling that could be restrictive if you needed more fine-grained control since it applies a set of criteria in a fixed priority order. 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zq``=2m$UDk3Tgz71Lka4K@y9`ud7ljJM4Id_voJy>E*<0=L6=&P7+rnM^D>H@E}_% z4!`pThT!!Zc2=oVcg+5k%mKy|Os=RCY?tYwfZ6Fz=MOB_H7z?vHKDyn+@RVv!yh+Q z0DCLPvJni!t%M9ERoe#Ngb`DiIS92_<2E?jy->s}B%CL!0F&II;yP5tm}`i~E@8AC zYX)3%xel?$wocc7W2$M}*F4@dupGI4?!h=HLBgK> z$dCn5)+`j$W&Xu&zYJ0?w!e)VJ@&DJ7IIOv0w<_O2At3EXt>%A_XCfMg(@XGpr;EP xve-pgfD#b>-4V3dZqfvrg#3+;{|^hZwmHl0b^7ni9SYz8Vx(uLOT6H6@83=@&`1CP From 0307bac7b8751e429b282162a34fe6d970ba6d11 Mon Sep 17 00:00:00 2001 From: colinmixonn <109253437+colinmixonn@users.noreply.github.com> Date: Thu, 19 Mar 2026 10:29:10 -0700 Subject: [PATCH 049/164] Add diagram for AKS Configurable Scheduler Profiles Added a diagram to illustrate the Configurable Scheduler Profiles feature in AKS. --- .../2025-12-16-aks-config-scheduler-profiles-preview/index.md | 2 ++ 1 file changed, 2 insertions(+) diff --git a/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md b/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md index 03615020c..b3fb6ca32 100644 --- a/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md +++ b/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md @@ -30,6 +30,8 @@ Lastly, you will find [best practices](#best-practices-and-configuration-conside AKS Configurable Scheduler Profiles uses a Custom Resource Definition (CRD) that lets users define custom scheduler profiles. A dedicated controller continuously reconciles these user-defined configurations with the underlying kube-scheduler deployment, validating changes and applying them transparently. If a configuration causes the scheduler to become unhealthy, the controller automatically rolls back to the last known good state to ensure cluster stability. +![Configurable Scheduler Profiles Diagram](CONFIG_SCHEDULER_PROFILES.png) + A scheduler profile is a set of one or more in-tree scheduling plugins and configurations that dictate how to schedule a pod. Previously, the scheduler configuration wasn't accessible to users. Starting from Kubernetes version 1.33, you can now configure and set a scheduler profile for the AKS scheduler on your cluster. AKS supports 18 in-tree Kubernetes [scheduling plugins][supported-in-tree-scheduling-plugins]. The plugins can be generally grouped into three categories: 1. Scheduling constraints and order-based plugins From 5f85dd4bfe4212854a424da243e8c46e3e9fd862 Mon Sep 17 00:00:00 2001 From: colinmixonn <109253437+colinmixonn@users.noreply.github.com> Date: Thu, 19 Mar 2026 11:05:07 -0700 Subject: [PATCH 050/164] Update scheduling strategies description in blog post Refine the explanation of scheduling strategies and constraints for better clarity. --- .../2025-12-16-aks-config-scheduler-profiles-preview/index.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md b/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md index b3fb6ca32..9df11d37b 100644 --- a/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md +++ b/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md @@ -6,7 +6,7 @@ authors: [colin-mixon] tags: [ai, performance, scheduler, best-practices, cost] --- -Thoughtful scheduling strategies can resolve pervasive challenges across web-distributed workloads and AI workloads like resiliency and resource utilization. But the default scheduler was primarily designed for general-purpose workloads and out-of-box pod scheduling that could be restrictive if you needed more fine-grained control since it applies a set of criteria in a fixed priority order. The scheduler selects the optimal node for newly created pod(s) based on several criteria, including (but not limited to): +Thoughtful scheduling strategies can resolve pervasive challenges like resource utilization. The default scheduler was primarily designed for general-purpose workloads and out-of-box pod scheduling that can be restrictive if you want to bin pack nodes since the hard and soft constraints for pod scheduling do not align with scheudling pods with nodes of higher utilization. The scheduler selects the optimal node for queued pod(s) based on several constraints, including (but not limited to): 1. Resource requirements (CPU, memory) 2. Node affinity/anti-affinity From b040176b0b1ec31959f74d1f77c557d06b9233c7 Mon Sep 17 00:00:00 2001 From: colinmixonn <109253437+colinmixonn@users.noreply.github.com> Date: Thu, 19 Mar 2026 14:07:31 -0700 Subject: [PATCH 051/164] Enhance introduction with kube-scheduler context Updated introduction to include kube-scheduler as a core component of Kubernetes Control Plane. --- .../2025-12-16-aks-config-scheduler-profiles-preview/index.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md b/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md index 9df11d37b..7980ad61d 100644 --- a/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md +++ b/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md @@ -6,7 +6,7 @@ authors: [colin-mixon] tags: [ai, performance, scheduler, best-practices, cost] --- -Thoughtful scheduling strategies can resolve pervasive challenges like resource utilization. The default scheduler was primarily designed for general-purpose workloads and out-of-box pod scheduling that can be restrictive if you want to bin pack nodes since the hard and soft constraints for pod scheduling do not align with scheudling pods with nodes of higher utilization. The scheduler selects the optimal node for queued pod(s) based on several constraints, including (but not limited to): +Thoughtful scheduling strategies can resolve pervasive challenges like resource utilization. The kube-scheduler is one of the core components of the Kubernetes Control Plane, alongside kube-apiserver, etcd, and the controller manager. The default scheduler was primarily designed for general-purpose workloads and out-of-box pod scheduling that can be restrictive if you want to bin pack nodes since the hard and soft constraints for pod scheduling do not align with scheudling pods with nodes of higher utilization. The scheduler selects the optimal node for queued pod(s) based on several constraints, including (but not limited to): 1. Resource requirements (CPU, memory) 2. 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Today, the AKS default scheduler lacks the flexibility for users to change which criteria should be prioritized, and ignored, in the scheduling cycle on a per workload basis. This means the default scheduling criteria, and their fixed priority order, are not suitable for workloads that demand co-locating pods with their persistent volumes for increased data locality, optimizing GPU utilization for machine learning, or enforcing strict zone-level distribution for microservices. This rigidity often forces users to either accept suboptimal placement or manage a separate custom scheduler, both of which increase operational complexity. +![Kube Scheduler Cycles Diagram](KUBE_SCHEDULER_SCHEDULING_CYCLES_DIAGRAM.png) + **[AKS Configurable Scheduler Profiles][concepts-scheduler-configuration] reduces operational complexity by providing extensibility and control.** Now, customers can define their own scheduling logic by selecting specific policies, altering parameter weight, changing policy priority, adding additional policy parameters, and changing policy evaluation point (i.e. PreFilter, Filter, Score) without deploying a second scheduler. On AKS, customers have mentioned that AKS Configurable Scheduler Profiles allows them to increase resiliency without operational overhead of YAML wrangling or reduce cluster costs without adopting a secondary scheduler. Additionally, our AI and HPC customers have batch workloads that have benefitted from improved bin-packing and increased GPU utilization. In this blog you will learn how to configure AKS Configurable Scheduler Profiles for three increased node utilization: From 50edecd975cfdca9f7d0078ee52b984916f1fabd Mon Sep 17 00:00:00 2001 From: colinmixonn <109253437+colinmixonn@users.noreply.github.com> Date: Thu, 19 Mar 2026 15:40:18 -0700 Subject: [PATCH 054/164] Apply suggestions from code review Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> --- .../index.md | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md b/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md index f53131326..bdd90f96e 100644 --- a/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md +++ b/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md @@ -15,7 +15,7 @@ Thoughtful scheduling strategies can resolve pervasive challenges like resource Out of the available nodes, the scheduler then filters out nodes that don't meet the requirements to identify the node that is most optimal for the pod(s). Today, the AKS default scheduler lacks the flexibility for users to change which criteria should be prioritized, and ignored, in the scheduling cycle on a per workload basis. This means the default scheduling criteria, and their fixed priority order, are not suitable for workloads that demand co-locating pods with their persistent volumes for increased data locality, optimizing GPU utilization for machine learning, or enforcing strict zone-level distribution for microservices. This rigidity often forces users to either accept suboptimal placement or manage a separate custom scheduler, both of which increase operational complexity. -![Kube Scheduler Cycles Diagram](KUBE_SCHEDULER_SCHEDULING_CYCLES_DIAGRAM.png) +![Kube Scheduler Cycles Diagram](./kube-scheduler-scheduling-cycles-diagram.png) **[AKS Configurable Scheduler Profiles][concepts-scheduler-configuration] reduces operational complexity by providing extensibility and control.** Now, customers can define their own scheduling logic by selecting specific policies, altering parameter weight, changing policy priority, adding additional policy parameters, and changing policy evaluation point (i.e. PreFilter, Filter, Score) without deploying a second scheduler. On AKS, customers have mentioned that AKS Configurable Scheduler Profiles allows them to increase resiliency without operational overhead of YAML wrangling or reduce cluster costs without adopting a secondary scheduler. Additionally, our AI and HPC customers have batch workloads that have benefitted from improved bin-packing and increased GPU utilization. @@ -32,7 +32,7 @@ Lastly, you will find [best practices](#best-practices-and-configuration-conside AKS Configurable Scheduler Profiles uses a Custom Resource Definition (CRD) that lets users define custom scheduler profiles. A dedicated controller continuously reconciles these user-defined configurations with the underlying kube-scheduler deployment, validating changes and applying them transparently. If a configuration causes the scheduler to become unhealthy, the controller automatically rolls back to the last known good state to ensure cluster stability. -![Configurable Scheduler Profiles Diagram](CONFIG_SCHEDULER_PROFILES.png) +![Configurable Scheduler Profiles Diagram](./config-scheduler-profiles.png) A scheduler profile is a set of one or more in-tree scheduling plugins and configurations that dictate how to schedule a pod. Previously, the scheduler configuration wasn't accessible to users. Starting from Kubernetes version 1.33, you can now configure and set a scheduler profile for the AKS scheduler on your cluster. AKS supports 18 in-tree Kubernetes [scheduling plugins][supported-in-tree-scheduling-plugins]. The plugins can be generally grouped into three categories: @@ -43,14 +43,14 @@ A scheduler profile is a set of one or more in-tree scheduling plugins and confi Below you will find example configurations for common workload objectives. :::note -Adjust VM SKUs in `NodeAffinity`, shift utilization curves or weights, and use the right zones for your cluster(s) in the configurations below. +Treat these examples as starting points. Adjust resource weights, utilization thresholds, and plugin parameters to match your VM SKUs, workload patterns, and cluster topology. ::: ### Increase GPU Utilization by Bin Packing GPU-backed Nodes The AKS default scheduler scores nodes for workload placement based on a _LeastAllocated_ strategy, to spread across the nodes in a cluster. However, this behavior can result in inefficient resource utilization, as nodes with higher allocation are not favored. You can use `NodeResourcesFit` to control how pods are assigned to nodes based on available resources (CPU, GPU, memory, etc.), including favoring nodes with high resource utilization, within the set configuration. -For example, scheduling pending jobs on nodes with a higher relative GPU utilization, users can reduce costs and increase GPU Utilization while maintaining performance. +For example, scheduling pending jobs on nodes with a higher relative GPU utilization, users can reduce costs and increase GPU utilization while maintaining performance. **This scheduler configuration maximizes GPU efficiency for larger batch jobs by consolidating smaller jobs onto fewer nodes and lowering the operational cost of underutilized resources without sacrificing performance.** From 110c48ab3ad8f3782ffa2538bd7a33fedc296047 Mon Sep 17 00:00:00 2001 From: colinmixonn <109253437+colinmixonn@users.noreply.github.com> Date: Thu, 19 Mar 2026 15:44:27 -0700 Subject: [PATCH 055/164] Add cost management tag to blog tags Added a new tag for cost management in AKS clusters. --- website/blog/tags.yml | 6 ++++++ 1 file changed, 6 insertions(+) diff --git a/website/blog/tags.yml b/website/blog/tags.yml index 0bf18cb2a..5e260c9b7 100644 --- a/website/blog/tags.yml +++ b/website/blog/tags.yml @@ -82,6 +82,11 @@ certbot: permalink: /certbot description: Using Certbot and Let’s Encrypt to obtain and manage TLS certificates for AKS ingress. +cost: + label: Cost + permalink: /cost + description: Recommended patterns, anti-patterns, and guidance for managing and optimizing cost of AKS clusters. + cilium: label: Cilium permalink: /cilium @@ -276,6 +281,7 @@ scheduler: label: Scheduler permalink: /scheduler description: Techniques and capabilities to control pod placement, improve resource efficiency, and tune scheduling for diverse AKS workloads. + security: label: Security permalink: /security From c28e170685568b658cadcfd5e476adb09149170b Mon Sep 17 00:00:00 2001 From: colinmixonn <109253437+colinmixonn@users.noreply.github.com> Date: Thu, 19 Mar 2026 16:22:59 -0700 Subject: [PATCH 056/164] Revise blog on AKS Configurable Scheduler Profiles Updated the blog post to improve clarity and accuracy regarding AKS Configurable Scheduler Profiles, including changes to the descriptions of scheduling logic, configuration examples, and next steps for optimization. --- .../index.md | 40 +++++++------------ 1 file changed, 15 insertions(+), 25 deletions(-) diff --git a/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md b/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md index bdd90f96e..e34d94b16 100644 --- a/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md +++ b/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md @@ -17,15 +17,13 @@ Out of the available nodes, the scheduler then filters out nodes that don't meet ![Kube Scheduler Cycles Diagram](./kube-scheduler-scheduling-cycles-diagram.png) -**[AKS Configurable Scheduler Profiles][concepts-scheduler-configuration] reduces operational complexity by providing extensibility and control.** Now, customers can define their own scheduling logic by selecting specific policies, altering parameter weight, changing policy priority, adding additional policy parameters, and changing policy evaluation point (i.e. PreFilter, Filter, Score) without deploying a second scheduler. On AKS, customers have mentioned that AKS Configurable Scheduler Profiles allows them to increase resiliency without operational overhead of YAML wrangling or reduce cluster costs without adopting a secondary scheduler. Additionally, our AI and HPC customers have batch workloads that have benefitted from improved bin-packing and increased GPU utilization. +**[Configurable Scheduler Profiles on AKS][concepts-scheduler-configuration] reduces operational complexity by providing extensibility and control.** Now, customers can define their own scheduling logic by enabling specific policies, changing policy priority, altering parameter weight, and changing policy evaluation point (i.e. PreFilter, Filter, Score) without deploying a second scheduler. On AKS, Configurable Scheduler Profiles allows customers to increase resiliency without operational overhead of YAML wrangling or reduce cluster costs without adopting a secondary scheduler. In this blog you will learn how to configure AKS Configurable Scheduler Profiles for three increased node utilization: 1. [How to increase CPU utilization](#increase-gpu-utilization-by-bin-packing-gpu-backed-nodes) 2. [How to increase GPU utilization](#increase-gpu-utilization-by-bin-packing-gpu-backed-nodes) -Lastly, you will find [best practices](#best-practices-and-configuration-considerations) to help guide how you consider both individual plugin configurations, your custom scheduler configuration, and your Deployment design holistically. - ## AKS Configurable Scheduler Profiles @@ -34,25 +32,23 @@ AKS Configurable Scheduler Profiles uses a Custom Resource Definition (CRD) that ![Configurable Scheduler Profiles Diagram](./config-scheduler-profiles.png) -A scheduler profile is a set of one or more in-tree scheduling plugins and configurations that dictate how to schedule a pod. Previously, the scheduler configuration wasn't accessible to users. Starting from Kubernetes version 1.33, you can now configure and set a scheduler profile for the AKS scheduler on your cluster. AKS supports 18 in-tree Kubernetes [scheduling plugins][supported-in-tree-scheduling-plugins]. The plugins can be generally grouped into three categories: +A profile is a set of one or more in-tree scheduling plugins and configurations that dictate how to schedule a pod. Previously, the scheduler configuration wasn't accessible to users. Starting from Kubernetes version 1.33, you can now configure and set a scheduler profile for your AKS cluster. AKS supports 18 in-tree Kubernetes [scheduling plugins][supported-in-tree-scheduling-plugins], which can be generally grouped into three categories: 1. Scheduling constraints and order-based plugins 2. Node selection constraints scheduling plugins 3. Resource and topology optimization scheduling plugins -Below you will find example configurations for common workload objectives. - :::note Treat these examples as starting points. Adjust resource weights, utilization thresholds, and plugin parameters to match your VM SKUs, workload patterns, and cluster topology. ::: -### Increase GPU Utilization by Bin Packing GPU-backed Nodes +### Increase GPU Utilization of AKS Clusters The AKS default scheduler scores nodes for workload placement based on a _LeastAllocated_ strategy, to spread across the nodes in a cluster. However, this behavior can result in inefficient resource utilization, as nodes with higher allocation are not favored. You can use `NodeResourcesFit` to control how pods are assigned to nodes based on available resources (CPU, GPU, memory, etc.), including favoring nodes with high resource utilization, within the set configuration. -For example, scheduling pending jobs on nodes with a higher relative GPU utilization, users can reduce costs and increase GPU utilization while maintaining performance. +Additionally, customers running GPU-dependent applications like batch jobs will benefit from improved bin-packing and increased GPU utilization. For example, scheduling jobs on nodes with a higher relative GPU utilization, can reduce costs while maintaining performance. -**This scheduler configuration maximizes GPU efficiency for larger batch jobs by consolidating smaller jobs onto fewer nodes and lowering the operational cost of underutilized resources without sacrificing performance.** +**This scheduler configuration maximizes provisioined GPU resource by consolidating smaller jobs onto fewer nodes, lowering the operational cost of underutilized resources without sacrificing performance.** ```yaml apiVersion: aks.azure.com/v1alpha1 @@ -80,19 +76,14 @@ spec: - name: cpu weight: 1 - name: nvidia.com/gpu - weight: 3 - - name: NodeResourcesBalancedAllocation - args: - resources: - - name: nvidia.com/gpu - weight: 1 + weight: 5 ``` -### ResourceToCapacity +### Increase CPU Utilizaiton of AKS Cluster - -**This scheduler configuration ensures workloads needing large memory footprints are placed on nodes that provide sufficient RAM and maintain proximity to their volumes, enabling fast, zone‑aligned PVC binding for optimal data locality.** +Scoring Strategy - ResourceToCapacity +**This scheduler configuration ensures nodes are not oversaturated.** ```yaml apiVersion: aks.azure.com/v1alpha1 @@ -117,11 +108,9 @@ spec: scoringStrategy: type: RequestedToCapacityRatio resources: - - name: memory - weight: 5 - name: cpu - weight: 1 - - name: ephemeral-storage + weight: 8 + - name: mempory weight: 1 requestedToCapacityRatio: shape: @@ -139,12 +128,12 @@ spec: score: 0 ``` -## Next Steps: Optimize and test with AKS Configurable Scheduler Profiles +## Next Steps: Optimize resources and test Configurable Scheduler Profiles on AKS -With AKS Configurable Scheduler Profiles, teams gain fine-grained control over pod placement strategies like bin-packing, topology distribution, and resource-based scoring that directly address the challenges of resilience and resource utilization for web-distributed workloads and AI workloads. By leveraging these advanced scheduling plugins, AKS users can ensure their workloads make full use of available GPU capacity, reduce idle time, and avoid costly overprovisioning. This not only improves ROI but also accelerates innovation by allowing more jobs to run concurrently and reliably. +With Configurable Scheduler Profiles, teams gain fine-grained control over pod placement strategies like bin-packing, topology distribution, and resource-based scoring that directly addresses challenges related to applicaiton resilience and resource utilization for their AKS clusters. By leveraging these scheduling plugins, AKS users can ensure their workloads make full use of available GPU capacity, reduce idle costs, and avoid costly overprovisioning. This not only improves ROI but also accelerates development by allowing more jobs to run concurrently and reliably. - For best practices using the kube-scheduler visit [kube-scheduler best practices][best-practices-advanced-scheduler] -- Configure your workload specific scheduler using the [AKS Configurable Scheduler][concepts-scheduler-configuration] +- Increase node utilziaton using [Configurable Scheduler Profiles][node-bin-packing-configurations] - If additional capabilities or ML frameworks are needed to schedule and queue batch workloads, you can [install and configure Kueue on AKS][kueue-overview] to ensure efficient, policy-driven scheduling in AKS clusters. [concepts-scheduler-configuration]: https://learn.microsoft.com/azure/aks/concepts-scheduler-configuration @@ -153,3 +142,4 @@ With AKS Configurable Scheduler Profiles, teams gain fine-grained control over p [scheduling-framework/#interfaces]: https://kubernetes.io/docs/concepts/scheduling-eviction/scheduling-framework/#interfaces [memory-optimized-vm]: https://learn.microsoft.com/azure/virtual-machines/sizes/overview?tabs=breakdownseries%2Cgeneralsizelist%2Ccomputesizelist%2Cmemorysizelist%2Cstoragesizelist%2Cgpusizelist%2Cfpgasizelist%2Chpcsizelist#memory-optimized [supported-in-tree-scheduling-plugins]: https://learn.microsoft.com/azure/aks/concepts-scheduler-configuration#supported-in-tree-scheduling-plugins +[node-bin-packing-configurations]: https://learn.microsoft.com/azure/aks/configure-node-binpack-scheduler?tabs=new-cluster From 70338254e35ca8517bb98e0b95e6de37edce2b56 Mon Sep 17 00:00:00 2001 From: colinmixonn <109253437+colinmixonn@users.noreply.github.com> Date: Thu, 19 Mar 2026 16:49:49 -0700 Subject: [PATCH 057/164] Revise AKS Configurable Scheduler Profiles blog content Updated blog post to clarify examples of scheduler profiles and their benefits for AKS Cluster utilization. --- .../index.md | 14 +++++++------- 1 file changed, 7 insertions(+), 7 deletions(-) diff --git a/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md b/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md index e34d94b16..565e376aa 100644 --- a/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md +++ b/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md @@ -15,14 +15,14 @@ Thoughtful scheduling strategies can resolve pervasive challenges like resource Out of the available nodes, the scheduler then filters out nodes that don't meet the requirements to identify the node that is most optimal for the pod(s). Today, the AKS default scheduler lacks the flexibility for users to change which criteria should be prioritized, and ignored, in the scheduling cycle on a per workload basis. This means the default scheduling criteria, and their fixed priority order, are not suitable for workloads that demand co-locating pods with their persistent volumes for increased data locality, optimizing GPU utilization for machine learning, or enforcing strict zone-level distribution for microservices. This rigidity often forces users to either accept suboptimal placement or manage a separate custom scheduler, both of which increase operational complexity. -![Kube Scheduler Cycles Diagram](./kube-scheduler-scheduling-cycles-diagram.png) +![kube-scheduler Phases Diagram](./kube-scheduler-scheduling-cycles-diagram.png) **[Configurable Scheduler Profiles on AKS][concepts-scheduler-configuration] reduces operational complexity by providing extensibility and control.** Now, customers can define their own scheduling logic by enabling specific policies, changing policy priority, altering parameter weight, and changing policy evaluation point (i.e. PreFilter, Filter, Score) without deploying a second scheduler. On AKS, Configurable Scheduler Profiles allows customers to increase resiliency without operational overhead of YAML wrangling or reduce cluster costs without adopting a secondary scheduler. -In this blog you will learn how to configure AKS Configurable Scheduler Profiles for three increased node utilization: +This blog you will provide examples of three different scheduler profiles, and detail the benefits of each, to increase the node utilization for AKS Clusters: -1. [How to increase CPU utilization](#increase-gpu-utilization-by-bin-packing-gpu-backed-nodes) -2. [How to increase GPU utilization](#increase-gpu-utilization-by-bin-packing-gpu-backed-nodes) +1. [How to increase AKS Cluster GPU utilization](#increase-aks-cluster-gpu-utilization) +2. [How to increase AKS Cluster CPU utilization](#increase-aks-cluster-cpu-utilization) @@ -42,7 +42,7 @@ A profile is a set of one or more in-tree scheduling plugins and configurations Treat these examples as starting points. Adjust resource weights, utilization thresholds, and plugin parameters to match your VM SKUs, workload patterns, and cluster topology. ::: -### Increase GPU Utilization of AKS Clusters +### Increase AKS Cluster GPU Utilization The AKS default scheduler scores nodes for workload placement based on a _LeastAllocated_ strategy, to spread across the nodes in a cluster. However, this behavior can result in inefficient resource utilization, as nodes with higher allocation are not favored. You can use `NodeResourcesFit` to control how pods are assigned to nodes based on available resources (CPU, GPU, memory, etc.), including favoring nodes with high resource utilization, within the set configuration. @@ -80,7 +80,7 @@ spec: ``` -### Increase CPU Utilizaiton of AKS Cluster +### Increase AKS Cluster CPU Utilization Scoring Strategy - ResourceToCapacity **This scheduler configuration ensures nodes are not oversaturated.** @@ -128,7 +128,7 @@ spec: score: 0 ``` -## Next Steps: Optimize resources and test Configurable Scheduler Profiles on AKS +## Next Steps: Optimize Azure resources and test Configurable Scheduler Profiles on AKS With Configurable Scheduler Profiles, teams gain fine-grained control over pod placement strategies like bin-packing, topology distribution, and resource-based scoring that directly addresses challenges related to applicaiton resilience and resource utilization for their AKS clusters. By leveraging these scheduling plugins, AKS users can ensure their workloads make full use of available GPU capacity, reduce idle costs, and avoid costly overprovisioning. This not only improves ROI but also accelerates development by allowing more jobs to run concurrently and reliably. From 506616ff574b32985f08a76fd05a3a15228d83f7 Mon Sep 17 00:00:00 2001 From: colinmixonn <109253437+colinmixonn@users.noreply.github.com> Date: Thu, 19 Mar 2026 16:51:20 -0700 Subject: [PATCH 058/164] Reorder cost tag in tags.yml --- website/blog/tags.yml | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/website/blog/tags.yml b/website/blog/tags.yml index 5e260c9b7..5409eb1b0 100644 --- a/website/blog/tags.yml +++ b/website/blog/tags.yml @@ -82,16 +82,16 @@ certbot: permalink: /certbot description: Using Certbot and Let’s Encrypt to obtain and manage TLS certificates for AKS ingress. -cost: - label: Cost - permalink: /cost - description: Recommended patterns, anti-patterns, and guidance for managing and optimizing cost of AKS clusters. - cilium: label: Cilium permalink: /cilium description: Cilium CNI, eBPF networking, observability, and security features on AKS. +cost: + label: Cost + permalink: /cost + description: Recommended patterns, anti-patterns, and guidance for managing and optimizing cost of AKS clusters. + databases: label: Databases permalink: /databases From 88b49aa08c388520638cd016fa22b868222abf65 Mon Sep 17 00:00:00 2001 From: colinmixonn <109253437+colinmixonn@users.noreply.github.com> Date: Thu, 19 Mar 2026 16:54:49 -0700 Subject: [PATCH 059/164] Apply suggestions from code review Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> --- .../index.md | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md b/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md index 565e376aa..ce172b747 100644 --- a/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md +++ b/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md @@ -48,7 +48,7 @@ The AKS default scheduler scores nodes for workload placement based on a _LeastA Additionally, customers running GPU-dependent applications like batch jobs will benefit from improved bin-packing and increased GPU utilization. For example, scheduling jobs on nodes with a higher relative GPU utilization, can reduce costs while maintaining performance. -**This scheduler configuration maximizes provisioined GPU resource by consolidating smaller jobs onto fewer nodes, lowering the operational cost of underutilized resources without sacrificing performance.** +**This scheduler configuration maximizes provisioned GPU resource by consolidating smaller jobs onto fewer nodes, lowering the operational cost of underutilized resources without sacrificing performance.** ```yaml apiVersion: aks.azure.com/v1alpha1 @@ -110,7 +110,7 @@ spec: resources: - name: cpu weight: 8 - - name: mempory + - name: memory weight: 1 requestedToCapacityRatio: shape: @@ -130,10 +130,10 @@ spec: ## Next Steps: Optimize Azure resources and test Configurable Scheduler Profiles on AKS -With Configurable Scheduler Profiles, teams gain fine-grained control over pod placement strategies like bin-packing, topology distribution, and resource-based scoring that directly addresses challenges related to applicaiton resilience and resource utilization for their AKS clusters. By leveraging these scheduling plugins, AKS users can ensure their workloads make full use of available GPU capacity, reduce idle costs, and avoid costly overprovisioning. This not only improves ROI but also accelerates development by allowing more jobs to run concurrently and reliably. +With Configurable Scheduler Profiles, teams gain fine-grained control over pod placement strategies like bin-packing, topology distribution, and resource-based scoring that directly addresses challenges related to application resilience and resource utilization for their AKS clusters. By leveraging these scheduling plugins, AKS users can ensure their workloads make full use of available GPU capacity, reduce idle costs, and avoid costly overprovisioning. This not only improves ROI but also accelerates development by allowing more jobs to run concurrently and reliably. - For best practices using the kube-scheduler visit [kube-scheduler best practices][best-practices-advanced-scheduler] -- Increase node utilziaton using [Configurable Scheduler Profiles][node-bin-packing-configurations] +- Increase node utilization using [Configurable Scheduler Profiles][node-bin-packing-configurations] - If additional capabilities or ML frameworks are needed to schedule and queue batch workloads, you can [install and configure Kueue on AKS][kueue-overview] to ensure efficient, policy-driven scheduling in AKS clusters. [concepts-scheduler-configuration]: https://learn.microsoft.com/azure/aks/concepts-scheduler-configuration From 9fb4796d27f5ab1795f35f3fb50bc1a5065c120f Mon Sep 17 00:00:00 2001 From: colinmixonn <109253437+colinmixonn@users.noreply.github.com> Date: Fri, 20 Mar 2026 11:28:24 -0700 Subject: [PATCH 060/164] Enhance AKS Configurable Scheduler Profiles documentation Added information about Configurable Scheduler Profiles and their benefits for resource utilization in AKS. Clarified how the kube-scheduler operates and its impact on CPU and GPU resource management. --- .../index.md | 12 +++++++----- 1 file changed, 7 insertions(+), 5 deletions(-) diff --git a/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md b/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md index ce172b747..bc1e53921 100644 --- a/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md +++ b/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md @@ -6,6 +6,12 @@ authors: [colin-mixon] tags: [ai, performance, scheduler, best-practices, cost] --- +Data shows most Kuberentes clusters only use an average of 10% cpu utilization. With the introduction of Configurable Scheduler Profiles on AKS, customers now have the opportunity to increase their node utilization across CPU and GPU resources and optimize their costs. + + +## How does the kube-scheduler Place Pods? +The AKS default scheduler scores nodes for workload placement based on a _LeastAllocated_ strategy, to spread across the nodes in a cluster. However, this behavior can result in inefficient resource utilization, as nodes with higher allocation are not favored. You can use `NodeResourcesFit` to control how pods are assigned to nodes based on available resources (CPU, GPU, memory, etc.), including favoring nodes with high resource utilization, within the set configuration. + Thoughtful scheduling strategies can resolve pervasive challenges like resource utilization. The kube-scheduler is one of the core components of the Kubernetes Control Plane, alongside kube-apiserver, etcd, and the controller manager. The default scheduler was primarily designed for general-purpose workloads and out-of-box pod scheduling that can be restrictive if you want to bin pack nodes since the hard and soft constraints for pod scheduling do not align with scheudling pods with nodes of higher utilization. The scheduler selects the optimal node for queued pod(s) based on several constraints, including (but not limited to): 1. Resource requirements (CPU, memory) @@ -24,11 +30,9 @@ This blog you will provide examples of three different scheduler profiles, and d 1. [How to increase AKS Cluster GPU utilization](#increase-aks-cluster-gpu-utilization) 2. [How to increase AKS Cluster CPU utilization](#increase-aks-cluster-cpu-utilization) - - ## AKS Configurable Scheduler Profiles -AKS Configurable Scheduler Profiles uses a Custom Resource Definition (CRD) that lets users define custom scheduler profiles. A dedicated controller continuously reconciles these user-defined configurations with the underlying kube-scheduler deployment, validating changes and applying them transparently. If a configuration causes the scheduler to become unhealthy, the controller automatically rolls back to the last known good state to ensure cluster stability. +Configurable Scheduler Profiles uses a Custom Resource Definition (CRD) that lets users define custom scheduler profiles. A dedicated controller continuously reconciles these user-defined configurations with the underlying kube-scheduler deployment, validating changes and applying them transparently. If a configuration causes the scheduler to become unhealthy, the controller automatically rolls back to the last known good state to ensure cluster stability. ![Configurable Scheduler Profiles Diagram](./config-scheduler-profiles.png) @@ -44,8 +48,6 @@ Treat these examples as starting points. Adjust resource weights, utilization th ### Increase AKS Cluster GPU Utilization -The AKS default scheduler scores nodes for workload placement based on a _LeastAllocated_ strategy, to spread across the nodes in a cluster. However, this behavior can result in inefficient resource utilization, as nodes with higher allocation are not favored. You can use `NodeResourcesFit` to control how pods are assigned to nodes based on available resources (CPU, GPU, memory, etc.), including favoring nodes with high resource utilization, within the set configuration. - Additionally, customers running GPU-dependent applications like batch jobs will benefit from improved bin-packing and increased GPU utilization. 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With the introduction of Configurable Scheduler Profiles on AKS, customers now have the opportunity to increase their node utilization across CPU and GPU resources and optimize their costs. + +![Increase Node Utilization with Node Bin Pack ](./configurable-scheduler-binpack-profile.png) + ## How does the kube-scheduler Place Pods? The AKS default scheduler scores nodes for workload placement based on a _LeastAllocated_ strategy, to spread across the nodes in a cluster. However, this behavior can result in inefficient resource utilization, as nodes with higher allocation are not favored. You can use `NodeResourcesFit` to control how pods are assigned to nodes based on available resources (CPU, GPU, memory, etc.), including favoring nodes with high resource utilization, within the set configuration. @@ -21,7 +24,7 @@ Thoughtful scheduling strategies can resolve pervasive challenges like resource Out of the available nodes, the scheduler then filters out nodes that don't meet the requirements to identify the node that is most optimal for the pod(s). Today, the AKS default scheduler lacks the flexibility for users to change which criteria should be prioritized, and ignored, in the scheduling cycle on a per workload basis. This means the default scheduling criteria, and their fixed priority order, are not suitable for workloads that demand co-locating pods with their persistent volumes for increased data locality, optimizing GPU utilization for machine learning, or enforcing strict zone-level distribution for microservices. This rigidity often forces users to either accept suboptimal placement or manage a separate custom scheduler, both of which increase operational complexity. -![kube-scheduler Phases Diagram](./kube-scheduler-scheduling-cycles-diagram.png) +![kube-scheduler Phases Diagram](./kube-scheduler-scheduling-phases-diagram.png) **[Configurable Scheduler Profiles on AKS][concepts-scheduler-configuration] reduces operational complexity by providing extensibility and control.** Now, customers can define their own scheduling logic by enabling specific policies, changing policy priority, altering parameter weight, and changing policy evaluation point (i.e. PreFilter, Filter, Score) without deploying a second scheduler. On AKS, Configurable Scheduler Profiles allows customers to increase resiliency without operational overhead of YAML wrangling or reduce cluster costs without adopting a secondary scheduler. From 38e7f635f3d6516cb744e9fb2b184feb2a3f0597 Mon Sep 17 00:00:00 2001 From: colinmixonn <109253437+colinmixonn@users.noreply.github.com> Date: Fri, 20 Mar 2026 11:38:26 -0700 Subject: [PATCH 064/164] Apply suggestions from code review Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> --- .../index.md | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md b/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md index 8cbc8738f..563ef24c5 100644 --- a/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md +++ b/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md @@ -6,7 +6,7 @@ authors: [colin-mixon] tags: [ai, performance, scheduler, best-practices, cost] --- -Data shows most Kuberentes clusters only use an average of 10% cpu utilization. With the introduction of Configurable Scheduler Profiles on AKS, customers now have the opportunity to increase their node utilization across CPU and GPU resources and optimize their costs. +Data shows most Kubernetes clusters only use an average of 10% cpu utilization. With the introduction of Configurable Scheduler Profiles on AKS, customers now have the opportunity to increase their node utilization across CPU and GPU resources and optimize their costs. @@ -15,7 +15,7 @@ Data shows most Kuberentes clusters only use an average of 10% cpu utilization. ## How does the kube-scheduler Place Pods? The AKS default scheduler scores nodes for workload placement based on a _LeastAllocated_ strategy, to spread across the nodes in a cluster. However, this behavior can result in inefficient resource utilization, as nodes with higher allocation are not favored. You can use `NodeResourcesFit` to control how pods are assigned to nodes based on available resources (CPU, GPU, memory, etc.), including favoring nodes with high resource utilization, within the set configuration. -Thoughtful scheduling strategies can resolve pervasive challenges like resource utilization. The kube-scheduler is one of the core components of the Kubernetes Control Plane, alongside kube-apiserver, etcd, and the controller manager. The default scheduler was primarily designed for general-purpose workloads and out-of-box pod scheduling that can be restrictive if you want to bin pack nodes since the hard and soft constraints for pod scheduling do not align with scheudling pods with nodes of higher utilization. The scheduler selects the optimal node for queued pod(s) based on several constraints, including (but not limited to): +Thoughtful scheduling strategies can resolve pervasive challenges like resource utilization. The kube-scheduler is one of the core components of the Kubernetes Control Plane, alongside kube-apiserver, etcd, and the controller manager. The default scheduler was primarily designed for general-purpose workloads and out-of-box pod scheduling that can be restrictive if you want to bin pack nodes since the hard and soft constraints for pod scheduling do not align with scheduling pods with nodes of higher utilization. The scheduler selects the optimal node for queued pod(s) based on several constraints, including (but not limited to): 1. Resource requirements (CPU, memory) 2. Node affinity/anti-affinity @@ -28,10 +28,10 @@ Out of the available nodes, the scheduler then filters out nodes that don't meet **[Configurable Scheduler Profiles on AKS][concepts-scheduler-configuration] reduces operational complexity by providing extensibility and control.** Now, customers can define their own scheduling logic by enabling specific policies, changing policy priority, altering parameter weight, and changing policy evaluation point (i.e. PreFilter, Filter, Score) without deploying a second scheduler. On AKS, Configurable Scheduler Profiles allows customers to increase resiliency without operational overhead of YAML wrangling or reduce cluster costs without adopting a secondary scheduler. -This blog you will provide examples of three different scheduler profiles, and detail the benefits of each, to increase the node utilization for AKS Clusters: +This blog provides examples of three different scheduler profiles and details the benefits of each to increase node utilization for AKS clusters: -1. [How to increase AKS Cluster GPU utilization](#increase-aks-cluster-gpu-utilization) -2. [How to increase AKS Cluster CPU utilization](#increase-aks-cluster-cpu-utilization) +1. [How to increase AKS cluster GPU utilization](#increase-aks-cluster-gpu-utilization) +2. [How to increase AKS cluster CPU utilization](#increase-aks-cluster-cpu-utilization) ## AKS Configurable Scheduler Profiles From 33e25442b562fc3dc3498714842b7e341da94e6f Mon Sep 17 00:00:00 2001 From: colinmixonn <109253437+colinmixonn@users.noreply.github.com> Date: Fri, 20 Mar 2026 11:39:52 -0700 Subject: [PATCH 065/164] Update section titles and improve clarity --- .../2025-12-16-aks-config-scheduler-profiles-preview/index.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md b/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md index 563ef24c5..b114f6e64 100644 --- a/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md +++ b/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md @@ -12,7 +12,7 @@ Data shows most Kubernetes clusters only use an average of 10% cpu utilization. ![Increase Node Utilization with Node Bin Pack ](./configurable-scheduler-binpack-profile.png) -## How does the kube-scheduler Place Pods? +## How does the default Kubernetes scheduler place pods? The AKS default scheduler scores nodes for workload placement based on a _LeastAllocated_ strategy, to spread across the nodes in a cluster. However, this behavior can result in inefficient resource utilization, as nodes with higher allocation are not favored. You can use `NodeResourcesFit` to control how pods are assigned to nodes based on available resources (CPU, GPU, memory, etc.), including favoring nodes with high resource utilization, within the set configuration. Thoughtful scheduling strategies can resolve pervasive challenges like resource utilization. The kube-scheduler is one of the core components of the Kubernetes Control Plane, alongside kube-apiserver, etcd, and the controller manager. The default scheduler was primarily designed for general-purpose workloads and out-of-box pod scheduling that can be restrictive if you want to bin pack nodes since the hard and soft constraints for pod scheduling do not align with scheduling pods with nodes of higher utilization. The scheduler selects the optimal node for queued pod(s) based on several constraints, including (but not limited to): @@ -33,7 +33,7 @@ This blog provides examples of three different scheduler profiles and details th 1. [How to increase AKS cluster GPU utilization](#increase-aks-cluster-gpu-utilization) 2. [How to increase AKS cluster CPU utilization](#increase-aks-cluster-cpu-utilization) -## AKS Configurable Scheduler Profiles +## Configurable Scheduler Profiles on AKS Configurable Scheduler Profiles uses a Custom Resource Definition (CRD) that lets users define custom scheduler profiles. A dedicated controller continuously reconciles these user-defined configurations with the underlying kube-scheduler deployment, validating changes and applying them transparently. If a configuration causes the scheduler to become unhealthy, the controller automatically rolls back to the last known good state to ensure cluster stability. From 85c09306adc0e7869696be52eec077fc0bbf256c Mon Sep 17 00:00:00 2001 From: colinmixonn <109253437+colinmixonn@users.noreply.github.com> Date: Fri, 20 Mar 2026 11:43:44 -0700 Subject: [PATCH 066/164] Apply suggestions from code review Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> --- .../index.md | 5 +---- 1 file changed, 1 insertion(+), 4 deletions(-) diff --git a/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md b/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md index b114f6e64..8146ff31e 100644 --- a/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md +++ b/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md @@ -83,10 +83,7 @@ spec: - name: nvidia.com/gpu weight: 5 ``` - - -### Increase AKS Cluster CPU Utilization - +### ResourceToCapacity Scoring Strategy - ResourceToCapacity **This scheduler configuration ensures nodes are not oversaturated.** From be0f84ade906d189be908ad5d4cc2d15d1616a82 Mon Sep 17 00:00:00 2001 From: colinmixonn <109253437+colinmixonn@users.noreply.github.com> Date: Fri, 20 Mar 2026 11:44:43 -0700 Subject: [PATCH 067/164] Update website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> --- .../2025-12-16-aks-config-scheduler-profiles-preview/index.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md b/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md index 8146ff31e..cccb7c374 100644 --- a/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md +++ b/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md @@ -10,7 +10,7 @@ Data shows most Kubernetes clusters only use an average of 10% cpu utilization. -![Increase Node Utilization with Node Bin Pack ](./configurable-scheduler-binpack-profile.png) +![Diagram showing increased node utilization with the node bin packing scheduler profile](./configurable-scheduler-binpack-profile.png) ## How does the default Kubernetes scheduler place pods? The AKS default scheduler scores nodes for workload placement based on a _LeastAllocated_ strategy, to spread across the nodes in a cluster. However, this behavior can result in inefficient resource utilization, as nodes with higher allocation are not favored. You can use `NodeResourcesFit` to control how pods are assigned to nodes based on available resources (CPU, GPU, memory, etc.), including favoring nodes with high resource utilization, within the set configuration. From 02141bd6f8dd2171a52369ea3e53cacef6b1c176 Mon Sep 17 00:00:00 2001 From: colinmixonn <109253437+colinmixonn@users.noreply.github.com> Date: Fri, 20 Mar 2026 12:00:30 -0700 Subject: [PATCH 068/164] Revise title and description for AKS scheduler profiles Updated the title and description to clarify the benefits of configurable scheduler profiles for both GPU and CPU utilization in AKS. Added a new section on increasing AKS cluster CPU utilization. --- .../index.md | 10 ++++++---- 1 file changed, 6 insertions(+), 4 deletions(-) diff --git a/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md b/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md index cccb7c374..fc89cbd3f 100644 --- a/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md +++ b/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md @@ -1,6 +1,6 @@ --- -title: "AKS configurable scheduler profiles (preview)" -description: "Optimize AKS scheduling with configurable scheduler profiles that improve GPU utilization and align pod placement to your critical workloads at scale." +title: "AKS Configurable Scheduler Profiles (preview)" +description: "Optimize GPU and CPU utilization and align pod placement to your critical workloads at scale with Configurable Scheduler Profiles on AKS." date: 2026-04-23 authors: [colin-mixon] tags: [ai, performance, scheduler, best-practices, cost] @@ -13,6 +13,7 @@ Data shows most Kubernetes clusters only use an average of 10% cpu utilization. ![Diagram showing increased node utilization with the node bin packing scheduler profile](./configurable-scheduler-binpack-profile.png) ## How does the default Kubernetes scheduler place pods? + The AKS default scheduler scores nodes for workload placement based on a _LeastAllocated_ strategy, to spread across the nodes in a cluster. However, this behavior can result in inefficient resource utilization, as nodes with higher allocation are not favored. You can use `NodeResourcesFit` to control how pods are assigned to nodes based on available resources (CPU, GPU, memory, etc.), including favoring nodes with high resource utilization, within the set configuration. Thoughtful scheduling strategies can resolve pervasive challenges like resource utilization. The kube-scheduler is one of the core components of the Kubernetes Control Plane, alongside kube-apiserver, etcd, and the controller manager. The default scheduler was primarily designed for general-purpose workloads and out-of-box pod scheduling that can be restrictive if you want to bin pack nodes since the hard and soft constraints for pod scheduling do not align with scheduling pods with nodes of higher utilization. The scheduler selects the optimal node for queued pod(s) based on several constraints, including (but not limited to): @@ -83,7 +84,9 @@ spec: - name: nvidia.com/gpu weight: 5 ``` -### ResourceToCapacity + +### Increase AKS Cluster CPU Utilization + Scoring Strategy - ResourceToCapacity **This scheduler configuration ensures nodes are not oversaturated.** @@ -142,6 +145,5 @@ With Configurable Scheduler Profiles, teams gain fine-grained control over pod p [kueue-overview]: https://learn.microsoft.com/azure/aks/kueue-overview [best-practices-advanced-scheduler]: https://learn.microsoft.com/azure/aks/operator-best-practices-advanced-scheduler [scheduling-framework/#interfaces]: https://kubernetes.io/docs/concepts/scheduling-eviction/scheduling-framework/#interfaces -[memory-optimized-vm]: https://learn.microsoft.com/azure/virtual-machines/sizes/overview?tabs=breakdownseries%2Cgeneralsizelist%2Ccomputesizelist%2Cmemorysizelist%2Cstoragesizelist%2Cgpusizelist%2Cfpgasizelist%2Chpcsizelist#memory-optimized [supported-in-tree-scheduling-plugins]: https://learn.microsoft.com/azure/aks/concepts-scheduler-configuration#supported-in-tree-scheduling-plugins [node-bin-packing-configurations]: https://learn.microsoft.com/azure/aks/configure-node-binpack-scheduler?tabs=new-cluster From 1f0c76da0da25502e614631d065275d3ccec9760 Mon Sep 17 00:00:00 2001 From: colinmixonn <109253437+colinmixonn@users.noreply.github.com> Date: Fri, 20 Mar 2026 12:08:25 -0700 Subject: [PATCH 069/164] Fix scoring strategy terminology in AKS blog post Updated the blog post to correct the scoring strategy terminology for AKS cluster CPU utilization. --- .../index.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md b/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md index fc89cbd3f..4beecacae 100644 --- a/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md +++ b/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md @@ -31,8 +31,8 @@ Out of the available nodes, the scheduler then filters out nodes that don't meet This blog provides examples of three different scheduler profiles and details the benefits of each to increase node utilization for AKS clusters: -1. [How to increase AKS cluster GPU utilization](#increase-aks-cluster-gpu-utilization) -2. [How to increase AKS cluster CPU utilization](#increase-aks-cluster-cpu-utilization) +1. [How to increase AKS cluster GPU utilization](#increase-aks-cluster-gpu-utilization) +2. [How to increase AKS cluster CPU utilization](#increase-aks-cluster-cpu-utilization) ## Configurable Scheduler Profiles on AKS @@ -87,7 +87,7 @@ spec: ### Increase AKS Cluster CPU Utilization -Scoring Strategy - ResourceToCapacity +Scoring Strategy - RequestedToCapacityRatio **This scheduler configuration ensures nodes are not oversaturated.** ```yaml From 6c6198b816feea58a7f9059eadb82833aba5041d Mon Sep 17 00:00:00 2001 From: colinmixonn <109253437+colinmixonn@users.noreply.github.com> Date: Fri, 20 Mar 2026 12:18:53 -0700 Subject: [PATCH 070/164] Revise content for AKS Configurable Scheduler Profiles post Updated text for clarity and improved descriptions of features and benefits of Configurable Scheduler Profiles on AKS. --- .../index.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md b/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md index 4beecacae..e3c9de49a 100644 --- a/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md +++ b/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md @@ -23,11 +23,11 @@ Thoughtful scheduling strategies can resolve pervasive challenges like resource 3. Pod affinity/anti-affinity 4. Taints and tolerations -Out of the available nodes, the scheduler then filters out nodes that don't meet the requirements to identify the node that is most optimal for the pod(s). Today, the AKS default scheduler lacks the flexibility for users to change which criteria should be prioritized, and ignored, in the scheduling cycle on a per workload basis. This means the default scheduling criteria, and their fixed priority order, are not suitable for workloads that demand co-locating pods with their persistent volumes for increased data locality, optimizing GPU utilization for machine learning, or enforcing strict zone-level distribution for microservices. This rigidity often forces users to either accept suboptimal placement or manage a separate custom scheduler, both of which increase operational complexity. +Out of the available nodes, the scheduler then filters out nodes that don't meet the requirements to identify the node that is most optimal for the pod(s). Today, the default scheduler on AKS lacks the flexibility for users to change which criteria should be prioritized, and ignored, in the scheduling cycle on a per workload basis. This means the default scheduling criteria, and their fixed priority order, are not suitable for workloads that demand optimizing GPU utilization for batch jobs or enforcing strict zone-level distribution for microservices. This rigidity often forces users to either accept suboptimal placement or manage a separate custom scheduler, both of which increase operational complexity. -![kube-scheduler Phases Diagram](./kube-scheduler-scheduling-phases-diagram.png) +![Diagram of the kube-scheduler workflow showing pods entering the scheduling cycle and binding cycle to assign a pod on a node](./kube-scheduler-scheduling-phases-diagram.png) -**[Configurable Scheduler Profiles on AKS][concepts-scheduler-configuration] reduces operational complexity by providing extensibility and control.** Now, customers can define their own scheduling logic by enabling specific policies, changing policy priority, altering parameter weight, and changing policy evaluation point (i.e. PreFilter, Filter, Score) without deploying a second scheduler. On AKS, Configurable Scheduler Profiles allows customers to increase resiliency without operational overhead of YAML wrangling or reduce cluster costs without adopting a secondary scheduler. +**[Configurable Scheduler Profiles on AKS][concepts-scheduler-configuration] allows customers to benefit from the extensibility of the [scheduling framework][scheduling-framework/#interfaces] while reducing the operational overhead of adopting a second scheduler or defininng a customer scheduler.** Now, customers can define their own scheduling logic by enabling specific policies, changing policy priority, altering parameter weight, and changing policy evaluation point (i.e. PreFilter, Filter, Score). This blog provides examples of three different scheduler profiles and details the benefits of each to increase node utilization for AKS clusters: From b92fc5e4135770d86568e219d7e33ce19dc9291a Mon Sep 17 00:00:00 2001 From: colinmixonn <109253437+colinmixonn@users.noreply.github.com> Date: Fri, 20 Mar 2026 12:24:56 -0700 Subject: [PATCH 071/164] Update website/blog/tags.yml Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> --- website/blog/tags.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/website/blog/tags.yml b/website/blog/tags.yml index 5409eb1b0..0236b7469 100644 --- a/website/blog/tags.yml +++ b/website/blog/tags.yml @@ -90,7 +90,7 @@ cilium: cost: label: Cost permalink: /cost - description: Recommended patterns, anti-patterns, and guidance for managing and optimizing cost of AKS clusters. + description: Recommended patterns, anti-patterns, and guidance for managing and optimizing AKS cluster costs. databases: label: Databases From 69cccb92338ff585041f4a300ab00c5a8083fb5a Mon Sep 17 00:00:00 2001 From: colinmixonn <109253437+colinmixonn@users.noreply.github.com> Date: Fri, 20 Mar 2026 12:27:47 -0700 Subject: [PATCH 072/164] Apply suggestions from code review Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> --- .../2025-12-16-aks-config-scheduler-profiles-preview/index.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md b/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md index e3c9de49a..ca611589f 100644 --- a/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md +++ b/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md @@ -16,7 +16,7 @@ Data shows most Kubernetes clusters only use an average of 10% cpu utilization. The AKS default scheduler scores nodes for workload placement based on a _LeastAllocated_ strategy, to spread across the nodes in a cluster. However, this behavior can result in inefficient resource utilization, as nodes with higher allocation are not favored. You can use `NodeResourcesFit` to control how pods are assigned to nodes based on available resources (CPU, GPU, memory, etc.), including favoring nodes with high resource utilization, within the set configuration. -Thoughtful scheduling strategies can resolve pervasive challenges like resource utilization. The kube-scheduler is one of the core components of the Kubernetes Control Plane, alongside kube-apiserver, etcd, and the controller manager. The default scheduler was primarily designed for general-purpose workloads and out-of-box pod scheduling that can be restrictive if you want to bin pack nodes since the hard and soft constraints for pod scheduling do not align with scheduling pods with nodes of higher utilization. The scheduler selects the optimal node for queued pod(s) based on several constraints, including (but not limited to): +Thoughtful scheduling strategies can resolve pervasive challenges like resource utilization. The kube-scheduler is one of the core components of the Kubernetes control plane, alongside kube-apiserver, etcd, and the controller manager. The default scheduler was primarily designed for general-purpose workloads and out-of-the-box pod scheduling that can be restrictive if you want to bin pack nodes since the hard and soft constraints for pod scheduling do not align with scheduling pods with nodes of higher utilization. The scheduler selects the optimal node for queued pod(s) based on several constraints, including (but not limited to): 1. Resource requirements (CPU, memory) 2. Node affinity/anti-affinity @@ -38,7 +38,7 @@ This blog provides examples of three different scheduler profiles and details th Configurable Scheduler Profiles uses a Custom Resource Definition (CRD) that lets users define custom scheduler profiles. A dedicated controller continuously reconciles these user-defined configurations with the underlying kube-scheduler deployment, validating changes and applying them transparently. If a configuration causes the scheduler to become unhealthy, the controller automatically rolls back to the last known good state to ensure cluster stability. -![Configurable Scheduler Profiles Diagram](./config-scheduler-profiles.png) +![Architecture diagram showing how Configurable Scheduler Profiles use a CRD and controller to reconcile user-defined profiles with the kube-scheduler deployment](./config-scheduler-profiles.png) A profile is a set of one or more in-tree scheduling plugins and configurations that dictate how to schedule a pod. Previously, the scheduler configuration wasn't accessible to users. Starting from Kubernetes version 1.33, you can now configure and set a scheduler profile for your AKS cluster. 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bb45216f24085314f1ffc3e4b9eb3d1817242e19 Mon Sep 17 00:00:00 2001 From: colinmixonn <109253437+colinmixonn@users.noreply.github.com> Date: Fri, 20 Mar 2026 12:31:01 -0700 Subject: [PATCH 075/164] Update index.md --- .../2025-12-16-aks-config-scheduler-profiles-preview/index.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md b/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md index ca611589f..fa0858710 100644 --- a/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md +++ b/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md @@ -10,7 +10,7 @@ Data shows most Kubernetes clusters only use an average of 10% cpu utilization. -![Diagram showing increased node utilization with the node bin packing scheduler profile](./configurable-scheduler-binpack-profile.png) +![Diagram showing increased node utilization with the node bin packing scheduler profile](./hero-image.png) ## How does the default Kubernetes scheduler place pods? 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zImc8=SmZ4cC6M+C*c$A=JODf+>-&3D5qTzg_b4a(-)3AJ!{1ocGxY+odOT8#dGr9O zBSN$tdAgps03cu+fKY&G;IN+M^Qv{m6*#rYuT!6TmAEn1NR~)(hu|&k(HAKT3?Otw ze?GgnoHfk-Mr-0MgiM8FE2K&0y->7(qUBQa$bOh19ss>r6Y$!Kt1+sRCmUIA%_;AR zxy+Oa^;tiIFMwNVgYCTP&rfoo>#(B2E#@{LE3DXWeMtYD6?g=d5Z*$cT zK794?ck&*T=#JpypTmo~-bONtgmi&vns7D(p9rue8A|g5RE*ItzGI0iF-v;ars?zI z;4s*`2^A7QgvXlsu;R!UMZpy|fTtpDP>?)%x^Qq2>nHqhC>`8;;CzZ%+C2*nWjk98 z1cKcxi~OJX8f2m8t7NXoSo?$f&`<}C1^{r1`=5hvkk`-~7S&R9oKkWO#kEEdA{hDE z&cB;$k@oxFp?FmxzWnBo25$umQ)*=&G5#BJd442jv%$ZqV1 znBRM06h}JM>VlWS|L%J(z~1`M;DFG88wf&Gs2J5O%FRyR4>4`fkQWT}BIm!!6Nwx} zzKIvy423I(2P%1h9H)n-%*xEIUZB;0ke`wGT4AcNtMAjRPmEt*aC0JKagzTFrvhK- z-QWPGI Date: Fri, 20 Mar 2026 14:42:35 -0700 Subject: [PATCH 078/164] Revise blog on AKS Configurable Scheduler Profiles Updated the blog to reflect the change from three to two scheduler profiles and added an FAQ section addressing common questions about the configurable scheduler profiles. --- .../index.md | 10 +++++++--- 1 file changed, 7 insertions(+), 3 deletions(-) diff --git a/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md b/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md index fa0858710..c1bd5a63d 100644 --- a/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md +++ b/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md @@ -29,10 +29,10 @@ Out of the available nodes, the scheduler then filters out nodes that don't meet **[Configurable Scheduler Profiles on AKS][concepts-scheduler-configuration] allows customers to benefit from the extensibility of the [scheduling framework][scheduling-framework/#interfaces] while reducing the operational overhead of adopting a second scheduler or defininng a customer scheduler.** Now, customers can define their own scheduling logic by enabling specific policies, changing policy priority, altering parameter weight, and changing policy evaluation point (i.e. PreFilter, Filter, Score). -This blog provides examples of three different scheduler profiles and details the benefits of each to increase node utilization for AKS clusters: +This blog provides examples of two different scheduler profiles and details the benefits of each to increase node utilization for AKS clusters: -1. [How to increase AKS cluster GPU utilization](#increase-aks-cluster-gpu-utilization) -2. [How to increase AKS cluster CPU utilization](#increase-aks-cluster-cpu-utilization) +1. [How to increase AKS cluster GPU and CPU utilization with MostAllocated](#increase-aks-cluster-gpu-utilization) +2. [How to increase AKS cluster CPU utilization with MostAllocated](#increase-aks-cluster-cpu-utilization) ## Configurable Scheduler Profiles on AKS @@ -132,6 +132,10 @@ spec: - utilization: 100 score: 0 ``` +### FAQ +1. How does this interact with the autoscalers: NAP, CAS, and VPA? +2. What if I don't use a resource in the scoringStrategy? Then that resource will not be considered in the filter or scoring cucles of the defined Configurable Scheduler Profile. +3. Which scoringStrategy does AKS recommend to increase node utilization? AKS recommends using the RequestedToCapacityRatio strategy because it provides a more granual scoring approach allowing users to define an ideal utilization curve for their respeciive nodes. ## Next Steps: Optimize Azure resources and test Configurable Scheduler Profiles on AKS From 43563ac0e82007fe3c003379162decef4fcc7956 Mon Sep 17 00:00:00 2001 From: colinmixonn <109253437+colinmixonn@users.noreply.github.com> Date: Fri, 20 Mar 2026 14:46:02 -0700 Subject: [PATCH 079/164] Enhance blog on AKS Configurable Scheduler Profiles Expanded the introduction to Configurable Scheduler Profiles on AKS, detailing its benefits and providing examples of two different scheduler profiles to increase node utilization. --- .../index.md | 14 ++++++-------- 1 file changed, 6 insertions(+), 8 deletions(-) diff --git a/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md b/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md index c1bd5a63d..756982966 100644 --- a/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md +++ b/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md @@ -6,7 +6,12 @@ authors: [colin-mixon] tags: [ai, performance, scheduler, best-practices, cost] --- -Data shows most Kubernetes clusters only use an average of 10% cpu utilization. With the introduction of Configurable Scheduler Profiles on AKS, customers now have the opportunity to increase their node utilization across CPU and GPU resources and optimize their costs. +Data shows most Kubernetes clusters only use an average of 10% cpu utilization. With the introduction of Configurable Scheduler Profiles on AKS, customers now have the opportunity to increase their node utilization across CPU and GPU resources and optimize their costs. **[Configurable Scheduler Profiles on AKS][concepts-scheduler-configuration] allows customers to benefit from the extensibility of the [scheduling framework][scheduling-framework/#interfaces] while reducing the operational overhead of adopting a second scheduler or defininng a customer scheduler.** Now, customers can define their own scheduling logic by enabling specific policies, changing policy priority, altering parameter weight, and changing policy evaluation point (i.e. PreFilter, Filter, Score). + +This blog provides examples of two different scheduler profiles and details the benefits of each to increase node utilization for AKS clusters: + +1. [How to increase AKS cluster GPU and CPU utilization with MostAllocated](#increase-aks-cluster-gpu-utilization) +2. [How to increase AKS cluster CPU utilization with MostAllocated](#increase-aks-cluster-cpu-utilization) @@ -27,13 +32,6 @@ Out of the available nodes, the scheduler then filters out nodes that don't meet ![Diagram of the kube-scheduler workflow showing pods entering the scheduling cycle and binding cycle to assign a pod on a node](./kube-scheduler-scheduling-phases-diagram.png) -**[Configurable Scheduler Profiles on AKS][concepts-scheduler-configuration] allows customers to benefit from the extensibility of the [scheduling framework][scheduling-framework/#interfaces] while reducing the operational overhead of adopting a second scheduler or defininng a customer scheduler.** Now, customers can define their own scheduling logic by enabling specific policies, changing policy priority, altering parameter weight, and changing policy evaluation point (i.e. PreFilter, Filter, Score). - -This blog provides examples of two different scheduler profiles and details the benefits of each to increase node utilization for AKS clusters: - -1. [How to increase AKS cluster GPU and CPU utilization with MostAllocated](#increase-aks-cluster-gpu-utilization) -2. [How to increase AKS cluster CPU utilization with MostAllocated](#increase-aks-cluster-cpu-utilization) - ## Configurable Scheduler Profiles on AKS Configurable Scheduler Profiles uses a Custom Resource Definition (CRD) that lets users define custom scheduler profiles. A dedicated controller continuously reconciles these user-defined configurations with the underlying kube-scheduler deployment, validating changes and applying them transparently. If a configuration causes the scheduler to become unhealthy, the controller automatically rolls back to the last known good state to ensure cluster stability. From 6fbee96c228ebb8b47827580d44de605b7dd03e4 Mon Sep 17 00:00:00 2001 From: colinmixonn <109253437+colinmixonn@users.noreply.github.com> Date: Fri, 20 Mar 2026 14:46:13 -0700 Subject: [PATCH 080/164] Refactor introduction for clarity and conciseness Removed redundant sentence and improved clarity in the introduction. --- .../2025-12-16-aks-config-scheduler-profiles-preview/index.md | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md b/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md index 756982966..b5a6a8056 100644 --- a/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md +++ b/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md @@ -6,7 +6,9 @@ authors: [colin-mixon] tags: [ai, performance, scheduler, best-practices, cost] --- -Data shows most Kubernetes clusters only use an average of 10% cpu utilization. With the introduction of Configurable Scheduler Profiles on AKS, customers now have the opportunity to increase their node utilization across CPU and GPU resources and optimize their costs. **[Configurable Scheduler Profiles on AKS][concepts-scheduler-configuration] allows customers to benefit from the extensibility of the [scheduling framework][scheduling-framework/#interfaces] while reducing the operational overhead of adopting a second scheduler or defininng a customer scheduler.** Now, customers can define their own scheduling logic by enabling specific policies, changing policy priority, altering parameter weight, and changing policy evaluation point (i.e. PreFilter, Filter, Score). +Data shows most Kubernetes clusters only use an average of 10% cpu utilization. With the introduction of Configurable Scheduler Profiles on AKS, customers now have the opportunity to increase their node utilization across CPU and GPU resources and optimize their costs. + +**[Configurable Scheduler Profiles on AKS][concepts-scheduler-configuration] allows customers to benefit from the extensibility of the [scheduling framework][scheduling-framework/#interfaces] while reducing the operational overhead of adopting a second scheduler or defininng a customer scheduler.** Now, customers can define their own scheduling logic by enabling specific policies, changing policy priority, altering parameter weight, and changing policy evaluation point (i.e. PreFilter, Filter, Score). This blog provides examples of two different scheduler profiles and details the benefits of each to increase node utilization for AKS clusters: From fcd58c033ff74cb19a28a0b5a6482d7dcb871ae3 Mon Sep 17 00:00:00 2001 From: colinmixonn <109253437+colinmixonn@users.noreply.github.com> Date: Fri, 20 Mar 2026 14:46:57 -0700 Subject: [PATCH 081/164] Refine note on resource weights and scheduling plugins Removed introductory phrase from note about resource weights and parameters. --- .../2025-12-16-aks-config-scheduler-profiles-preview/index.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md b/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md index b5a6a8056..e8348cd68 100644 --- a/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md +++ b/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md @@ -47,7 +47,7 @@ A profile is a set of one or more in-tree scheduling plugins and configurations 3. Resource and topology optimization scheduling plugins :::note -Treat these examples as starting points. Adjust resource weights, utilization thresholds, and plugin parameters to match your VM SKUs, workload patterns, and cluster topology. +Adjust resource weights, utilization thresholds, and plugin parameters to match your VM SKUs, workload patterns, and cluster topology. ::: ### Increase AKS Cluster GPU Utilization From 17e134811e89ee14f35a630a4ba58857a2b748d4 Mon Sep 17 00:00:00 2001 From: colinmixonn <109253437+colinmixonn@users.noreply.github.com> Date: Fri, 20 Mar 2026 15:00:14 -0700 Subject: [PATCH 082/164] Update website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> --- .../index.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md b/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md index e8348cd68..1ba6cdc76 100644 --- a/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md +++ b/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md @@ -133,9 +133,9 @@ spec: score: 0 ``` ### FAQ -1. How does this interact with the autoscalers: NAP, CAS, and VPA? -2. What if I don't use a resource in the scoringStrategy? Then that resource will not be considered in the filter or scoring cucles of the defined Configurable Scheduler Profile. -3. Which scoringStrategy does AKS recommend to increase node utilization? AKS recommends using the RequestedToCapacityRatio strategy because it provides a more granual scoring approach allowing users to define an ideal utilization curve for their respeciive nodes. +1. How does this interact with autoscalers such as Node Auto Provisioning (NAP), Cluster Autoscaler (CAS), and Vertical Pod Autoscaler (VPA)? +2. What if I don't use a resource in the scoringStrategy? Then that resource will not be considered in the filter or scoring cycles of the defined Configurable Scheduler Profile. +3. Which scoringStrategy does AKS recommend to increase node utilization? AKS recommends using the RequestedToCapacityRatio strategy because it provides a more granular scoring approach allowing users to define an ideal utilization curve for their respective nodes. ## Next Steps: Optimize Azure resources and test Configurable Scheduler Profiles on AKS From 8409dc954b9e63facc82f19f785d052d2879efa7 Mon Sep 17 00:00:00 2001 From: colinmixonn <109253437+colinmixonn@users.noreply.github.com> Date: Fri, 20 Mar 2026 15:08:42 -0700 Subject: [PATCH 083/164] Enhance Configurable Scheduler Profiles explanation Updated the description of Configurable Scheduler Profiles to emphasize increased node utilization and clarified the functionality of the scheduling framework. Added information about the accessibility of scheduler configuration starting from Kubernetes version 1.33. --- .../index.md | 10 ++++++---- 1 file changed, 6 insertions(+), 4 deletions(-) diff --git a/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md b/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md index 1ba6cdc76..c14b1935a 100644 --- a/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md +++ b/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md @@ -8,7 +8,7 @@ tags: [ai, performance, scheduler, best-practices, cost] Data shows most Kubernetes clusters only use an average of 10% cpu utilization. With the introduction of Configurable Scheduler Profiles on AKS, customers now have the opportunity to increase their node utilization across CPU and GPU resources and optimize their costs. -**[Configurable Scheduler Profiles on AKS][concepts-scheduler-configuration] allows customers to benefit from the extensibility of the [scheduling framework][scheduling-framework/#interfaces] while reducing the operational overhead of adopting a second scheduler or defininng a customer scheduler.** Now, customers can define their own scheduling logic by enabling specific policies, changing policy priority, altering parameter weight, and changing policy evaluation point (i.e. PreFilter, Filter, Score). +**[Configurable Scheduler Profiles on AKS][concepts-scheduler-configuration] allows customers to increase node utilization by defining their own scheduling logic by enabling specific policies, changing policy priority, altering parameter weight, and changing policy evaluation point (i.e. PreFilter, Filter, Score). This blog provides examples of two different scheduler profiles and details the benefits of each to increase node utilization for AKS clusters: @@ -30,17 +30,19 @@ Thoughtful scheduling strategies can resolve pervasive challenges like resource 3. Pod affinity/anti-affinity 4. Taints and tolerations -Out of the available nodes, the scheduler then filters out nodes that don't meet the requirements to identify the node that is most optimal for the pod(s). Today, the default scheduler on AKS lacks the flexibility for users to change which criteria should be prioritized, and ignored, in the scheduling cycle on a per workload basis. This means the default scheduling criteria, and their fixed priority order, are not suitable for workloads that demand optimizing GPU utilization for batch jobs or enforcing strict zone-level distribution for microservices. This rigidity often forces users to either accept suboptimal placement or manage a separate custom scheduler, both of which increase operational complexity. +Out of the available nodes, the scheduler then filters out nodes that don't meet the requirements to identify the node that is most optimal for the pod(s). Today, the default scheduler on AKS lacks the flexibility for users to change which criteria should be prioritized, and ignored, in the scheduling cycle on a per workload basis. This means the default scheduling criteria, and their fixed priority order, are not suitable for workloads that demand optimizing GPU utilization for batch jobs or enforcing strict zone-level distribution for microservices. This rigidity often forces users to either accept suboptimal placement or manage a separate custom scheduler, both of which increase operational complexity. ![Diagram of the kube-scheduler workflow showing pods entering the scheduling cycle and binding cycle to assign a pod on a node](./kube-scheduler-scheduling-phases-diagram.png) +Previously, the scheduler configuration wasn't accessible to users. Starting from Kubernetes version 1.33, you can now configure and set a scheduler profile for your AKS cluster using Configurable Scheduler Profiles. + ## Configurable Scheduler Profiles on AKS -Configurable Scheduler Profiles uses a Custom Resource Definition (CRD) that lets users define custom scheduler profiles. A dedicated controller continuously reconciles these user-defined configurations with the underlying kube-scheduler deployment, validating changes and applying them transparently. If a configuration causes the scheduler to become unhealthy, the controller automatically rolls back to the last known good state to ensure cluster stability. +customers to benefit from the extensibility of the [scheduling framework][scheduling-framework/#interfaces] while reducing the operational overhead of adopting a second scheduler or defininng a customer scheduler.** Configurable Scheduler Profiles uses a Custom Resource Definition (CRD) that lets users define custom scheduler profiles with their own scheduling logic. A dedicated controller continuously reconciles these user-defined configurations with the underlying kube-scheduler deployment, validating changes and applying them transparently. If a configuration causes the scheduler to become unhealthy, the controller automatically rolls back to the last known good state to ensure cluster stability. This means incorrect plugins or values will not be applied. ![Architecture diagram showing how Configurable Scheduler Profiles use a CRD and controller to reconcile user-defined profiles with the kube-scheduler deployment](./config-scheduler-profiles.png) -A profile is a set of one or more in-tree scheduling plugins and configurations that dictate how to schedule a pod. Previously, the scheduler configuration wasn't accessible to users. Starting from Kubernetes version 1.33, you can now configure and set a scheduler profile for your AKS cluster. AKS supports 18 in-tree Kubernetes [scheduling plugins][supported-in-tree-scheduling-plugins], which can be generally grouped into three categories: +A profile is a set of one or more in-tree scheduling plugins and configurations that dictate how to schedule a pod. AKS supports 18 in-tree Kubernetes [scheduling plugins][supported-in-tree-scheduling-plugins], which can be generally grouped into three categories: 1. Scheduling constraints and order-based plugins 2. Node selection constraints scheduling plugins From 673b0ebfd756456909b0955086785b215ad9fc77 Mon Sep 17 00:00:00 2001 From: colinmixonn <109253437+colinmixonn@users.noreply.github.com> Date: Fri, 20 Mar 2026 15:08:54 -0700 Subject: [PATCH 084/164] Fix formatting in AKS Configurable Scheduler Profiles blog --- .../2025-12-16-aks-config-scheduler-profiles-preview/index.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md b/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md index c14b1935a..c8dbaa012 100644 --- a/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md +++ b/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md @@ -8,7 +8,7 @@ tags: [ai, performance, scheduler, best-practices, cost] Data shows most Kubernetes clusters only use an average of 10% cpu utilization. With the introduction of Configurable Scheduler Profiles on AKS, customers now have the opportunity to increase their node utilization across CPU and GPU resources and optimize their costs. -**[Configurable Scheduler Profiles on AKS][concepts-scheduler-configuration] allows customers to increase node utilization by defining their own scheduling logic by enabling specific policies, changing policy priority, altering parameter weight, and changing policy evaluation point (i.e. PreFilter, Filter, Score). +[Configurable Scheduler Profiles on AKS][concepts-scheduler-configuration] allows customers to increase node utilization by defining their own scheduling logic by enabling specific policies, changing policy priority, altering parameter weight, and changing policy evaluation point (i.e. PreFilter, Filter, Score). This blog provides examples of two different scheduler profiles and details the benefits of each to increase node utilization for AKS clusters: From f40a7acf331ad85d0fe638cd3fcfc7a739f2f2dd Mon Sep 17 00:00:00 2001 From: colinmixonn <109253437+colinmixonn@users.noreply.github.com> Date: Fri, 20 Mar 2026 15:23:47 -0700 Subject: [PATCH 085/164] Improve clarity on AKS Configurable Scheduler Profiles Enhanced the explanation of Kubernetes scheduler operations and the benefits of Configurable Scheduler Profiles on AKS. Clarified the impact of scheduling strategies on resource utilization and operational complexity. --- .../index.md | 15 +++++++-------- 1 file changed, 7 insertions(+), 8 deletions(-) diff --git a/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md b/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md index c8dbaa012..60ac27a0d 100644 --- a/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md +++ b/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md @@ -6,7 +6,7 @@ authors: [colin-mixon] tags: [ai, performance, scheduler, best-practices, cost] --- -Data shows most Kubernetes clusters only use an average of 10% cpu utilization. With the introduction of Configurable Scheduler Profiles on AKS, customers now have the opportunity to increase their node utilization across CPU and GPU resources and optimize their costs. +Data shows most Kubernetes clusters only use an average of 10% cpu utilization. While there are many factors that impact node utilization, as a core componenet of the Kuberentes control plane, the kube-scheduler has a big influence on the utilizatoin of nodes. With the introduction of Configurable Scheduler Profiles on AKS, customers now have the opportunity to increase their node utilization across CPU and GPU resources and optimize their costs with access to fine-grain pod scheduling strategies. [Configurable Scheduler Profiles on AKS][concepts-scheduler-configuration] allows customers to increase node utilization by defining their own scheduling logic by enabling specific policies, changing policy priority, altering parameter weight, and changing policy evaluation point (i.e. PreFilter, Filter, Score). @@ -21,24 +21,23 @@ This blog provides examples of two different scheduler profiles and details the ## How does the default Kubernetes scheduler place pods? -The AKS default scheduler scores nodes for workload placement based on a _LeastAllocated_ strategy, to spread across the nodes in a cluster. However, this behavior can result in inefficient resource utilization, as nodes with higher allocation are not favored. You can use `NodeResourcesFit` to control how pods are assigned to nodes based on available resources (CPU, GPU, memory, etc.), including favoring nodes with high resource utilization, within the set configuration. - -Thoughtful scheduling strategies can resolve pervasive challenges like resource utilization. The kube-scheduler is one of the core components of the Kubernetes control plane, alongside kube-apiserver, etcd, and the controller manager. The default scheduler was primarily designed for general-purpose workloads and out-of-the-box pod scheduling that can be restrictive if you want to bin pack nodes since the hard and soft constraints for pod scheduling do not align with scheduling pods with nodes of higher utilization. The scheduler selects the optimal node for queued pod(s) based on several constraints, including (but not limited to): +The Kubernetes scheduler operates across two cycles, a scheduling cycle and the binding cycle. The scheduling cycle has two sub-phases: filtering and scoring. The filter phase removes unsuitable nodes based on hard constratins and the scoring attributes a score to nodes that is most suitable for the incoming pod. The scheduler filters and scores the optimal node for a pending pod based on several hard and soft constraints, including (but not limited to): 1. Resource requirements (CPU, memory) 2. Node affinity/anti-affinity 3. Pod affinity/anti-affinity 4. Taints and tolerations +5. TopologySpreadConstraints -Out of the available nodes, the scheduler then filters out nodes that don't meet the requirements to identify the node that is most optimal for the pod(s). Today, the default scheduler on AKS lacks the flexibility for users to change which criteria should be prioritized, and ignored, in the scheduling cycle on a per workload basis. This means the default scheduling criteria, and their fixed priority order, are not suitable for workloads that demand optimizing GPU utilization for batch jobs or enforcing strict zone-level distribution for microservices. This rigidity often forces users to either accept suboptimal placement or manage a separate custom scheduler, both of which increase operational complexity. +The default scheduler was primarily designed for general-purpose workloads and out-of-the-box pod scheduling that can be restrictive if you want to bin pack nodes since the hard and soft constraints for pod scheduling do not align with scheduling pods with nodes of higher utilization. The Kubernetes default scheduler scores nodes for workload placement based on a _LeastAllocated_ strategy, to spread across the nodes in a cluster. However, this behavior can result in inefficient resource utilization, as nodes with higher allocation are not favored. You can use `NodeResourcesFit` to control how pods are assigned to nodes based on available resources (CPU, GPU, memory, etc.), including favoring nodes with high resource utilization, within the set configuration. ![Diagram of the kube-scheduler workflow showing pods entering the scheduling cycle and binding cycle to assign a pod on a node](./kube-scheduler-scheduling-phases-diagram.png) -Previously, the scheduler configuration wasn't accessible to users. Starting from Kubernetes version 1.33, you can now configure and set a scheduler profile for your AKS cluster using Configurable Scheduler Profiles. +Today, the default scheduler on AKS lacks the flexibility for users to change which criteria should be prioritized, and ignored, in the scheduling cycle on a per cluster basis. This means the default scheduling criteria, and their fixed priority order, are not suitable for workloads that demand optimizing GPU utilization for batch jobs. This rigidity often forces users to either accept suboptimal placement or manage a separate custom scheduler, both of which increase operational complexity. Starting from Kubernetes version 1.33, you can now configure and optimize a scheduler profile for your AKS cluster using Configurable Scheduler Profiles. ## Configurable Scheduler Profiles on AKS -customers to benefit from the extensibility of the [scheduling framework][scheduling-framework/#interfaces] while reducing the operational overhead of adopting a second scheduler or defininng a customer scheduler.** Configurable Scheduler Profiles uses a Custom Resource Definition (CRD) that lets users define custom scheduler profiles with their own scheduling logic. A dedicated controller continuously reconciles these user-defined configurations with the underlying kube-scheduler deployment, validating changes and applying them transparently. If a configuration causes the scheduler to become unhealthy, the controller automatically rolls back to the last known good state to ensure cluster stability. This means incorrect plugins or values will not be applied. +[Configurable Scheduler Profiles on AKS][concepts-scheduler-configuration] allows customers to customers to benefit from the extensibility of the [scheduling framework][scheduling-framework-interfaces] while reducing the operational overhead of adopting a second scheduler or defininng a customer scheduler. Configurable Scheduler Profiles uses a Custom Resource Definition (CRD) that lets users define custom scheduler profiles with their own scheduling logic. A dedicated controller continuously reconciles these user-defined configurations with the underlying kube-scheduler deployment, validating changes and applying them transparently. If a configuration causes the scheduler to become unhealthy, the controller automatically rolls back to the last known good state to ensure cluster stability. This means incorrect plugins or values will not be applied. ![Architecture diagram showing how Configurable Scheduler Profiles use a CRD and controller to reconcile user-defined profiles with the kube-scheduler deployment](./config-scheduler-profiles.png) @@ -150,6 +149,6 @@ With Configurable Scheduler Profiles, teams gain fine-grained control over pod p [concepts-scheduler-configuration]: https://learn.microsoft.com/azure/aks/concepts-scheduler-configuration [kueue-overview]: https://learn.microsoft.com/azure/aks/kueue-overview [best-practices-advanced-scheduler]: https://learn.microsoft.com/azure/aks/operator-best-practices-advanced-scheduler -[scheduling-framework/#interfaces]: https://kubernetes.io/docs/concepts/scheduling-eviction/scheduling-framework/#interfaces +[scheduling-framework-interfaces]: https://kubernetes.io/docs/concepts/scheduling-eviction/scheduling-framework/#interfaces [supported-in-tree-scheduling-plugins]: https://learn.microsoft.com/azure/aks/concepts-scheduler-configuration#supported-in-tree-scheduling-plugins [node-bin-packing-configurations]: https://learn.microsoft.com/azure/aks/configure-node-binpack-scheduler?tabs=new-cluster From 3a0d97e91bcfe36fb480d5ae1805e2d7679db74d Mon Sep 17 00:00:00 2001 From: colinmixonn <109253437+colinmixonn@users.noreply.github.com> Date: Fri, 20 Mar 2026 15:44:50 -0700 Subject: [PATCH 086/164] Add files via upload --- .../default-config-scheduler-comparison.png | Bin 0 -> 62972 bytes 1 file changed, 0 insertions(+), 0 deletions(-) create mode 100644 website/blog/2025-12-16-aks-config-scheduler-profiles-preview/default-config-scheduler-comparison.png diff --git a/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/default-config-scheduler-comparison.png b/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/default-config-scheduler-comparison.png new file mode 100644 index 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zGkg_rkb0l!RgTt1F50EIG><6xcv?2wT^l>V%NgO3C{SeZ}NJkL8LqAfjImzZ1y(>**I@scjkfKtnM z`B$CP^udW@%QDoiOZn0mMUM^XNTLl>Jh=Oi$#~g Date: Fri, 20 Mar 2026 15:48:51 -0700 Subject: [PATCH 087/164] Enhance blog post with node utilization insights Added section on increasing node utilization and operator control with configurable scheduler profiles. --- .../index.md | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md b/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md index 60ac27a0d..233e787dc 100644 --- a/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md +++ b/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md @@ -51,6 +51,11 @@ A profile is a set of one or more in-tree scheduling plugins and configurations Adjust resource weights, utilization thresholds, and plugin parameters to match your VM SKUs, workload patterns, and cluster topology. ::: +## Increase Node Utilization and Operator Control + +Configurable Scheduler Profiles using the `NodeResourcesFit` plugin shows a visible consolidation pattern that differs from the default scheduler's logic. As result, platform engineers gain more control and resources are used more efficiently when using AKS. +![Table showing increased node utilization with the node bin packing scheduler profiles versus the pod distribution using the default scheduler](./default-config-scheduler-comparison.png) + ### Increase AKS Cluster GPU Utilization Additionally, customers running GPU-dependent applications like batch jobs will benefit from improved bin-packing and increased GPU utilization. For example, scheduling jobs on nodes with a higher relative GPU utilization, can reduce costs while maintaining performance. 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+- .../kube-scheduler-scheduling-phases-diagram.png | Bin 5 files changed, 1 insertion(+), 1 deletion(-) rename website/blog/{2025-12-16-aks-config-scheduler-profiles-preview => 2026-3-31-aks-config-scheduler-profiles-preview}/config-scheduler-profiles.png (100%) rename website/blog/{2025-12-16-aks-config-scheduler-profiles-preview => 2026-3-31-aks-config-scheduler-profiles-preview}/default-config-scheduler-comparison.png (100%) rename website/blog/{2025-12-16-aks-config-scheduler-profiles-preview => 2026-3-31-aks-config-scheduler-profiles-preview}/hero-image.png (100%) rename website/blog/{2025-12-16-aks-config-scheduler-profiles-preview => 2026-3-31-aks-config-scheduler-profiles-preview}/index.md (99%) rename website/blog/{2025-12-16-aks-config-scheduler-profiles-preview => 2026-3-31-aks-config-scheduler-profiles-preview}/kube-scheduler-scheduling-phases-diagram.png (100%) diff --git a/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/config-scheduler-profiles.png b/website/blog/2026-3-31-aks-config-scheduler-profiles-preview/config-scheduler-profiles.png similarity index 100% rename from website/blog/2025-12-16-aks-config-scheduler-profiles-preview/config-scheduler-profiles.png rename to website/blog/2026-3-31-aks-config-scheduler-profiles-preview/config-scheduler-profiles.png diff --git a/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/default-config-scheduler-comparison.png b/website/blog/2026-3-31-aks-config-scheduler-profiles-preview/default-config-scheduler-comparison.png similarity index 100% rename from website/blog/2025-12-16-aks-config-scheduler-profiles-preview/default-config-scheduler-comparison.png rename to website/blog/2026-3-31-aks-config-scheduler-profiles-preview/default-config-scheduler-comparison.png diff --git a/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/hero-image.png b/website/blog/2026-3-31-aks-config-scheduler-profiles-preview/hero-image.png similarity index 100% rename from website/blog/2025-12-16-aks-config-scheduler-profiles-preview/hero-image.png rename to website/blog/2026-3-31-aks-config-scheduler-profiles-preview/hero-image.png diff --git a/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md b/website/blog/2026-3-31-aks-config-scheduler-profiles-preview/index.md similarity index 99% rename from website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md rename to website/blog/2026-3-31-aks-config-scheduler-profiles-preview/index.md index 233e787dc..68f5110df 100644 --- a/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/index.md +++ b/website/blog/2026-3-31-aks-config-scheduler-profiles-preview/index.md @@ -1,7 +1,7 @@ --- title: "AKS Configurable Scheduler Profiles (preview)" description: "Optimize GPU and CPU utilization and align pod placement to your critical workloads at scale with Configurable Scheduler Profiles on AKS." -date: 2026-04-23 +date: 2026-03-31 authors: [colin-mixon] tags: [ai, performance, scheduler, best-practices, cost] --- diff --git a/website/blog/2025-12-16-aks-config-scheduler-profiles-preview/kube-scheduler-scheduling-phases-diagram.png b/website/blog/2026-3-31-aks-config-scheduler-profiles-preview/kube-scheduler-scheduling-phases-diagram.png similarity index 100% rename from website/blog/2025-12-16-aks-config-scheduler-profiles-preview/kube-scheduler-scheduling-phases-diagram.png rename to website/blog/2026-3-31-aks-config-scheduler-profiles-preview/kube-scheduler-scheduling-phases-diagram.png From 381c3ecd71ee34aa457d9391af79072de1fae02c Mon Sep 17 00:00:00 2001 From: colinmixonn <109253437+colinmixonn@users.noreply.github.com> Date: Mon, 23 Mar 2026 09:47:56 -0700 Subject: [PATCH 095/164] Enhance FAQ section in AKS Config Scheduler Profiles Added FAQ section addressing interactions with autoscalers and recommended scoring strategy for AKS. --- .../2026-3-31-aks-config-scheduler-profiles-preview/index.md | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/website/blog/2026-3-31-aks-config-scheduler-profiles-preview/index.md b/website/blog/2026-3-31-aks-config-scheduler-profiles-preview/index.md index 68f5110df..2e756102d 100644 --- a/website/blog/2026-3-31-aks-config-scheduler-profiles-preview/index.md +++ b/website/blog/2026-3-31-aks-config-scheduler-profiles-preview/index.md @@ -138,8 +138,10 @@ spec: - utilization: 100 score: 0 ``` + ### FAQ -1. How does this interact with autoscalers such as Node Auto Provisioning (NAP), Cluster Autoscaler (CAS), and Vertical Pod Autoscaler (VPA)? + +1. How does this interact with autoscalers such as Node Auto Provisioning (NAP), Cluster Autoscaler (CAS), and Vertical Pod Autoscaler (VPA)? 2. What if I don't use a resource in the scoringStrategy? Then that resource will not be considered in the filter or scoring cycles of the defined Configurable Scheduler Profile. 3. Which scoringStrategy does AKS recommend to increase node utilization? AKS recommends using the RequestedToCapacityRatio strategy because it provides a more granular scoring approach allowing users to define an ideal utilization curve for their respective nodes. From 91ae3684da0bbdc535d65ff57b8404dcddd5c6e6 Mon Sep 17 00:00:00 2001 From: colinmixonn <109253437+colinmixonn@users.noreply.github.com> Date: Mon, 23 Mar 2026 10:00:54 -0700 Subject: [PATCH 096/164] Fix typos and enhance clarity in AKS blog post Corrected typos and improved clarity in the blog post about AKS Configurable Scheduler Profiles. --- .../2026-3-31-aks-config-scheduler-profiles-preview/index.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/website/blog/2026-3-31-aks-config-scheduler-profiles-preview/index.md b/website/blog/2026-3-31-aks-config-scheduler-profiles-preview/index.md index 2e756102d..7bcc435c3 100644 --- a/website/blog/2026-3-31-aks-config-scheduler-profiles-preview/index.md +++ b/website/blog/2026-3-31-aks-config-scheduler-profiles-preview/index.md @@ -6,7 +6,7 @@ authors: [colin-mixon] tags: [ai, performance, scheduler, best-practices, cost] --- -Data shows most Kubernetes clusters only use an average of 10% cpu utilization. While there are many factors that impact node utilization, as a core componenet of the Kuberentes control plane, the kube-scheduler has a big influence on the utilizatoin of nodes. With the introduction of Configurable Scheduler Profiles on AKS, customers now have the opportunity to increase their node utilization across CPU and GPU resources and optimize their costs with access to fine-grain pod scheduling strategies. +Data shows most Kubernetes clusters only use an average of 10% cpu utilization. While there are many factors that impact node utilization, as a core componenet of the Kuberenetes control plane, the kube-scheduler has a big influence on the utilization of nodes. With the introduction of Configurable Scheduler Profiles on AKS, customers now have the opportunity to increase their node utilization across CPU and GPU resources and optimize their costs with access to fine-grain pod scheduling strategies. [Configurable Scheduler Profiles on AKS][concepts-scheduler-configuration] allows customers to increase node utilization by defining their own scheduling logic by enabling specific policies, changing policy priority, altering parameter weight, and changing policy evaluation point (i.e. PreFilter, Filter, Score). @@ -21,7 +21,7 @@ This blog provides examples of two different scheduler profiles and details the ## How does the default Kubernetes scheduler place pods? -The Kubernetes scheduler operates across two cycles, a scheduling cycle and the binding cycle. The scheduling cycle has two sub-phases: filtering and scoring. The filter phase removes unsuitable nodes based on hard constratins and the scoring attributes a score to nodes that is most suitable for the incoming pod. The scheduler filters and scores the optimal node for a pending pod based on several hard and soft constraints, including (but not limited to): +The Kubernetes scheduler operates across two cycles, a synchronous scheduling cycle and an asynchronous binding cycle. The scheduling cycle has two sub-phases: filtering and scoring, and only manages one pod at a time. The filter phase removes unsuitable nodes based on hard constratins and the scoring phase calculates a score to the remianing nodes based on the soft contstraints, utlimaely scoring the the node most suitable for the incoming pod the highest. The binding cycle can manage multiple pods in parallel. If a pod binding to a node fails, the node with the next highest score is tried. The scheduler filters and scores the optimal node for a pending pod based on several hard and soft constraints, including (but not limited to): 1. Resource requirements (CPU, memory) 2. Node affinity/anti-affinity From df220d2d2ab0a8ef7b2918612cd829dd4124be8e Mon Sep 17 00:00:00 2001 From: colinmixonn <109253437+colinmixonn@users.noreply.github.com> Date: Mon, 23 Mar 2026 10:02:34 -0700 Subject: [PATCH 097/164] Apply suggestions from code review Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> --- .../2026-3-31-aks-config-scheduler-profiles-preview/index.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/website/blog/2026-3-31-aks-config-scheduler-profiles-preview/index.md b/website/blog/2026-3-31-aks-config-scheduler-profiles-preview/index.md index 7bcc435c3..9906f6b98 100644 --- a/website/blog/2026-3-31-aks-config-scheduler-profiles-preview/index.md +++ b/website/blog/2026-3-31-aks-config-scheduler-profiles-preview/index.md @@ -8,7 +8,7 @@ tags: [ai, performance, scheduler, best-practices, cost] Data shows most Kubernetes clusters only use an average of 10% cpu utilization. While there are many factors that impact node utilization, as a core componenet of the Kuberenetes control plane, the kube-scheduler has a big influence on the utilization of nodes. With the introduction of Configurable Scheduler Profiles on AKS, customers now have the opportunity to increase their node utilization across CPU and GPU resources and optimize their costs with access to fine-grain pod scheduling strategies. -[Configurable Scheduler Profiles on AKS][concepts-scheduler-configuration] allows customers to increase node utilization by defining their own scheduling logic by enabling specific policies, changing policy priority, altering parameter weight, and changing policy evaluation point (i.e. PreFilter, Filter, Score). +[Configurable Scheduler Profiles on AKS][concepts-scheduler-configuration] allows customers to increase node utilization by configuring their own scheduling logic: enabling specific policies, adjusting policy priorities, tuning parameter weights, and choosing when each policy runs (for example, during PreFilter, Filter, or Score). This blog provides examples of two different scheduler profiles and details the benefits of each to increase node utilization for AKS clusters: From d3915cc58c2942ab74b9dc90bee900c9c6d92f96 Mon Sep 17 00:00:00 2001 From: colinmixonn <109253437+colinmixonn@users.noreply.github.com> Date: Mon, 23 Mar 2026 10:05:17 -0700 Subject: [PATCH 098/164] Apply suggestions from code review Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> --- .../2026-3-31-aks-config-scheduler-profiles-preview/index.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/website/blog/2026-3-31-aks-config-scheduler-profiles-preview/index.md b/website/blog/2026-3-31-aks-config-scheduler-profiles-preview/index.md index 9906f6b98..5828dc266 100644 --- a/website/blog/2026-3-31-aks-config-scheduler-profiles-preview/index.md +++ b/website/blog/2026-3-31-aks-config-scheduler-profiles-preview/index.md @@ -13,7 +13,7 @@ Data shows most Kubernetes clusters only use an average of 10% cpu utilization. This blog provides examples of two different scheduler profiles and details the benefits of each to increase node utilization for AKS clusters: 1. [How to increase AKS cluster GPU and CPU utilization with MostAllocated](#increase-aks-cluster-gpu-utilization) -2. [How to increase AKS cluster CPU utilization with MostAllocated](#increase-aks-cluster-cpu-utilization) +2. [How to increase AKS cluster CPU utilization with RequestedToCapacityRatio](#increase-aks-cluster-cpu-utilization) @@ -37,7 +37,7 @@ Today, the default scheduler on AKS lacks the flexibility for users to change wh ## Configurable Scheduler Profiles on AKS -[Configurable Scheduler Profiles on AKS][concepts-scheduler-configuration] allows customers to customers to benefit from the extensibility of the [scheduling framework][scheduling-framework-interfaces] while reducing the operational overhead of adopting a second scheduler or defininng a customer scheduler. Configurable Scheduler Profiles uses a Custom Resource Definition (CRD) that lets users define custom scheduler profiles with their own scheduling logic. A dedicated controller continuously reconciles these user-defined configurations with the underlying kube-scheduler deployment, validating changes and applying them transparently. If a configuration causes the scheduler to become unhealthy, the controller automatically rolls back to the last known good state to ensure cluster stability. This means incorrect plugins or values will not be applied. +[Configurable Scheduler Profiles on AKS][concepts-scheduler-configuration] allows customers to benefit from the extensibility of the [scheduling framework][scheduling-framework-interfaces] while reducing the operational overhead of adopting a second scheduler or defining a custom scheduler. Configurable Scheduler Profiles uses a Custom Resource Definition (CRD) that lets users define custom scheduler profiles with their own scheduling logic. A dedicated controller continuously reconciles these user-defined configurations with the underlying kube-scheduler deployment, validating changes and applying them transparently. If a configuration causes the scheduler to become unhealthy, the controller automatically rolls back to the last known good state to ensure cluster stability. This means incorrect plugins or values will not be applied. ![Architecture diagram showing how Configurable Scheduler Profiles use a CRD and controller to reconcile user-defined profiles with the kube-scheduler deployment](./config-scheduler-profiles.png) From 004b930740910a3aa032756bc71d3504ed3d02c2 Mon Sep 17 00:00:00 2001 From: colinmixonn <109253437+colinmixonn@users.noreply.github.com> Date: Mon, 23 Mar 2026 10:07:31 -0700 Subject: [PATCH 099/164] updated date format in folder and extended description for SEO --- .../config-scheduler-profiles.png | Bin .../default-config-scheduler-comparison.png | Bin .../hero-image.png | Bin .../index.md | 2 +- .../kube-scheduler-scheduling-phases-diagram.png | Bin 5 files changed, 1 insertion(+), 1 deletion(-) rename website/blog/{2026-3-31-aks-config-scheduler-profiles-preview => 2026-03-31-aks-config-scheduler-profiles-preview}/config-scheduler-profiles.png (100%) rename website/blog/{2026-3-31-aks-config-scheduler-profiles-preview => 2026-03-31-aks-config-scheduler-profiles-preview}/default-config-scheduler-comparison.png (100%) rename website/blog/{2026-3-31-aks-config-scheduler-profiles-preview => 2026-03-31-aks-config-scheduler-profiles-preview}/hero-image.png (100%) rename website/blog/{2026-3-31-aks-config-scheduler-profiles-preview => 2026-03-31-aks-config-scheduler-profiles-preview}/index.md (99%) rename website/blog/{2026-3-31-aks-config-scheduler-profiles-preview => 2026-03-31-aks-config-scheduler-profiles-preview}/kube-scheduler-scheduling-phases-diagram.png (100%) diff --git a/website/blog/2026-3-31-aks-config-scheduler-profiles-preview/config-scheduler-profiles.png b/website/blog/2026-03-31-aks-config-scheduler-profiles-preview/config-scheduler-profiles.png similarity index 100% rename from website/blog/2026-3-31-aks-config-scheduler-profiles-preview/config-scheduler-profiles.png rename to website/blog/2026-03-31-aks-config-scheduler-profiles-preview/config-scheduler-profiles.png diff --git a/website/blog/2026-3-31-aks-config-scheduler-profiles-preview/default-config-scheduler-comparison.png b/website/blog/2026-03-31-aks-config-scheduler-profiles-preview/default-config-scheduler-comparison.png similarity index 100% rename from website/blog/2026-3-31-aks-config-scheduler-profiles-preview/default-config-scheduler-comparison.png rename to website/blog/2026-03-31-aks-config-scheduler-profiles-preview/default-config-scheduler-comparison.png diff --git a/website/blog/2026-3-31-aks-config-scheduler-profiles-preview/hero-image.png b/website/blog/2026-03-31-aks-config-scheduler-profiles-preview/hero-image.png similarity index 100% rename from website/blog/2026-3-31-aks-config-scheduler-profiles-preview/hero-image.png rename to website/blog/2026-03-31-aks-config-scheduler-profiles-preview/hero-image.png diff --git a/website/blog/2026-3-31-aks-config-scheduler-profiles-preview/index.md b/website/blog/2026-03-31-aks-config-scheduler-profiles-preview/index.md similarity index 99% rename from website/blog/2026-3-31-aks-config-scheduler-profiles-preview/index.md rename to website/blog/2026-03-31-aks-config-scheduler-profiles-preview/index.md index 5828dc266..076b53663 100644 --- a/website/blog/2026-3-31-aks-config-scheduler-profiles-preview/index.md +++ b/website/blog/2026-03-31-aks-config-scheduler-profiles-preview/index.md @@ -1,6 +1,6 @@ --- title: "AKS Configurable Scheduler Profiles (preview)" -description: "Optimize GPU and CPU utilization and align pod placement to your critical workloads at scale with Configurable Scheduler Profiles on AKS." +description: "Optimize GPU and CPU utilization and align pod placement to your critical workloads at scale with Configurable Scheduler Profiles on AKS to optimize node cost." date: 2026-03-31 authors: [colin-mixon] tags: [ai, performance, scheduler, best-practices, cost] diff --git a/website/blog/2026-3-31-aks-config-scheduler-profiles-preview/kube-scheduler-scheduling-phases-diagram.png b/website/blog/2026-03-31-aks-config-scheduler-profiles-preview/kube-scheduler-scheduling-phases-diagram.png similarity index 100% rename from website/blog/2026-3-31-aks-config-scheduler-profiles-preview/kube-scheduler-scheduling-phases-diagram.png rename to website/blog/2026-03-31-aks-config-scheduler-profiles-preview/kube-scheduler-scheduling-phases-diagram.png From 3833ed02cf6f3289961391f35eba1260853a71ec Mon Sep 17 00:00:00 2001 From: colinmixonn <109253437+colinmixonn@users.noreply.github.com> Date: Mon, 23 Mar 2026 10:09:02 -0700 Subject: [PATCH 100/164] updated config for GPU utilization --- .../2026-03-31-aks-config-scheduler-profiles-preview/index.md | 2 ++ 1 file changed, 2 insertions(+) diff --git a/website/blog/2026-03-31-aks-config-scheduler-profiles-preview/index.md b/website/blog/2026-03-31-aks-config-scheduler-profiles-preview/index.md index 076b53663..7aa6647cf 100644 --- a/website/blog/2026-03-31-aks-config-scheduler-profiles-preview/index.md +++ b/website/blog/2026-03-31-aks-config-scheduler-profiles-preview/index.md @@ -82,6 +82,8 @@ spec: pluginConfig: - name: NodeResourcesFit args: + apiVersion: kubescheduler.config.k8s.io/v1 + kind: NodeResourcesFitArgs scoringStrategy: type: MostAllocated resources: From b5735cef07ec556c1d239d1dca66d8a5b57aff49 Mon Sep 17 00:00:00 2001 From: colinmixonn <109253437+colinmixonn@users.noreply.github.com> Date: Mon, 23 Mar 2026 10:15:20 -0700 Subject: [PATCH 101/164] Fix typos and enhance Kubernetes scheduler description Corrected typos and improved clarity in the explanation of the Kubernetes scheduler's operation. --- .../2026-03-31-aks-config-scheduler-profiles-preview/index.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/website/blog/2026-03-31-aks-config-scheduler-profiles-preview/index.md b/website/blog/2026-03-31-aks-config-scheduler-profiles-preview/index.md index 7aa6647cf..5516396ac 100644 --- a/website/blog/2026-03-31-aks-config-scheduler-profiles-preview/index.md +++ b/website/blog/2026-03-31-aks-config-scheduler-profiles-preview/index.md @@ -21,7 +21,7 @@ This blog provides examples of two different scheduler profiles and details the ## How does the default Kubernetes scheduler place pods? -The Kubernetes scheduler operates across two cycles, a synchronous scheduling cycle and an asynchronous binding cycle. The scheduling cycle has two sub-phases: filtering and scoring, and only manages one pod at a time. The filter phase removes unsuitable nodes based on hard constratins and the scoring phase calculates a score to the remianing nodes based on the soft contstraints, utlimaely scoring the the node most suitable for the incoming pod the highest. The binding cycle can manage multiple pods in parallel. If a pod binding to a node fails, the node with the next highest score is tried. The scheduler filters and scores the optimal node for a pending pod based on several hard and soft constraints, including (but not limited to): +The Kubernetes scheduler operates across two cycles, a synchronous scheduling cycle and an asynchronous binding cycle. The scheduling cycle has two sub-phases: filtering and scoring, and only manages one pod at a time. The filter phase removes unsuitable nodes based on hard constraints and the scoring phase calculates a score to the remaining nodes based on the soft contstraints, utlimately the most suitable node has the highest score. The binding cycle can manage multiple pods in parallel. If a pod binding to a node fails, the node with the next highest score is tried. The scheduler filters and scores the optimal node for a pending pod based on several hard and soft constraints, including (but not limited to): 1. Resource requirements (CPU, memory) 2. Node affinity/anti-affinity From 2594b65e94f3e0ba26a4c9135abec64bde984c12 Mon Sep 17 00:00:00 2001 From: colinmixonn <109253437+colinmixonn@users.noreply.github.com> Date: Mon, 23 Mar 2026 10:59:05 -0700 Subject: [PATCH 102/164] Fix typos in AKS Configurable Scheduler Profiles blog Corrected spelling and capitalization errors in the introduction. --- .../2026-03-31-aks-config-scheduler-profiles-preview/index.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/website/blog/2026-03-31-aks-config-scheduler-profiles-preview/index.md b/website/blog/2026-03-31-aks-config-scheduler-profiles-preview/index.md index 5516396ac..c67850872 100644 --- a/website/blog/2026-03-31-aks-config-scheduler-profiles-preview/index.md +++ b/website/blog/2026-03-31-aks-config-scheduler-profiles-preview/index.md @@ -6,7 +6,7 @@ authors: [colin-mixon] tags: [ai, performance, scheduler, best-practices, cost] --- -Data shows most Kubernetes clusters only use an average of 10% cpu utilization. While there are many factors that impact node utilization, as a core componenet of the Kuberenetes control plane, the kube-scheduler has a big influence on the utilization of nodes. With the introduction of Configurable Scheduler Profiles on AKS, customers now have the opportunity to increase their node utilization across CPU and GPU resources and optimize their costs with access to fine-grain pod scheduling strategies. +Data shows most Kubernetes clusters only use an average of 10% CPU utilization. While there are many factors that impact node utilization, as a core component of the Kubernetes control plane, the kube-scheduler has a big influence on the utilization of nodes. With the introduction of Configurable Scheduler Profiles on AKS, customers now have the opportunity to increase their node utilization across CPU and GPU resources and optimize their costs with access to fine-grained pod scheduling strategies. [Configurable Scheduler Profiles on AKS][concepts-scheduler-configuration] allows customers to increase node utilization by configuring their own scheduling logic: enabling specific policies, adjusting policy priorities, tuning parameter weights, and choosing when each policy runs (for example, during PreFilter, Filter, or Score). From 4faa6bd859663985b7c0d1803d269d17afbba750 Mon Sep 17 00:00:00 2001 From: colinmixonn <109253437+colinmixonn@users.noreply.github.com> Date: Mon, 23 Mar 2026 11:01:10 -0700 Subject: [PATCH 103/164] Apply suggestions from code review Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> --- .../index.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/website/blog/2026-03-31-aks-config-scheduler-profiles-preview/index.md b/website/blog/2026-03-31-aks-config-scheduler-profiles-preview/index.md index c67850872..b72680ec4 100644 --- a/website/blog/2026-03-31-aks-config-scheduler-profiles-preview/index.md +++ b/website/blog/2026-03-31-aks-config-scheduler-profiles-preview/index.md @@ -53,12 +53,12 @@ Adjust resource weights, utilization thresholds, and plugin parameters to match ## Increase Node Utilization and Operator Control -Configurable Scheduler Profiles using the `NodeResourcesFit` plugin shows a visible consolidation pattern that differs from the default scheduler's logic. As result, platform engineers gain more control and resources are used more efficiently when using AKS. +Configurable Scheduler Profiles using the `NodeResourcesFit` plugin shows a visible consolidation pattern that differs from the default scheduler's logic. As a result, platform engineers gain more control and resources are used more efficiently when using AKS. ![Table showing increased node utilization with the node bin packing scheduler profiles versus the pod distribution using the default scheduler](./default-config-scheduler-comparison.png) ### Increase AKS Cluster GPU Utilization -Additionally, customers running GPU-dependent applications like batch jobs will benefit from improved bin-packing and increased GPU utilization. For example, scheduling jobs on nodes with a higher relative GPU utilization, can reduce costs while maintaining performance. +Additionally, customers running GPU-dependent applications like batch jobs will benefit from improved bin-packing and increased GPU utilization. For example, scheduling jobs on nodes with a higher relative GPU utilization can reduce costs while maintaining performance. **This scheduler configuration maximizes provisioned GPU resource by consolidating smaller jobs onto fewer nodes, lowering the operational cost of underutilized resources without sacrificing performance.** @@ -143,7 +143,7 @@ spec: ### FAQ -1. How does this interact with autoscalers such as Node Auto Provisioning (NAP), Cluster Autoscaler (CAS), and Vertical Pod Autoscaler (VPA)? +1. How does this interact with autoscalers such as Node Auto Provisioning (NAP), Cluster Autoscaler (CAS), and Vertical Pod Autoscaler (VPA)? Configurable Scheduler Profiles change how pods are placed on nodes, while autoscalers still make scaling decisions based on their existing metrics and pending pods, so they work together without disabling one another. 2. What if I don't use a resource in the scoringStrategy? Then that resource will not be considered in the filter or scoring cycles of the defined Configurable Scheduler Profile. 3. Which scoringStrategy does AKS recommend to increase node utilization? AKS recommends using the RequestedToCapacityRatio strategy because it provides a more granular scoring approach allowing users to define an ideal utilization curve for their respective nodes. From 1fc238efc9486f0fef2f3577b842acc6d359d954 Mon Sep 17 00:00:00 2001 From: colinmixonn <109253437+colinmixonn@users.noreply.github.com> Date: Mon, 23 Mar 2026 11:05:26 -0700 Subject: [PATCH 104/164] Fix typos and enhance clarity in scheduler documentation Corrected typos and improved clarity in the explanation of the Kubernetes scheduler's operation and plugin configuration. --- .../index.md | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/website/blog/2026-03-31-aks-config-scheduler-profiles-preview/index.md b/website/blog/2026-03-31-aks-config-scheduler-profiles-preview/index.md index b72680ec4..9dfa8923b 100644 --- a/website/blog/2026-03-31-aks-config-scheduler-profiles-preview/index.md +++ b/website/blog/2026-03-31-aks-config-scheduler-profiles-preview/index.md @@ -21,7 +21,7 @@ This blog provides examples of two different scheduler profiles and details the ## How does the default Kubernetes scheduler place pods? -The Kubernetes scheduler operates across two cycles, a synchronous scheduling cycle and an asynchronous binding cycle. The scheduling cycle has two sub-phases: filtering and scoring, and only manages one pod at a time. The filter phase removes unsuitable nodes based on hard constraints and the scoring phase calculates a score to the remaining nodes based on the soft contstraints, utlimately the most suitable node has the highest score. The binding cycle can manage multiple pods in parallel. If a pod binding to a node fails, the node with the next highest score is tried. The scheduler filters and scores the optimal node for a pending pod based on several hard and soft constraints, including (but not limited to): +The Kubernetes scheduler operates across two cycles, a synchronous scheduling cycle and an asynchronous binding cycle. The scheduling cycle has two sub-phases: filtering and scoring, and only manages one pod at a time. The filter phase removes unsuitable nodes based on hard constraints and the scoring phase calculates a score to the remaining nodes based on the soft constraints, ultimately the most suitable node has the highest score. The binding cycle can manage multiple pods in parallel. If a pod binding to a node fails, the node with the next highest score is tried. The scheduler filters and scores the optimal node for a pending pod based on several hard and soft constraints, including (but not limited to): 1. Resource requirements (CPU, memory) 2. Node affinity/anti-affinity @@ -79,6 +79,8 @@ spec: - name: ImageLocality - name: NodeResourcesFit - name: NodeResourcesBalancedAllocation + disabled: + - name: PodTopologySpread pluginConfig: - name: NodeResourcesFit args: @@ -113,6 +115,8 @@ spec: multiPoint: enabled: - name: NodeResourcesFit + disabled: + - name: PodTopologySpread pluginConfig: - name: NodeResourcesFit args: From c7fc1053a8da0f0037db6d1efc0904dcb7ea2e09 Mon Sep 17 00:00:00 2001 From: colinmixonn <109253437+colinmixonn@users.noreply.github.com> Date: Mon, 23 Mar 2026 11:10:10 -0700 Subject: [PATCH 105/164] Apply suggestions from code review Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> --- .../2026-03-31-aks-config-scheduler-profiles-preview/index.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/website/blog/2026-03-31-aks-config-scheduler-profiles-preview/index.md b/website/blog/2026-03-31-aks-config-scheduler-profiles-preview/index.md index 9dfa8923b..ab17b3541 100644 --- a/website/blog/2026-03-31-aks-config-scheduler-profiles-preview/index.md +++ b/website/blog/2026-03-31-aks-config-scheduler-profiles-preview/index.md @@ -21,7 +21,7 @@ This blog provides examples of two different scheduler profiles and details the ## How does the default Kubernetes scheduler place pods? -The Kubernetes scheduler operates across two cycles, a synchronous scheduling cycle and an asynchronous binding cycle. The scheduling cycle has two sub-phases: filtering and scoring, and only manages one pod at a time. The filter phase removes unsuitable nodes based on hard constraints and the scoring phase calculates a score to the remaining nodes based on the soft constraints, ultimately the most suitable node has the highest score. The binding cycle can manage multiple pods in parallel. If a pod binding to a node fails, the node with the next highest score is tried. The scheduler filters and scores the optimal node for a pending pod based on several hard and soft constraints, including (but not limited to): +The Kubernetes scheduler operates in two cycles: a synchronous scheduling cycle and an asynchronous binding cycle. The scheduling cycle has two sub-phases, filtering and scoring, and only manages one pod at a time. During the filter phase, the scheduler removes unsuitable nodes based on hard constraints. During the scoring phase, it calculates a score for the remaining nodes based on soft constraints; ultimately, the most suitable node has the highest score. The binding cycle can manage multiple pods in parallel. If binding a pod to a node fails, the scheduler tries the node with the next highest score. The scheduler filters and scores the optimal node for a pending pod based on several hard and soft constraints, including (but not limited to): 1. Resource requirements (CPU, memory) 2. 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z^;F2@4T+=(jTKrm4Uw>>(&Y~B>E`ZY3GvdX#NyhIA!{zDn9ng}es1_*Vmu!Rg8VQ< zv)6sXL_gj}cJA=2>dD&t8F4?%&6q^_YZpIXCKDPiV(FAnc)Ek5@n3{^g_%(bg2T0a z9?1Re)f!=${1-84z5T70JjZ*zLfF8|_-}9cNtwTOA6>GCP=OP5{pMR|IdI-v&*6y0 zKMJ0ghwPt9kTt+hu3DhXvCKmcLQdz7s~XyI!#^IK3EyD1@AL%FF$%yaP-i$ zSbn4c{=2f|;b*%4YI^_S#eX;Rf5iGfOHM*>3qL{rhGOWqh@yV(-t}k0M796f>HmSM z{MSDt`(u_v{*Pb&k7AYb|5@bmVKx_UFUC|Hsq&|9+5! aJK8mT*%?)%D|!s{OZJ`8+e%5Jp#KGDIJk5G literal 0 HcmV?d00001 From 025f06d44de2a21cdcab31f8228b22eb74c813d2 Mon Sep 17 00:00:00 2001 From: colinmixonn <109253437+colinmixonn@users.noreply.github.com> Date: Mon, 23 Mar 2026 14:29:58 -0700 Subject: [PATCH 108/164] Enhance blog on AKS Configurable Scheduler Profiles Revise blog content to improve clarity on AKS Configurable Scheduler Profiles, including updated examples and recommendations for increasing node utilization. Adjust sections on scheduler operations and limitations to reflect new insights and best practices. --- .../index.md | 114 ++++++++++-------- 1 file changed, 65 insertions(+), 49 deletions(-) diff --git a/website/blog/2026-03-31-aks-config-scheduler-profiles-preview/index.md b/website/blog/2026-03-31-aks-config-scheduler-profiles-preview/index.md index ab17b3541..bdd94052c 100644 --- a/website/blog/2026-03-31-aks-config-scheduler-profiles-preview/index.md +++ b/website/blog/2026-03-31-aks-config-scheduler-profiles-preview/index.md @@ -6,14 +6,15 @@ authors: [colin-mixon] tags: [ai, performance, scheduler, best-practices, cost] --- -Data shows most Kubernetes clusters only use an average of 10% CPU utilization. While there are many factors that impact node utilization, as a core component of the Kubernetes control plane, the kube-scheduler has a big influence on the utilization of nodes. With the introduction of Configurable Scheduler Profiles on AKS, customers now have the opportunity to increase their node utilization across CPU and GPU resources and optimize their costs with access to fine-grained pod scheduling strategies. +On average, Kubernetes clusters reach [10% CPU utilization][cast-ai-k8s-cost-report] and most Kubernetes containers use less than [25% of their requested CPU][datadog-state-of-containers]. This data signals that underutilized resources are materially contributing to increased infrastructure cost. While there are many factors that impact node utilization, as a core component of the Kubernetes control plane, the kube-scheduler has a big influence on node utilization. With the introduction of Configurable Scheduler Profiles on AKS, customers now have the opportunity to increase their node utilization across CPU and GPU resources and optimize their costs with access to fine-grained pod scheduling strategies. [Configurable Scheduler Profiles on AKS][concepts-scheduler-configuration] allows customers to increase node utilization by configuring their own scheduling logic: enabling specific policies, adjusting policy priorities, tuning parameter weights, and choosing when each policy runs (for example, during PreFilter, Filter, or Score). -This blog provides examples of two different scheduler profiles and details the benefits of each to increase node utilization for AKS clusters: +This blog details how the default Kubernetes scheduler places pods, limitations, and provides best-practice recommendations to increase node utilization for your workloads using Configurable Scheduler Profiles on AKS. -1. [How to increase AKS cluster GPU and CPU utilization with MostAllocated](#increase-aks-cluster-gpu-utilization) +1. [How does kube-scheduler work?](how-does-the-default-kubernetes-scheduler-place-pods) 2. [How to increase AKS cluster CPU utilization with RequestedToCapacityRatio](#increase-aks-cluster-cpu-utilization) +3. [How to increase AKS cluster GPU and CPU utilization with MostAllocated](#increase-aks-cluster-gpu-utilization) @@ -21,7 +22,15 @@ This blog provides examples of two different scheduler profiles and details the ## How does the default Kubernetes scheduler place pods? -The Kubernetes scheduler operates in two cycles: a synchronous scheduling cycle and an asynchronous binding cycle. The scheduling cycle has two sub-phases, filtering and scoring, and only manages one pod at a time. During the filter phase, the scheduler removes unsuitable nodes based on hard constraints. During the scoring phase, it calculates a score for the remaining nodes based on soft constraints; ultimately, the most suitable node has the highest score. The binding cycle can manage multiple pods in parallel. If binding a pod to a node fails, the scheduler tries the node with the next highest score. The scheduler filters and scores the optimal node for a pending pod based on several hard and soft constraints, including (but not limited to): +The Kubernetes scheduler operates in two cycles: a synchronous scheduling cycle and an asynchronous binding cycle. The scheduling cycle has two sub-phases, filtering and scoring, and only manages one pod at a time. +1. **Filtering** phase removes unsuitable nodes based on hard and soft constraints. +2. **Scoring** phase calculates a score for the remaining nodes; ultimately, the most suitable node has the highest score. + +Once a node is selected, the binding cycle can process multiple pods in parallel. During this phase, the scheduler attempts to bind the pod to the chosen node. If binding a pod to a node fails, the scheduler tries the node with the next highest score. + +![Diagram of the kube-scheduler workflow showing pods entering the scheduling cycle and binding cycle to assign a pod on a node](./kube-scheduler-scheduling-phases-diagram.png) + +When filtering and scoring nodes, for the pending pod, the default scheduler considers several hard and soft constraints with predefined weights, including (but not limited to): 1. Resource requirements (CPU, memory) 2. Node affinity/anti-affinity @@ -29,11 +38,11 @@ The Kubernetes scheduler operates in two cycles: a synchronous scheduling cycle 4. Taints and tolerations 5. TopologySpreadConstraints -The default scheduler was primarily designed for general-purpose workloads and out-of-the-box pod scheduling that can be restrictive if you want to bin pack nodes since the hard and soft constraints for pod scheduling do not align with scheduling pods with nodes of higher utilization. The Kubernetes default scheduler scores nodes for workload placement based on a _LeastAllocated_ strategy, to spread across the nodes in a cluster. However, this behavior can result in inefficient resource utilization, as nodes with higher allocation are not favored. You can use `NodeResourcesFit` to control how pods are assigned to nodes based on available resources (CPU, GPU, memory, etc.), including favoring nodes with high resource utilization, within the set configuration. +### Limitations of the default Kubernetes scheduler -![Diagram of the kube-scheduler workflow showing pods entering the scheduling cycle and binding cycle to assign a pod on a node](./kube-scheduler-scheduling-phases-diagram.png) +The default scheduler is primarily designed for general-purpose workloads that prioritizes nodes with the most avaialable resources using the _LeastAllocated_ scoring strategy. This spreads pods across nodes, even when they could safely be packed more densely. While this works well for many services, the default scheduling criteria, and their fixed priority order, are not suitable for workloads that demand optimizing GPU and CPU utilization. In these scenarios, spreading pods across nodes can lead to fragmented resources, underutilized GPUs, and increased infrastructure cost. -Today, the default scheduler on AKS lacks the flexibility for users to change which criteria should be prioritized, and ignored, in the scheduling cycle on a per cluster basis. This means the default scheduling criteria, and their fixed priority order, are not suitable for workloads that demand optimizing GPU utilization for batch jobs. This rigidity often forces users to either accept suboptimal placement or manage a separate custom scheduler, both of which increase operational complexity. Starting from Kubernetes version 1.33, you can now configure and optimize a scheduler profile for your AKS cluster using Configurable Scheduler Profiles. +Today, the default scheduler on AKS lacks the flexibility for users to change which criteria should be prioritized, or ignored, in the scheduling cycle on a per cluster basis. This rigidity often forces users to either accept suboptimal placement or manage a separate custom scheduler, both of which increase operational complexity. Starting with Kubernetes v1.33, AKS introduces Configurable Scheduler Profiles, enabling customized scheduler behavior without maintaining a separate scheduler. Now, users can adjust the `NodeResourcesFit` plugin from the default configuration to favor nodes with higher utilization to achieve more efficient bin‑packing and reduce infrastructure cost. ## Configurable Scheduler Profiles on AKS @@ -41,26 +50,25 @@ Today, the default scheduler on AKS lacks the flexibility for users to change wh ![Architecture diagram showing how Configurable Scheduler Profiles use a CRD and controller to reconcile user-defined profiles with the kube-scheduler deployment](./config-scheduler-profiles.png) -A profile is a set of one or more in-tree scheduling plugins and configurations that dictate how to schedule a pod. AKS supports 18 in-tree Kubernetes [scheduling plugins][supported-in-tree-scheduling-plugins], which can be generally grouped into three categories: - -1. Scheduling constraints and order-based plugins -2. Node selection constraints scheduling plugins -3. Resource and topology optimization scheduling plugins - -:::note -Adjust resource weights, utilization thresholds, and plugin parameters to match your VM SKUs, workload patterns, and cluster topology. -::: +A profile is a set of one or more in-tree scheduling plugins and configurations that dictate how to schedule a pod. AKS supports 18 in-tree Kubernetes [scheduling plugins][supported-in-tree-scheduling-plugins]. ## Increase Node Utilization and Operator Control Configurable Scheduler Profiles using the `NodeResourcesFit` plugin shows a visible consolidation pattern that differs from the default scheduler's logic. As a result, platform engineers gain more control and resources are used more efficiently when using AKS. ![Table showing increased node utilization with the node bin packing scheduler profiles versus the pod distribution using the default scheduler](./default-config-scheduler-comparison.png) -### Increase AKS Cluster GPU Utilization +### Increase AKS Cluster CPU Utilization -Additionally, customers running GPU-dependent applications like batch jobs will benefit from improved bin-packing and increased GPU utilization. For example, scheduling jobs on nodes with a higher relative GPU utilization can reduce costs while maintaining performance. +Scoring Strategy - RequestedToCapacityRatio -**This scheduler configuration maximizes provisioned GPU resource by consolidating smaller jobs onto fewer nodes, lowering the operational cost of underutilized resources without sacrificing performance.** +For example, this bin packing strategy allows users to configure a target utilization of 85%. + +AKS recommends using the scoring strategy `RequestedToCapacityRatio` because it provides a more granular scoring approach allowing users to define an ideal utilization curve for their respective nodes. +**This scheduler configuration ensures nodes are not oversaturated.** + +:::note +Adjust resource weights, utilization thresholds, and plugin parameters to match your VM SKUs, workload patterns, and cluster topology. +::: ```yaml apiVersion: aks.azure.com/v1alpha1 @@ -72,13 +80,11 @@ spec: apiVersion: kubescheduler.config.k8s.io/v1 kind: KubeSchedulerConfiguration profiles: - - schedulerName: gpu-node-binpacking-scheduler + - schedulerName: cpu-binpacking-scheduler plugins: multiPoint: enabled: - - name: ImageLocality - name: NodeResourcesFit - - name: NodeResourcesBalancedAllocation disabled: - name: PodTopologySpread pluginConfig: @@ -87,18 +93,37 @@ spec: apiVersion: kubescheduler.config.k8s.io/v1 kind: NodeResourcesFitArgs scoringStrategy: - type: MostAllocated + type: RequestedToCapacityRatio resources: - name: cpu + weight: 8 + - name: memory weight: 1 - - name: nvidia.com/gpu - weight: 5 + requestedToCapacityRatio: + shape: + - utilization: 0 + score: 0 + - utilization: 30 + score: 8 + - utilization: 50 + score: 10 + - utilization: 85 + score: 10 + - utilization: 90 + score: 3 + - utilization: 100 + score: 0 ``` -### Increase AKS Cluster CPU Utilization +### Increase AKS Cluster GPU Utilization -Scoring Strategy - RequestedToCapacityRatio -**This scheduler configuration ensures nodes are not oversaturated.** +Additionally, customers running GPU-dependent applications like batch jobs will benefit from improved bin-packing and increased GPU utilization. For example, scheduling jobs on nodes with a higher relative GPU utilization can reduce costs while maintaining performance. + +**This scheduler configuration maximizes provisioned GPU resource by consolidating smaller jobs onto fewer nodes, lowering the operational cost of underutilized resources without sacrificing performance.** + +:::note +Adjust resource weights, utilization thresholds, and plugin parameters to match your VM SKUs, workload patterns, and cluster topology. +::: ```yaml apiVersion: aks.azure.com/v1alpha1 @@ -110,11 +135,13 @@ spec: apiVersion: kubescheduler.config.k8s.io/v1 kind: KubeSchedulerConfiguration profiles: - - schedulerName: cpu-binpacking-scheduler + - schedulerName: gpu-node-binpacking-scheduler plugins: multiPoint: enabled: + - name: ImageLocality - name: NodeResourcesFit + - name: NodeResourcesBalancedAllocation disabled: - name: PodTopologySpread pluginConfig: @@ -123,33 +150,20 @@ spec: apiVersion: kubescheduler.config.k8s.io/v1 kind: NodeResourcesFitArgs scoringStrategy: - type: RequestedToCapacityRatio + type: MostAllocated resources: - name: cpu - weight: 8 - - name: memory weight: 1 - requestedToCapacityRatio: - shape: - - utilization: 0 - score: 0 - - utilization: 30 - score: 8 - - utilization: 50 - score: 10 - - utilization: 85 - score: 10 - - utilization: 90 - score: 3 - - utilization: 100 - score: 0 + - name: nvidia.com/gpu + weight: 5 ``` ### FAQ -1. How does this interact with autoscalers such as Node Auto Provisioning (NAP), Cluster Autoscaler (CAS), and Vertical Pod Autoscaler (VPA)? Configurable Scheduler Profiles change how pods are placed on nodes, while autoscalers still make scaling decisions based on their existing metrics and pending pods, so they work together without disabling one another. -2. What if I don't use a resource in the scoringStrategy? Then that resource will not be considered in the filter or scoring cycles of the defined Configurable Scheduler Profile. -3. Which scoringStrategy does AKS recommend to increase node utilization? AKS recommends using the RequestedToCapacityRatio strategy because it provides a more granular scoring approach allowing users to define an ideal utilization curve for their respective nodes. +1. Which Bin packing strategy does AKS recommend to increase node utilization? AKS recommends using the scoring strategy `RequestedToCapacityRatio` because it provides a more granular scoring approach allowing users to define an ideal utilization curve for their respective nodes. For example, this bin packing strategy allows users to configure a target utilization of 85%. +2. How does this interact with autoscalers such as Node Auto Provisioning (NAP), Cluster Autoscaler (CAS), and Vertical Pod Autoscaler (VPA)? Configurable Scheduler Profiles change how pods are placed on nodes, while autoscalers make scaling decisions based on their existing metrics and pending pods, so they work together without disabling one another. **NAP**, is triggered when pods are unscheduable, so if a suitable node is found that Pod will be scheduled with the defined Configurable Scheduler Profile. For the **Cluster Autoscaler**, the cluster autoscaler scales nodes and is triggered when there aren't any suitable nodes available. Lastly, **VPA** optimizes resource utilization patterns in pods, so as pods are recreated they will be scheduled using the scheudler profile with the updated resource requests provided by VPA. +3. What if a resource is omitted in the `scoringStrategy` like `memory`? If a resource is omitted in the `scoringStrategy`, then that resource will not be considered in the filter or scoring cycles of the defined Configurable Scheduler Profile. If that resource should be considered, but with a reduced influence on the final score, it can be included with reduced weight. + ## Next Steps: Optimize Azure resources and test Configurable Scheduler Profiles on AKS @@ -165,3 +179,5 @@ With Configurable Scheduler Profiles, teams gain fine-grained control over pod p [scheduling-framework-interfaces]: https://kubernetes.io/docs/concepts/scheduling-eviction/scheduling-framework/#interfaces [supported-in-tree-scheduling-plugins]: https://learn.microsoft.com/azure/aks/concepts-scheduler-configuration#supported-in-tree-scheduling-plugins [node-bin-packing-configurations]: https://learn.microsoft.com/azure/aks/configure-node-binpack-scheduler?tabs=new-cluster +[datadog-state-of-containers]: https://www.datadoghq.com/state-of-containers-and-serverless/ +[cast-ai-k8s-cost-report]: https://cast.ai/reports/kubernetes-cost-benchmark/ From 323c6a203b7a6dc4766bb577d522bffc74c6dcb0 Mon Sep 17 00:00:00 2001 From: colinmixonn <109253437+colinmixonn@users.noreply.github.com> Date: Mon, 23 Mar 2026 14:46:13 -0700 Subject: [PATCH 109/164] Revise FAQ on Configurable Scheduler Profiles and autoscalers Updated FAQ section to clarify interactions between Configurable Scheduler Profiles and autoscalers, including Node Auto Provisioning, Cluster Autoscaler, and Vertical Pod Autoscaler. Enhanced explanations for resource omission in scoring strategy. --- .../index.md | 7 +++++-- 1 file changed, 5 insertions(+), 2 deletions(-) diff --git a/website/blog/2026-03-31-aks-config-scheduler-profiles-preview/index.md b/website/blog/2026-03-31-aks-config-scheduler-profiles-preview/index.md index bdd94052c..d0aec91c4 100644 --- a/website/blog/2026-03-31-aks-config-scheduler-profiles-preview/index.md +++ b/website/blog/2026-03-31-aks-config-scheduler-profiles-preview/index.md @@ -161,8 +161,11 @@ spec: ### FAQ 1. Which Bin packing strategy does AKS recommend to increase node utilization? AKS recommends using the scoring strategy `RequestedToCapacityRatio` because it provides a more granular scoring approach allowing users to define an ideal utilization curve for their respective nodes. For example, this bin packing strategy allows users to configure a target utilization of 85%. -2. How does this interact with autoscalers such as Node Auto Provisioning (NAP), Cluster Autoscaler (CAS), and Vertical Pod Autoscaler (VPA)? Configurable Scheduler Profiles change how pods are placed on nodes, while autoscalers make scaling decisions based on their existing metrics and pending pods, so they work together without disabling one another. **NAP**, is triggered when pods are unscheduable, so if a suitable node is found that Pod will be scheduled with the defined Configurable Scheduler Profile. For the **Cluster Autoscaler**, the cluster autoscaler scales nodes and is triggered when there aren't any suitable nodes available. Lastly, **VPA** optimizes resource utilization patterns in pods, so as pods are recreated they will be scheduled using the scheudler profile with the updated resource requests provided by VPA. -3. What if a resource is omitted in the `scoringStrategy` like `memory`? If a resource is omitted in the `scoringStrategy`, then that resource will not be considered in the filter or scoring cycles of the defined Configurable Scheduler Profile. If that resource should be considered, but with a reduced influence on the final score, it can be included with reduced weight. +2. How does Configurable Scheduler Profiles interact with autoscalers such as Node Auto Provisioning (NAP), Cluster Autoscaler (CAS), and Vertical Pod Autoscaler (VPA)? These componenents are complimentary to each other. Configurable Scheduler Profiles influences how pods are placed on nodes, while autoscalers make scaling decisions based on resource utilization and pending pods. + - **Node Auto Provisioning (NAP)** is triggered when pods are unscheduable. If a suitable node already exists, that pod will be scheduled with the defined Configurable Scheduler Profile. If no suitable node exists, NAP provisions new capacity, after which the pod is scheduled according to the selected profile. + - **Cluster Autoscaler (CA)** maanages node scale-up and scale-down. On scale-up, CA is triggered when there aren't any suitable nodes available for the pending pod. Using the Configurable Scheduler Profiles ensure nodes are only scaled when provisioned resources are no longer suitable. On scale-down, CA is triggered when nodes fall below utilization thresholds, the default is 50%. As active nodes are packed more efficiently, underutilized nodes become easier candidates for removal. + - **VPA** optimizes resource utilization patterns in pods. As pods are recreated with updated CPU and memory requests, they are scheduled using the configured scheduler profile, allowing placement decisions to reflect the new resource requirements. +4. What if a resource is omitted in the `scoringStrategy` like `memory`? If a resource is omitted in the `scoringStrategy`, then that resource will not be considered in the filter or scoring cycles of the defined Configurable Scheduler Profile. If that resource should be considered, but with a reduced influence on the final score, it can be included with reduced weight. ## Next Steps: Optimize Azure resources and test Configurable Scheduler Profiles on AKS From 6f31bb6cc1b720c071019f82df37807e661f6e62 Mon Sep 17 00:00:00 2001 From: colinmixonn <109253437+colinmixonn@users.noreply.github.com> Date: Mon, 23 Mar 2026 14:54:22 -0700 Subject: [PATCH 110/164] Update website/blog/2026-03-31-aks-config-scheduler-profiles-preview/index.md Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> --- .../2026-03-31-aks-config-scheduler-profiles-preview/index.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/website/blog/2026-03-31-aks-config-scheduler-profiles-preview/index.md b/website/blog/2026-03-31-aks-config-scheduler-profiles-preview/index.md index d0aec91c4..16814328a 100644 --- a/website/blog/2026-03-31-aks-config-scheduler-profiles-preview/index.md +++ b/website/blog/2026-03-31-aks-config-scheduler-profiles-preview/index.md @@ -12,7 +12,7 @@ On average, Kubernetes clusters reach [10% CPU utilization][cast-ai-k8s-cost-rep This blog details how the default Kubernetes scheduler places pods, limitations, and provides best-practice recommendations to increase node utilization for your workloads using Configurable Scheduler Profiles on AKS. -1. [How does kube-scheduler work?](how-does-the-default-kubernetes-scheduler-place-pods) +1. [How does kube-scheduler work?](#how-does-the-default-kubernetes-scheduler-place-pods) 2. [How to increase AKS cluster CPU utilization with RequestedToCapacityRatio](#increase-aks-cluster-cpu-utilization) 3. [How to increase AKS cluster GPU and CPU utilization with MostAllocated](#increase-aks-cluster-gpu-utilization) From 21490f950e401ea0d0a37e0bc468647521c29f68 Mon Sep 17 00:00:00 2001 From: colinmixonn <109253437+colinmixonn@users.noreply.github.com> Date: Mon, 23 Mar 2026 14:55:46 -0700 Subject: [PATCH 111/164] Apply suggestions from code review Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> --- .../index.md | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/website/blog/2026-03-31-aks-config-scheduler-profiles-preview/index.md b/website/blog/2026-03-31-aks-config-scheduler-profiles-preview/index.md index 16814328a..3c2533a6a 100644 --- a/website/blog/2026-03-31-aks-config-scheduler-profiles-preview/index.md +++ b/website/blog/2026-03-31-aks-config-scheduler-profiles-preview/index.md @@ -40,7 +40,7 @@ When filtering and scoring nodes, for the pending pod, the default scheduler con ### Limitations of the default Kubernetes scheduler -The default scheduler is primarily designed for general-purpose workloads that prioritizes nodes with the most avaialable resources using the _LeastAllocated_ scoring strategy. This spreads pods across nodes, even when they could safely be packed more densely. While this works well for many services, the default scheduling criteria, and their fixed priority order, are not suitable for workloads that demand optimizing GPU and CPU utilization. In these scenarios, spreading pods across nodes can lead to fragmented resources, underutilized GPUs, and increased infrastructure cost. +The default scheduler is primarily designed for general-purpose workloads that prioritizes nodes with the most available resources using the _LeastAllocated_ scoring strategy. This spreads pods across nodes, even when they could safely be packed more densely. While this works well for many services, the default scheduling criteria, and their fixed priority order, are not suitable for workloads that demand optimizing GPU and CPU utilization. In these scenarios, spreading pods across nodes can lead to fragmented resources, underutilized GPUs, and increased infrastructure cost. Today, the default scheduler on AKS lacks the flexibility for users to change which criteria should be prioritized, or ignored, in the scheduling cycle on a per cluster basis. This rigidity often forces users to either accept suboptimal placement or manage a separate custom scheduler, both of which increase operational complexity. Starting with Kubernetes v1.33, AKS introduces Configurable Scheduler Profiles, enabling customized scheduler behavior without maintaining a separate scheduler. Now, users can adjust the `NodeResourcesFit` plugin from the default configuration to favor nodes with higher utilization to achieve more efficient bin‑packing and reduce infrastructure cost. @@ -161,11 +161,11 @@ spec: ### FAQ 1. Which Bin packing strategy does AKS recommend to increase node utilization? AKS recommends using the scoring strategy `RequestedToCapacityRatio` because it provides a more granular scoring approach allowing users to define an ideal utilization curve for their respective nodes. For example, this bin packing strategy allows users to configure a target utilization of 85%. -2. How does Configurable Scheduler Profiles interact with autoscalers such as Node Auto Provisioning (NAP), Cluster Autoscaler (CAS), and Vertical Pod Autoscaler (VPA)? These componenents are complimentary to each other. Configurable Scheduler Profiles influences how pods are placed on nodes, while autoscalers make scaling decisions based on resource utilization and pending pods. - - **Node Auto Provisioning (NAP)** is triggered when pods are unscheduable. If a suitable node already exists, that pod will be scheduled with the defined Configurable Scheduler Profile. If no suitable node exists, NAP provisions new capacity, after which the pod is scheduled according to the selected profile. - - **Cluster Autoscaler (CA)** maanages node scale-up and scale-down. On scale-up, CA is triggered when there aren't any suitable nodes available for the pending pod. Using the Configurable Scheduler Profiles ensure nodes are only scaled when provisioned resources are no longer suitable. On scale-down, CA is triggered when nodes fall below utilization thresholds, the default is 50%. As active nodes are packed more efficiently, underutilized nodes become easier candidates for removal. +2. How does Configurable Scheduler Profiles interact with autoscalers such as Node Auto Provisioning (NAP), Cluster Autoscaler (CAS), and Vertical Pod Autoscaler (VPA)? These components are complimentary to each other. Configurable Scheduler Profiles influences how pods are placed on nodes, while autoscalers make scaling decisions based on resource utilization and pending pods. + - **Node Auto Provisioning (NAP)** is triggered when pods are unschedulable. If a suitable node already exists, that pod will be scheduled with the defined Configurable Scheduler Profile. If no suitable node exists, NAP provisions new capacity, after which the pod is scheduled according to the selected profile. + - **Cluster Autoscaler (CA)** manages node scale-up and scale-down. On scale-up, CA is triggered when there aren't any suitable nodes available for the pending pod. Using the Configurable Scheduler Profiles ensure nodes are only scaled when provisioned resources are no longer suitable. On scale-down, CA is triggered when nodes fall below utilization thresholds, the default is 50%. As active nodes are packed more efficiently, underutilized nodes become easier candidates for removal. - **VPA** optimizes resource utilization patterns in pods. As pods are recreated with updated CPU and memory requests, they are scheduled using the configured scheduler profile, allowing placement decisions to reflect the new resource requirements. -4. What if a resource is omitted in the `scoringStrategy` like `memory`? If a resource is omitted in the `scoringStrategy`, then that resource will not be considered in the filter or scoring cycles of the defined Configurable Scheduler Profile. If that resource should be considered, but with a reduced influence on the final score, it can be included with reduced weight. +3. What if a resource is omitted in the `scoringStrategy` like `memory`? If a resource is omitted in the `scoringStrategy`, then that resource will not be considered in the filter or scoring cycles of the defined Configurable Scheduler Profile. If that resource should be considered, but with a reduced influence on the final score, it can be included with reduced weight. ## Next Steps: Optimize Azure resources and test Configurable Scheduler Profiles on AKS From 274c45c7167d5e42679cb0930a98f8a156115e67 Mon Sep 17 00:00:00 2001 From: colinmixonn <109253437+colinmixonn@users.noreply.github.com> Date: Mon, 23 Mar 2026 15:35:48 -0700 Subject: [PATCH 112/164] Revise scheduler profiles and scoring strategy details Updated the explanation of the Kubernetes scheduler's filtering and scoring phases, added a diagram, and clarified the benefits of the RequestedToCapacityRatio scoring strategy for CPU utilization. --- .../index.md | 18 ++++++++---------- 1 file changed, 8 insertions(+), 10 deletions(-) diff --git a/website/blog/2026-03-31-aks-config-scheduler-profiles-preview/index.md b/website/blog/2026-03-31-aks-config-scheduler-profiles-preview/index.md index 3c2533a6a..48b3b4697 100644 --- a/website/blog/2026-03-31-aks-config-scheduler-profiles-preview/index.md +++ b/website/blog/2026-03-31-aks-config-scheduler-profiles-preview/index.md @@ -26,11 +26,7 @@ The Kubernetes scheduler operates in two cycles: a synchronous scheduling cycle 1. **Filtering** phase removes unsuitable nodes based on hard and soft constraints. 2. **Scoring** phase calculates a score for the remaining nodes; ultimately, the most suitable node has the highest score. -Once a node is selected, the binding cycle can process multiple pods in parallel. During this phase, the scheduler attempts to bind the pod to the chosen node. If binding a pod to a node fails, the scheduler tries the node with the next highest score. - -![Diagram of the kube-scheduler workflow showing pods entering the scheduling cycle and binding cycle to assign a pod on a node](./kube-scheduler-scheduling-phases-diagram.png) - -When filtering and scoring nodes, for the pending pod, the default scheduler considers several hard and soft constraints with predefined weights, including (but not limited to): +Once a node is selected, the binding cycle can process multiple pods in parallel. During this phase, the scheduler attempts to bind the pod to the chosen node. If binding a pod to a node fails, the scheduler tries the node with the next highest score. When filtering and scoring nodes, the default scheduler considers several hard and soft constraints with predefined weights, including (but not limited to): 1. Resource requirements (CPU, memory) 2. Node affinity/anti-affinity @@ -38,6 +34,8 @@ When filtering and scoring nodes, for the pending pod, the default scheduler con 4. Taints and tolerations 5. TopologySpreadConstraints +![Diagram of the kube-scheduler workflow showing pods entering the scheduling cycle and binding cycle to assign a pod on a node](./kube-scheduler-scheduling-phases-diagram.png) + ### Limitations of the default Kubernetes scheduler The default scheduler is primarily designed for general-purpose workloads that prioritizes nodes with the most available resources using the _LeastAllocated_ scoring strategy. This spreads pods across nodes, even when they could safely be packed more densely. While this works well for many services, the default scheduling criteria, and their fixed priority order, are not suitable for workloads that demand optimizing GPU and CPU utilization. In these scenarios, spreading pods across nodes can lead to fragmented resources, underutilized GPUs, and increased infrastructure cost. @@ -55,16 +53,15 @@ A profile is a set of one or more in-tree scheduling plugins and configurations ## Increase Node Utilization and Operator Control Configurable Scheduler Profiles using the `NodeResourcesFit` plugin shows a visible consolidation pattern that differs from the default scheduler's logic. As a result, platform engineers gain more control and resources are used more efficiently when using AKS. + ![Table showing increased node utilization with the node bin packing scheduler profiles versus the pod distribution using the default scheduler](./default-config-scheduler-comparison.png) ### Increase AKS Cluster CPU Utilization +`RequestedToCapacityRatio` scores nodes based on the ratio of requested resources to total node capacity after the pod is hypothetically placed. The `RequestedToCapacityRatio` scoring strategy enables more fine-grained CPU bin‑packing by allowing operators to define an ideal utilization curve for their nodes rather than simply preferring the most or least utilized nodes. Instead of aggressively packing pods onto the fullest node, this approach scores nodes based on how closely their post‑placement utilization aligns with a target range ensuring high efficiency without oversaturation. -Scoring Strategy - RequestedToCapacityRatio - -For example, this bin packing strategy allows users to configure a target utilization of 85%. +For CPU‑based workloads, this results in denser placement on active nodes while preserving headroom for bursts, background processes, and system components. By shaping the scoring curve to target a range of 50-85% CPU utilization, operators can explicitly maximize privisioned nodes while preserving headroom. Visit official bin packing [documentation][bin-packing-scoring] for more details on how a nodes score is calculated for a given set of values using `RequestedToCapacityRatio`. -AKS recommends using the scoring strategy `RequestedToCapacityRatio` because it provides a more granular scoring approach allowing users to define an ideal utilization curve for their respective nodes. -**This scheduler configuration ensures nodes are not oversaturated.** +**This bin packing profile favor nodes within a utilization band of 50-85%, avoids empty nodes, and severely deprioritize nearly full nodes at 90% utilization or more, ensuring nodes are not oversaturated. Given this level of configuration detail, `RequestedtoCapacityRatio` is the recommended scoring strategy for node bin‑packing on AKS for production clusters.** :::note Adjust resource weights, utilization thresholds, and plugin parameters to match your VM SKUs, workload patterns, and cluster topology. @@ -184,3 +181,4 @@ With Configurable Scheduler Profiles, teams gain fine-grained control over pod p [node-bin-packing-configurations]: https://learn.microsoft.com/azure/aks/configure-node-binpack-scheduler?tabs=new-cluster [datadog-state-of-containers]: https://www.datadoghq.com/state-of-containers-and-serverless/ [cast-ai-k8s-cost-report]: https://cast.ai/reports/kubernetes-cost-benchmark/ +[bin-packing-scoring]: https://kubernetes.io/docs/concepts/scheduling-eviction/resource-bin-packing/ From af2fb7870a238d2974e6a4f28b44ca9180756b41 Mon Sep 17 00:00:00 2001 From: colinmixonn <109253437+colinmixonn@users.noreply.github.com> Date: Mon, 23 Mar 2026 15:53:17 -0700 Subject: [PATCH 113/164] Refine blog content on AKS Configurable Scheduler Profiles Revised text for clarity and consistency regarding Kubernetes cluster CPU utilization and Configurable Scheduler Profiles. Updated YAML examples and added notes for resource adjustments. --- .../index.md | 26 ++++++++++--------- 1 file changed, 14 insertions(+), 12 deletions(-) diff --git a/website/blog/2026-03-31-aks-config-scheduler-profiles-preview/index.md b/website/blog/2026-03-31-aks-config-scheduler-profiles-preview/index.md index 48b3b4697..964d52eed 100644 --- a/website/blog/2026-03-31-aks-config-scheduler-profiles-preview/index.md +++ b/website/blog/2026-03-31-aks-config-scheduler-profiles-preview/index.md @@ -6,7 +6,7 @@ authors: [colin-mixon] tags: [ai, performance, scheduler, best-practices, cost] --- -On average, Kubernetes clusters reach [10% CPU utilization][cast-ai-k8s-cost-report] and most Kubernetes containers use less than [25% of their requested CPU][datadog-state-of-containers]. This data signals that underutilized resources are materially contributing to increased infrastructure cost. While there are many factors that impact node utilization, as a core component of the Kubernetes control plane, the kube-scheduler has a big influence on node utilization. With the introduction of Configurable Scheduler Profiles on AKS, customers now have the opportunity to increase their node utilization across CPU and GPU resources and optimize their costs with access to fine-grained pod scheduling strategies. +As reported by CastAI, the average Kubernetes clusters only reaches [10% CPU utilization][cast-ai-k8s-cost-report] and Datadog finds most Kubernetes containers use less than [25% of their requested CPU][datadog-state-of-containers]. This data signals that underutilized resources are materially contributing to increased infrastructure cost. While there are many factors that impact node utilization, as a core component of the Kubernetes control plane, the kube-scheduler has a big influence on node utilization. With the introduction of Configurable Scheduler Profiles on AKS, customers now have the opportunity to increase their node utilization across CPU and GPU resources and optimize their costs with access to fine-grained pod scheduling strategies. [Configurable Scheduler Profiles on AKS][concepts-scheduler-configuration] allows customers to increase node utilization by configuring their own scheduling logic: enabling specific policies, adjusting policy priorities, tuning parameter weights, and choosing when each policy runs (for example, during PreFilter, Filter, or Score). @@ -22,7 +22,8 @@ This blog details how the default Kubernetes scheduler places pods, limitations, ## How does the default Kubernetes scheduler place pods? -The Kubernetes scheduler operates in two cycles: a synchronous scheduling cycle and an asynchronous binding cycle. The scheduling cycle has two sub-phases, filtering and scoring, and only manages one pod at a time. +The Kubernetes scheduler operates in two cycles: a synchronous scheduling cycle and an asynchronous binding cycle. The scheduling cycle has two sub-phases, filtering and scoring, and only manages one pod at a time. + 1. **Filtering** phase removes unsuitable nodes based on hard and soft constraints. 2. **Scoring** phase calculates a score for the remaining nodes; ultimately, the most suitable node has the highest score. @@ -48,7 +49,7 @@ Today, the default scheduler on AKS lacks the flexibility for users to change wh ![Architecture diagram showing how Configurable Scheduler Profiles use a CRD and controller to reconcile user-defined profiles with the kube-scheduler deployment](./config-scheduler-profiles.png) -A profile is a set of one or more in-tree scheduling plugins and configurations that dictate how to schedule a pod. AKS supports 18 in-tree Kubernetes [scheduling plugins][supported-in-tree-scheduling-plugins]. +A profile is a set of one or more in-tree scheduling plugins and configurations that dictate how to schedule a pod. AKS supports 18 in-tree Kubernetes [scheduling plugins][supported-in-tree-scheduling-plugins]. ## Increase Node Utilization and Operator Control @@ -57,14 +58,15 @@ Configurable Scheduler Profiles using the `NodeResourcesFit` plugin shows a visi ![Table showing increased node utilization with the node bin packing scheduler profiles versus the pod distribution using the default scheduler](./default-config-scheduler-comparison.png) ### Increase AKS Cluster CPU Utilization -`RequestedToCapacityRatio` scores nodes based on the ratio of requested resources to total node capacity after the pod is hypothetically placed. The `RequestedToCapacityRatio` scoring strategy enables more fine-grained CPU bin‑packing by allowing operators to define an ideal utilization curve for their nodes rather than simply preferring the most or least utilized nodes. Instead of aggressively packing pods onto the fullest node, this approach scores nodes based on how closely their post‑placement utilization aligns with a target range ensuring high efficiency without oversaturation. -For CPU‑based workloads, this results in denser placement on active nodes while preserving headroom for bursts, background processes, and system components. By shaping the scoring curve to target a range of 50-85% CPU utilization, operators can explicitly maximize privisioned nodes while preserving headroom. Visit official bin packing [documentation][bin-packing-scoring] for more details on how a nodes score is calculated for a given set of values using `RequestedToCapacityRatio`. +`RequestedToCapacityRatio` scores nodes based on the ratio of requested resources to total node capacity after the pod is _hypothetically_ placed. This strategy enables more fine-grained bin‑packing by allowing operators to define an ideal utilization curve for their nodes rather than simply preferring the most or least utilized nodes. + +By shaping the scoring curve to target a range of 50-85% CPU utilization, operators can increase pod density on privisioned nodes while preserving headroom for bursts, background processes, and system components in CPU-based workloads. [Configure node bin-packing][configure-requested-to-capacity] using the RequestedtoCapacity strategy to improve utilization and reduce infrastructure costs. -**This bin packing profile favor nodes within a utilization band of 50-85%, avoids empty nodes, and severely deprioritize nearly full nodes at 90% utilization or more, ensuring nodes are not oversaturated. Given this level of configuration detail, `RequestedtoCapacityRatio` is the recommended scoring strategy for node bin‑packing on AKS for production clusters.** +**This bin packing profile is configured to favor nodes within a utilization band of 50-85%, avoiding empty nodes, and severely deprioritizing nearly full nodes at 90% utilization or more, to limit versaturated nodes. Given this level of configuration detail, `RequestedtoCapacityRatio` is the recommended scoring strategy for node bin‑packing on AKS for production clusters.** :::note -Adjust resource weights, utilization thresholds, and plugin parameters to match your VM SKUs, workload patterns, and cluster topology. +Scoring strategy can be used for GPU alos. Adjust resources, resource weights, utilization thresholds, and plugin parameters to match your VM SKUs, workload patterns, and cluster topology. ::: ```yaml @@ -92,7 +94,7 @@ spec: scoringStrategy: type: RequestedToCapacityRatio resources: - - name: cpu + - name: cpu // weight: 8 - name: memory weight: 1 @@ -114,12 +116,12 @@ spec: ### Increase AKS Cluster GPU Utilization -Additionally, customers running GPU-dependent applications like batch jobs will benefit from improved bin-packing and increased GPU utilization. For example, scheduling jobs on nodes with a higher relative GPU utilization can reduce costs while maintaining performance. +Additionally, customers running GPU-dependent applications like batch jobs will benefit from improved bin-packing and increased GPU utilization. For example, scheduling jobs on nodes with a higher relative GPU utilization can reduce costs while maintaining performance. [Configure node bin-packing][configure-most-allocated] using the MostAllocated strategy to improve utilization reduce infrastructure costs. **This scheduler configuration maximizes provisioned GPU resource by consolidating smaller jobs onto fewer nodes, lowering the operational cost of underutilized resources without sacrificing performance.** :::note -Adjust resource weights, utilization thresholds, and plugin parameters to match your VM SKUs, workload patterns, and cluster topology. +Adjust resources, resource weights, utilization thresholds, and plugin parameters to match your VM SKUs, workload patterns, and cluster topology. ::: ```yaml @@ -164,7 +166,6 @@ spec: - **VPA** optimizes resource utilization patterns in pods. As pods are recreated with updated CPU and memory requests, they are scheduled using the configured scheduler profile, allowing placement decisions to reflect the new resource requirements. 3. What if a resource is omitted in the `scoringStrategy` like `memory`? If a resource is omitted in the `scoringStrategy`, then that resource will not be considered in the filter or scoring cycles of the defined Configurable Scheduler Profile. If that resource should be considered, but with a reduced influence on the final score, it can be included with reduced weight. - ## Next Steps: Optimize Azure resources and test Configurable Scheduler Profiles on AKS With Configurable Scheduler Profiles, teams gain fine-grained control over pod placement strategies like bin-packing, topology distribution, and resource-based scoring that directly addresses challenges related to application resilience and resource utilization for their AKS clusters. By leveraging these scheduling plugins, AKS users can ensure their workloads make full use of available GPU capacity, reduce idle costs, and avoid costly overprovisioning. This not only improves ROI but also accelerates development by allowing more jobs to run concurrently and reliably. @@ -181,4 +182,5 @@ With Configurable Scheduler Profiles, teams gain fine-grained control over pod p [node-bin-packing-configurations]: https://learn.microsoft.com/azure/aks/configure-node-binpack-scheduler?tabs=new-cluster [datadog-state-of-containers]: https://www.datadoghq.com/state-of-containers-and-serverless/ [cast-ai-k8s-cost-report]: https://cast.ai/reports/kubernetes-cost-benchmark/ -[bin-packing-scoring]: https://kubernetes.io/docs/concepts/scheduling-eviction/resource-bin-packing/ +[configure-requested-to-capacity]: https://learn.microsoft.com/azure/aks/configure-node-binpack-scheduler?tabs=new-cluster#configure-node-bin-packing-with-requestedtocapacity-plugin +[configure-most-allocated]: https://learn.microsoft.com/azure/aks/configure-node-binpack-scheduler?tabs=new-cluster#configure-node-bin-packing-with-mostallocated-plugin From e747ad8ea87b0f7b33bfa88bbd3cba7b0c920d40 Mon Sep 17 00:00:00 2001 From: colinmixonn <109253437+colinmixonn@users.noreply.github.com> Date: Mon, 23 Mar 2026 16:23:13 -0700 Subject: [PATCH 114/164] Apply suggestions from code review Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> --- .../index.md | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/website/blog/2026-03-31-aks-config-scheduler-profiles-preview/index.md b/website/blog/2026-03-31-aks-config-scheduler-profiles-preview/index.md index 964d52eed..7455ba8f9 100644 --- a/website/blog/2026-03-31-aks-config-scheduler-profiles-preview/index.md +++ b/website/blog/2026-03-31-aks-config-scheduler-profiles-preview/index.md @@ -6,7 +6,7 @@ authors: [colin-mixon] tags: [ai, performance, scheduler, best-practices, cost] --- -As reported by CastAI, the average Kubernetes clusters only reaches [10% CPU utilization][cast-ai-k8s-cost-report] and Datadog finds most Kubernetes containers use less than [25% of their requested CPU][datadog-state-of-containers]. This data signals that underutilized resources are materially contributing to increased infrastructure cost. While there are many factors that impact node utilization, as a core component of the Kubernetes control plane, the kube-scheduler has a big influence on node utilization. With the introduction of Configurable Scheduler Profiles on AKS, customers now have the opportunity to increase their node utilization across CPU and GPU resources and optimize their costs with access to fine-grained pod scheduling strategies. +As reported by CastAI, on average Kubernetes clusters only reach [10% CPU utilization][cast-ai-k8s-cost-report] and Datadog finds most Kubernetes containers use less than [25% of their requested CPU][datadog-state-of-containers]. This data signals that underutilized resources are materially contributing to increased infrastructure cost. While there are many factors that impact node utilization, as a core component of the Kubernetes control plane, the kube-scheduler has a big influence on node utilization. With the introduction of Configurable Scheduler Profiles on AKS, customers now have the opportunity to increase their node utilization across CPU and GPU resources and optimize their costs with access to fine-grained pod scheduling strategies. [Configurable Scheduler Profiles on AKS][concepts-scheduler-configuration] allows customers to increase node utilization by configuring their own scheduling logic: enabling specific policies, adjusting policy priorities, tuning parameter weights, and choosing when each policy runs (for example, during PreFilter, Filter, or Score). @@ -61,12 +61,12 @@ Configurable Scheduler Profiles using the `NodeResourcesFit` plugin shows a visi `RequestedToCapacityRatio` scores nodes based on the ratio of requested resources to total node capacity after the pod is _hypothetically_ placed. This strategy enables more fine-grained bin‑packing by allowing operators to define an ideal utilization curve for their nodes rather than simply preferring the most or least utilized nodes. -By shaping the scoring curve to target a range of 50-85% CPU utilization, operators can increase pod density on privisioned nodes while preserving headroom for bursts, background processes, and system components in CPU-based workloads. [Configure node bin-packing][configure-requested-to-capacity] using the RequestedtoCapacity strategy to improve utilization and reduce infrastructure costs. +By shaping the scoring curve to target a range of 50-85% CPU utilization, operators can increase pod density on provisioned nodes while preserving headroom for bursts, background processes, and system components in CPU-based workloads. [Configure node bin-packing][configure-requested-to-capacity] using the `RequestedToCapacityRatio` strategy to improve utilization and reduce infrastructure costs. -**This bin packing profile is configured to favor nodes within a utilization band of 50-85%, avoiding empty nodes, and severely deprioritizing nearly full nodes at 90% utilization or more, to limit versaturated nodes. Given this level of configuration detail, `RequestedtoCapacityRatio` is the recommended scoring strategy for node bin‑packing on AKS for production clusters.** +**This bin packing profile is configured to favor nodes within a utilization band of 50-85%, avoiding empty nodes, and severely deprioritizing nearly full nodes at 90% utilization or more, to limit oversaturated nodes. Given this level of configuration detail, `RequestedToCapacityRatio` is the recommended scoring strategy for node bin‑packing on AKS for production clusters.** :::note -Scoring strategy can be used for GPU alos. Adjust resources, resource weights, utilization thresholds, and plugin parameters to match your VM SKUs, workload patterns, and cluster topology. +Scoring strategy can be used for GPU also. Adjust resources, resource weights, utilization thresholds, and plugin parameters to match your VM SKUs, workload patterns, and cluster topology. ::: ```yaml @@ -94,7 +94,7 @@ spec: scoringStrategy: type: RequestedToCapacityRatio resources: - - name: cpu // + - name: cpu weight: 8 - name: memory weight: 1 @@ -116,7 +116,7 @@ spec: ### Increase AKS Cluster GPU Utilization -Additionally, customers running GPU-dependent applications like batch jobs will benefit from improved bin-packing and increased GPU utilization. For example, scheduling jobs on nodes with a higher relative GPU utilization can reduce costs while maintaining performance. [Configure node bin-packing][configure-most-allocated] using the MostAllocated strategy to improve utilization reduce infrastructure costs. +Additionally, customers running GPU-dependent applications like batch jobs will benefit from improved bin-packing and increased GPU utilization. For example, scheduling jobs on nodes with a higher relative GPU utilization can reduce costs while maintaining performance. [Configure node bin-packing][configure-most-allocated] using the MostAllocated strategy to improve utilization and reduce infrastructure costs. **This scheduler configuration maximizes provisioned GPU resource by consolidating smaller jobs onto fewer nodes, lowering the operational cost of underutilized resources without sacrificing performance.** From e15dfee3c5bd17a09ff5a82c24a7daafae8f343c Mon Sep 17 00:00:00 2001 From: colinmixonn <109253437+colinmixonn@users.noreply.github.com> Date: Mon, 23 Mar 2026 17:02:09 -0700 Subject: [PATCH 115/164] Revise blog on AKS Configurable Scheduler Profiles Updated the blog post to clarify the benefits of Configurable Scheduler Profiles on AKS, including improvements in CPU and GPU utilization. Enhanced explanations of scheduling strategies and their impact on resource efficiency and cost optimization. --- .../index.md | 38 +++++++++++++------ 1 file changed, 26 insertions(+), 12 deletions(-) diff --git a/website/blog/2026-03-31-aks-config-scheduler-profiles-preview/index.md b/website/blog/2026-03-31-aks-config-scheduler-profiles-preview/index.md index 7455ba8f9..50ebe59a4 100644 --- a/website/blog/2026-03-31-aks-config-scheduler-profiles-preview/index.md +++ b/website/blog/2026-03-31-aks-config-scheduler-profiles-preview/index.md @@ -6,15 +6,15 @@ authors: [colin-mixon] tags: [ai, performance, scheduler, best-practices, cost] --- -As reported by CastAI, on average Kubernetes clusters only reach [10% CPU utilization][cast-ai-k8s-cost-report] and Datadog finds most Kubernetes containers use less than [25% of their requested CPU][datadog-state-of-containers]. This data signals that underutilized resources are materially contributing to increased infrastructure cost. While there are many factors that impact node utilization, as a core component of the Kubernetes control plane, the kube-scheduler has a big influence on node utilization. With the introduction of Configurable Scheduler Profiles on AKS, customers now have the opportunity to increase their node utilization across CPU and GPU resources and optimize their costs with access to fine-grained pod scheduling strategies. +As reported by CastAI, on average Kubernetes clusters only reach [10% CPU utilization][cast-ai-k8s-cost-report] and Datadog finds most Kubernetes containers use less than [25% of their requested CPU][datadog-state-of-containers]. This data signals that underutilized resources are materially contributing to increased infrastructure cost. While there are many factors that impact node utilization, as a core component of the Kubernetes control plane, the kube-scheduler has a big influence on node utilization. -[Configurable Scheduler Profiles on AKS][concepts-scheduler-configuration] allows customers to increase node utilization by configuring their own scheduling logic: enabling specific policies, adjusting policy priorities, tuning parameter weights, and choosing when each policy runs (for example, during PreFilter, Filter, or Score). +With the introduction of[Configurable Scheduler Profiles][concepts-scheduler-configuration] on AKS, customers now have the opportunity to increase their node utilization across CPU and GPU resources and optimize their costs with access to fine-grained pod scheduling strategies by configuring their own scheduling logic: enabling specific policies, adjusting policy priorities, tuning parameter weights, and choosing when each policy runs (for example, during PreFilter, Filter, or Score). This blog details how the default Kubernetes scheduler places pods, limitations, and provides best-practice recommendations to increase node utilization for your workloads using Configurable Scheduler Profiles on AKS. 1. [How does kube-scheduler work?](#how-does-the-default-kubernetes-scheduler-place-pods) -2. [How to increase AKS cluster CPU utilization with RequestedToCapacityRatio](#increase-aks-cluster-cpu-utilization) -3. [How to increase AKS cluster GPU and CPU utilization with MostAllocated](#increase-aks-cluster-gpu-utilization) +2. [How to increase AKS cluster CPU utilization with Configurable Scheduler](#increase-aks-cpu-utilization) +3. [How to increase AKS cluster GPU and CPU utilization with Configurable Scheduler](#increase-aks-gpu-utilization) @@ -53,11 +53,15 @@ A profile is a set of one or more in-tree scheduling plugins and configurations ## Increase Node Utilization and Operator Control -Configurable Scheduler Profiles using the `NodeResourcesFit` plugin shows a visible consolidation pattern that differs from the default scheduler's logic. As a result, platform engineers gain more control and resources are used more efficiently when using AKS. +In this simple scale-out scenario, manually increasing replicas from 8 to 30 with identical pod specs, the default scheduler distributes pods evenly across nodes. The Configurable Scheduler Profiles using the `NodeResourcesFit` plugin are introduced shows a visible consolidation pattern that differs from the default scheduler's logic. Instead of spreading pods evenly, the scheduler begins to intentionally concentrate workloads onto fewer nodes. This shift occurs without changing the workload, node size, or autoscaling behavior - only the scheduler’s scoring logic. + +While this experiment uses intentionally simple, CPU-bound containers to isolate scheduling behavior, the placement patterns observed here generalize to more complex workloads where consolidation and utilization efficiency matter. This change in distribution shape enables downstream efficiencies: improved control for platform engineers, efficient resource usage, and cost optimization that are difficult to achieve when pods are evenly spread. ![Table showing increased node utilization with the node bin packing scheduler profiles versus the pod distribution using the default scheduler](./default-config-scheduler-comparison.png) -### Increase AKS Cluster CPU Utilization +The key takeaway is not that each profile expresses a distinct scheduling intent. The next two sections detail how MostAllocated and RequestedToCapacityRatio achieve these outcomes. + +### Increase AKS CPU Utilization `RequestedToCapacityRatio` scores nodes based on the ratio of requested resources to total node capacity after the pod is _hypothetically_ placed. This strategy enables more fine-grained bin‑packing by allowing operators to define an ideal utilization curve for their nodes rather than simply preferring the most or least utilized nodes. @@ -114,11 +118,13 @@ spec: score: 0 ``` -### Increase AKS Cluster GPU Utilization +### Increase AKS GPU Utilization -Additionally, customers running GPU-dependent applications like batch jobs will benefit from improved bin-packing and increased GPU utilization. For example, scheduling jobs on nodes with a higher relative GPU utilization can reduce costs while maintaining performance. [Configure node bin-packing][configure-most-allocated] using the MostAllocated strategy to improve utilization and reduce infrastructure costs. +`MostAllocated` scores nodes based on their current resource utilization, favoring nodes that are already more heavily utilized. Unlike `RequestedToCapacityRatio`, it does not consider node capacity in node scoring, making it more suitable for an agressive cost-optimization scheduling strategy. When paired with MostAllocated, `NodeResourcesBalancedAllocation` complements the behoavior because it encourages pod placement on nodes with user-defined proportional utilization, helping reduce bottlenecks caused by asymmetric resource pressure. -**This scheduler configuration maximizes provisioned GPU resource by consolidating smaller jobs onto fewer nodes, lowering the operational cost of underutilized resources without sacrificing performance.** +When combined, these plugins favor GPU‑bound nodes with balanced CPU and memory usage over nodes with large amounts of unused memory or fragmented resources. This results in more efficient GPU placement and fewer partially utilized nodes.[Configure node bin-packing][configure-most-allocated] using the MostAllocated strategy to improve utilization and reduce infrastructure costs. + +**This scheduler configuration maximizes GPU utilization by consolidating smaller jobs onto fewer nodes, reducing idle accelerator capacity while maintaining reasonable CPU and memory balance.** :::note Adjust resources, resource weights, utilization thresholds, and plugin parameters to match your VM SKUs, workload patterns, and cluster topology. @@ -138,7 +144,6 @@ spec: plugins: multiPoint: enabled: - - name: ImageLocality - name: NodeResourcesFit - name: NodeResourcesBalancedAllocation disabled: @@ -151,10 +156,19 @@ spec: scoringStrategy: type: MostAllocated resources: + - name: nvidia.com/gpu + weight: 8 - name: cpu weight: 1 - - name: nvidia.com/gpu - weight: 5 + - name: NodeResourcesBalancedAllocation + args: + apiVersion: kubescheduler.config.k8s.io/v1 + kind: NodeResourcesBalancedAllocationArgs + resources: + - name: cpu + weight: 1 + - name: memory + weight: 1 ``` ### FAQ From 06f870b3b204a4bf2292ad10da323e013cbc0083 Mon Sep 17 00:00:00 2001 From: colinmixonn <109253437+colinmixonn@users.noreply.github.com> Date: Mon, 23 Mar 2026 17:46:52 -0700 Subject: [PATCH 116/164] Enhance clarity on AKS scheduling strategies Clarify the explanation of the 'RequestedToCapacityRatio' and 'MostAllocated' scheduling strategies for AKS, emphasizing their impact on CPU and GPU utilization. --- .../2026-03-31-aks-config-scheduler-profiles-preview/index.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/website/blog/2026-03-31-aks-config-scheduler-profiles-preview/index.md b/website/blog/2026-03-31-aks-config-scheduler-profiles-preview/index.md index 50ebe59a4..d0e49b365 100644 --- a/website/blog/2026-03-31-aks-config-scheduler-profiles-preview/index.md +++ b/website/blog/2026-03-31-aks-config-scheduler-profiles-preview/index.md @@ -61,7 +61,7 @@ While this experiment uses intentionally simple, CPU-bound containers to isolate The key takeaway is not that each profile expresses a distinct scheduling intent. The next two sections detail how MostAllocated and RequestedToCapacityRatio achieve these outcomes. -### Increase AKS CPU Utilization +### Increase AKS CPU Utilization `RequestedToCapacityRatio` scores nodes based on the ratio of requested resources to total node capacity after the pod is _hypothetically_ placed. This strategy enables more fine-grained bin‑packing by allowing operators to define an ideal utilization curve for their nodes rather than simply preferring the most or least utilized nodes. @@ -120,7 +120,7 @@ spec: ### Increase AKS GPU Utilization -`MostAllocated` scores nodes based on their current resource utilization, favoring nodes that are already more heavily utilized. Unlike `RequestedToCapacityRatio`, it does not consider node capacity in node scoring, making it more suitable for an agressive cost-optimization scheduling strategy. When paired with MostAllocated, `NodeResourcesBalancedAllocation` complements the behoavior because it encourages pod placement on nodes with user-defined proportional utilization, helping reduce bottlenecks caused by asymmetric resource pressure. +`MostAllocated` scores nodes based on their current resource utilization, favoring nodes that are already more heavily utilized. Unlike `RequestedToCapacityRatio`, it does not consider node capacity in node scoring, making it more suitable for an agressive cost-optimization scheduling strategy. When paired with MostAllocated, `NodeResourcesBalancedAllocation` complements the behoavior because it encourages pod placement on nodes with user-defined proportional utilization, helping reduce bottlenecks caused by asymmetric resource pressure. When combined, these plugins favor GPU‑bound nodes with balanced CPU and memory usage over nodes with large amounts of unused memory or fragmented resources. This results in more efficient GPU placement and fewer partially utilized nodes.[Configure node bin-packing][configure-most-allocated] using the MostAllocated strategy to improve utilization and reduce infrastructure costs. From 3ff5fdadd4f95b5561690751f4c0558211104662 Mon Sep 17 00:00:00 2001 From: colinmixonn <109253437+colinmixonn@users.noreply.github.com> Date: Mon, 23 Mar 2026 17:47:23 -0700 Subject: [PATCH 117/164] Apply suggestions from code review Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> --- .../index.md | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/website/blog/2026-03-31-aks-config-scheduler-profiles-preview/index.md b/website/blog/2026-03-31-aks-config-scheduler-profiles-preview/index.md index d0e49b365..c0fa0e4be 100644 --- a/website/blog/2026-03-31-aks-config-scheduler-profiles-preview/index.md +++ b/website/blog/2026-03-31-aks-config-scheduler-profiles-preview/index.md @@ -1,6 +1,6 @@ --- title: "AKS Configurable Scheduler Profiles (preview)" -description: "Optimize GPU and CPU utilization and align pod placement to your critical workloads at scale with Configurable Scheduler Profiles on AKS to optimize node cost." +description: "Improve GPU and CPU utilization, align pod placement to critical workloads, and reduce node costs at scale with Configurable Scheduler Profiles on AKS." date: 2026-03-31 authors: [colin-mixon] tags: [ai, performance, scheduler, best-practices, cost] @@ -8,7 +8,7 @@ tags: [ai, performance, scheduler, best-practices, cost] As reported by CastAI, on average Kubernetes clusters only reach [10% CPU utilization][cast-ai-k8s-cost-report] and Datadog finds most Kubernetes containers use less than [25% of their requested CPU][datadog-state-of-containers]. This data signals that underutilized resources are materially contributing to increased infrastructure cost. While there are many factors that impact node utilization, as a core component of the Kubernetes control plane, the kube-scheduler has a big influence on node utilization. -With the introduction of[Configurable Scheduler Profiles][concepts-scheduler-configuration] on AKS, customers now have the opportunity to increase their node utilization across CPU and GPU resources and optimize their costs with access to fine-grained pod scheduling strategies by configuring their own scheduling logic: enabling specific policies, adjusting policy priorities, tuning parameter weights, and choosing when each policy runs (for example, during PreFilter, Filter, or Score). +With the introduction of [Configurable Scheduler Profiles][concepts-scheduler-configuration] on AKS, customers now have the opportunity to increase their node utilization across CPU and GPU resources and optimize their costs with access to fine-grained pod scheduling strategies by configuring their own scheduling logic: enabling specific policies, adjusting policy priorities, tuning parameter weights, and choosing when each policy runs (for example, during PreFilter, Filter, or Score). This blog details how the default Kubernetes scheduler places pods, limitations, and provides best-practice recommendations to increase node utilization for your workloads using Configurable Scheduler Profiles on AKS. @@ -45,7 +45,7 @@ Today, the default scheduler on AKS lacks the flexibility for users to change wh ## Configurable Scheduler Profiles on AKS -[Configurable Scheduler Profiles on AKS][concepts-scheduler-configuration] allows customers to benefit from the extensibility of the [scheduling framework][scheduling-framework-interfaces] while reducing the operational overhead of adopting a second scheduler or defining a custom scheduler. Configurable Scheduler Profiles uses a Custom Resource Definition (CRD) that lets users define custom scheduler profiles with their own scheduling logic. A dedicated controller continuously reconciles these user-defined configurations with the underlying kube-scheduler deployment, validating changes and applying them transparently. If a configuration causes the scheduler to become unhealthy, the controller automatically rolls back to the last known good state to ensure cluster stability. This means incorrect plugins or values will not be applied. +[Configurable Scheduler Profiles on AKS][concepts-scheduler-configuration] allows customers to benefit from the extensibility of the [scheduling framework][scheduling-framework-interfaces] while reducing the operational overhead of adopting a second scheduler or defining a custom scheduler. Configurable Scheduler Profiles use a Custom Resource Definition (CRD) that lets users define custom scheduler profiles with their own scheduling logic. A dedicated controller continuously reconciles these user-defined configurations with the underlying kube-scheduler deployment, validating changes and applying them transparently. If a configuration causes the scheduler to become unhealthy, the controller automatically rolls back to the last known good state to ensure cluster stability. This means incorrect plugins or values will not be applied. ![Architecture diagram showing how Configurable Scheduler Profiles use a CRD and controller to reconcile user-defined profiles with the kube-scheduler deployment](./config-scheduler-profiles.png) @@ -53,13 +53,13 @@ A profile is a set of one or more in-tree scheduling plugins and configurations ## Increase Node Utilization and Operator Control -In this simple scale-out scenario, manually increasing replicas from 8 to 30 with identical pod specs, the default scheduler distributes pods evenly across nodes. The Configurable Scheduler Profiles using the `NodeResourcesFit` plugin are introduced shows a visible consolidation pattern that differs from the default scheduler's logic. Instead of spreading pods evenly, the scheduler begins to intentionally concentrate workloads onto fewer nodes. This shift occurs without changing the workload, node size, or autoscaling behavior - only the scheduler’s scoring logic. +In this simple scale-out scenario, when you manually increase replicas from 8 to 30 with identical pod specs, the default scheduler distributes pods evenly across nodes. Configurable Scheduler Profiles that use the `NodeResourcesFit` plugin show a visible consolidation pattern that differs from the default scheduler's logic. Instead of spreading pods evenly, the scheduler begins to intentionally concentrate workloads onto fewer nodes. This shift occurs without changing the workload, node size, or autoscaling behavior - only the scheduler’s scoring logic. While this experiment uses intentionally simple, CPU-bound containers to isolate scheduling behavior, the placement patterns observed here generalize to more complex workloads where consolidation and utilization efficiency matter. This change in distribution shape enables downstream efficiencies: improved control for platform engineers, efficient resource usage, and cost optimization that are difficult to achieve when pods are evenly spread. ![Table showing increased node utilization with the node bin packing scheduler profiles versus the pod distribution using the default scheduler](./default-config-scheduler-comparison.png) -The key takeaway is not that each profile expresses a distinct scheduling intent. The next two sections detail how MostAllocated and RequestedToCapacityRatio achieve these outcomes. +The key takeaway is that each profile expresses a distinct scheduling intent. The next two sections detail how MostAllocated and RequestedToCapacityRatio achieve these outcomes. ### Increase AKS CPU Utilization @@ -174,7 +174,7 @@ spec: ### FAQ 1. Which Bin packing strategy does AKS recommend to increase node utilization? AKS recommends using the scoring strategy `RequestedToCapacityRatio` because it provides a more granular scoring approach allowing users to define an ideal utilization curve for their respective nodes. For example, this bin packing strategy allows users to configure a target utilization of 85%. -2. How does Configurable Scheduler Profiles interact with autoscalers such as Node Auto Provisioning (NAP), Cluster Autoscaler (CAS), and Vertical Pod Autoscaler (VPA)? These components are complimentary to each other. Configurable Scheduler Profiles influences how pods are placed on nodes, while autoscalers make scaling decisions based on resource utilization and pending pods. +2. How does Configurable Scheduler Profiles interact with autoscalers such as Node Auto Provisioning (NAP), Cluster Autoscaler (CAS), and Vertical Pod Autoscaler (VPA)? These components are complementary to each other. Configurable Scheduler Profiles influences how pods are placed on nodes, while autoscalers make scaling decisions based on resource utilization and pending pods. - **Node Auto Provisioning (NAP)** is triggered when pods are unschedulable. If a suitable node already exists, that pod will be scheduled with the defined Configurable Scheduler Profile. If no suitable node exists, NAP provisions new capacity, after which the pod is scheduled according to the selected profile. - **Cluster Autoscaler (CA)** manages node scale-up and scale-down. On scale-up, CA is triggered when there aren't any suitable nodes available for the pending pod. Using the Configurable Scheduler Profiles ensure nodes are only scaled when provisioned resources are no longer suitable. On scale-down, CA is triggered when nodes fall below utilization thresholds, the default is 50%. As active nodes are packed more efficiently, underutilized nodes become easier candidates for removal. - **VPA** optimizes resource utilization patterns in pods. As pods are recreated with updated CPU and memory requests, they are scheduled using the configured scheduler profile, allowing placement decisions to reflect the new resource requirements. From d4863acfc12ff13d42855a8b9c531c62626843b9 Mon Sep 17 00:00:00 2001 From: colinmixonn <109253437+colinmixonn@users.noreply.github.com> Date: Tue, 24 Mar 2026 09:37:36 -0700 Subject: [PATCH 118/164] Apply suggestions from code review Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> --- .../index.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/website/blog/2026-03-31-aks-config-scheduler-profiles-preview/index.md b/website/blog/2026-03-31-aks-config-scheduler-profiles-preview/index.md index c0fa0e4be..093f07890 100644 --- a/website/blog/2026-03-31-aks-config-scheduler-profiles-preview/index.md +++ b/website/blog/2026-03-31-aks-config-scheduler-profiles-preview/index.md @@ -51,7 +51,7 @@ Today, the default scheduler on AKS lacks the flexibility for users to change wh A profile is a set of one or more in-tree scheduling plugins and configurations that dictate how to schedule a pod. AKS supports 18 in-tree Kubernetes [scheduling plugins][supported-in-tree-scheduling-plugins]. -## Increase Node Utilization and Operator Control +## Increase node utilization and operator control In this simple scale-out scenario, when you manually increase replicas from 8 to 30 with identical pod specs, the default scheduler distributes pods evenly across nodes. Configurable Scheduler Profiles that use the `NodeResourcesFit` plugin show a visible consolidation pattern that differs from the default scheduler's logic. Instead of spreading pods evenly, the scheduler begins to intentionally concentrate workloads onto fewer nodes. This shift occurs without changing the workload, node size, or autoscaling behavior - only the scheduler’s scoring logic. @@ -174,9 +174,9 @@ spec: ### FAQ 1. Which Bin packing strategy does AKS recommend to increase node utilization? AKS recommends using the scoring strategy `RequestedToCapacityRatio` because it provides a more granular scoring approach allowing users to define an ideal utilization curve for their respective nodes. For example, this bin packing strategy allows users to configure a target utilization of 85%. -2. How does Configurable Scheduler Profiles interact with autoscalers such as Node Auto Provisioning (NAP), Cluster Autoscaler (CAS), and Vertical Pod Autoscaler (VPA)? These components are complementary to each other. Configurable Scheduler Profiles influences how pods are placed on nodes, while autoscalers make scaling decisions based on resource utilization and pending pods. +2. How does Configurable Scheduler Profiles interact with autoscalers such as Node Auto Provisioning (NAP), Cluster Autoscaler (CAS), and Vertical Pod Autoscaler (VPA)? These components are complementary to each other. Configurable Scheduler Profiles influence how pods are placed on nodes, while autoscalers make scaling decisions based on resource utilization and pending pods. - **Node Auto Provisioning (NAP)** is triggered when pods are unschedulable. If a suitable node already exists, that pod will be scheduled with the defined Configurable Scheduler Profile. If no suitable node exists, NAP provisions new capacity, after which the pod is scheduled according to the selected profile. - - **Cluster Autoscaler (CA)** manages node scale-up and scale-down. On scale-up, CA is triggered when there aren't any suitable nodes available for the pending pod. Using the Configurable Scheduler Profiles ensure nodes are only scaled when provisioned resources are no longer suitable. On scale-down, CA is triggered when nodes fall below utilization thresholds, the default is 50%. As active nodes are packed more efficiently, underutilized nodes become easier candidates for removal. + - **Cluster Autoscaler (CA)** manages node scale-up and scale-down. On scale-up, CA is triggered when there aren't any suitable nodes available for the pending pod. Using Configurable Scheduler Profiles ensures nodes are only scaled when provisioned resources are no longer suitable. On scale-down, CA is triggered when nodes fall below utilization thresholds, the default is 50%. As active nodes are packed more efficiently, underutilized nodes become easier candidates for removal. - **VPA** optimizes resource utilization patterns in pods. As pods are recreated with updated CPU and memory requests, they are scheduled using the configured scheduler profile, allowing placement decisions to reflect the new resource requirements. 3. What if a resource is omitted in the `scoringStrategy` like `memory`? If a resource is omitted in the `scoringStrategy`, then that resource will not be considered in the filter or scoring cycles of the defined Configurable Scheduler Profile. If that resource should be considered, but with a reduced influence on the final score, it can be included with reduced weight. From 440a7ea6625b1ebb8a69e538195ba4729d05d96c Mon Sep 17 00:00:00 2001 From: colinmixonn <109253437+colinmixonn@users.noreply.github.com> Date: Wed, 25 Mar 2026 11:33:40 -0700 Subject: [PATCH 119/164] Apply suggestions from code review Co-authored-by: Ahmed Sabbour <103856+sabbour@users.noreply.github.com> --- .../2026-03-31-aks-config-scheduler-profiles-preview/index.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/website/blog/2026-03-31-aks-config-scheduler-profiles-preview/index.md b/website/blog/2026-03-31-aks-config-scheduler-profiles-preview/index.md index 093f07890..ae87fa26d 100644 --- a/website/blog/2026-03-31-aks-config-scheduler-profiles-preview/index.md +++ b/website/blog/2026-03-31-aks-config-scheduler-profiles-preview/index.md @@ -70,7 +70,7 @@ By shaping the scoring curve to target a range of 50-85% CPU utilization, operat **This bin packing profile is configured to favor nodes within a utilization band of 50-85%, avoiding empty nodes, and severely deprioritizing nearly full nodes at 90% utilization or more, to limit oversaturated nodes. Given this level of configuration detail, `RequestedToCapacityRatio` is the recommended scoring strategy for node bin‑packing on AKS for production clusters.** :::note -Scoring strategy can be used for GPU also. Adjust resources, resource weights, utilization thresholds, and plugin parameters to match your VM SKUs, workload patterns, and cluster topology. +Scoring strategy can also be used for GPU. Adjust resources, resource weights, utilization thresholds, and plugin parameters to match your VM SKUs, workload patterns, and cluster topology. ::: ```yaml @@ -120,7 +120,7 @@ spec: ### Increase AKS GPU Utilization -`MostAllocated` scores nodes based on their current resource utilization, favoring nodes that are already more heavily utilized. Unlike `RequestedToCapacityRatio`, it does not consider node capacity in node scoring, making it more suitable for an agressive cost-optimization scheduling strategy. When paired with MostAllocated, `NodeResourcesBalancedAllocation` complements the behoavior because it encourages pod placement on nodes with user-defined proportional utilization, helping reduce bottlenecks caused by asymmetric resource pressure. +`MostAllocated` scores nodes based on their current resource utilization, favoring nodes that are already more heavily utilized. Unlike `RequestedToCapacityRatio`, it does not consider node capacity in node scoring, making it more suitable for an aggressive cost-optimization scheduling strategy. When paired with MostAllocated, `NodeResourcesBalancedAllocation` complements the behavior because it encourages pod placement on nodes with user-defined proportional utilization, helping reduce bottlenecks caused by asymmetric resource pressure. When combined, these plugins favor GPU‑bound nodes with balanced CPU and memory usage over nodes with large amounts of unused memory or fragmented resources. This results in more efficient GPU placement and fewer partially utilized nodes.[Configure node bin-packing][configure-most-allocated] using the MostAllocated strategy to improve utilization and reduce infrastructure costs. From d3674ee1cc247adbb9957859c43c2e3582053486 Mon Sep 17 00:00:00 2001 From: colinmixonn <109253437+colinmixonn@users.noreply.github.com> Date: Fri, 27 Mar 2026 12:10:03 -0700 Subject: [PATCH 120/164] Update blog on Configurable Scheduler Profiles for AKS Revise blog content for clarity and conciseness regarding Configurable Scheduler Profiles on AKS, emphasizing benefits and operational details. --- .../index.md | 27 +++++++++++++------ 1 file changed, 19 insertions(+), 8 deletions(-) diff --git a/website/blog/2026-03-31-aks-config-scheduler-profiles-preview/index.md b/website/blog/2026-03-31-aks-config-scheduler-profiles-preview/index.md index ae87fa26d..570283632 100644 --- a/website/blog/2026-03-31-aks-config-scheduler-profiles-preview/index.md +++ b/website/blog/2026-03-31-aks-config-scheduler-profiles-preview/index.md @@ -8,13 +8,15 @@ tags: [ai, performance, scheduler, best-practices, cost] As reported by CastAI, on average Kubernetes clusters only reach [10% CPU utilization][cast-ai-k8s-cost-report] and Datadog finds most Kubernetes containers use less than [25% of their requested CPU][datadog-state-of-containers]. This data signals that underutilized resources are materially contributing to increased infrastructure cost. While there are many factors that impact node utilization, as a core component of the Kubernetes control plane, the kube-scheduler has a big influence on node utilization. -With the introduction of [Configurable Scheduler Profiles][concepts-scheduler-configuration] on AKS, customers now have the opportunity to increase their node utilization across CPU and GPU resources and optimize their costs with access to fine-grained pod scheduling strategies by configuring their own scheduling logic: enabling specific policies, adjusting policy priorities, tuning parameter weights, and choosing when each policy runs (for example, during PreFilter, Filter, or Score). +[Configurable Scheduler Profiles][concepts-scheduler-configuration] on AKS lets customers configure their own scheduling logic: enable specific plugins, adjust plugin priorities, and tune parameter weights. The result: higher node density, better GPU utilization, and lower infrastructure costs. -This blog details how the default Kubernetes scheduler places pods, limitations, and provides best-practice recommendations to increase node utilization for your workloads using Configurable Scheduler Profiles on AKS. +This blog explains how the default Kubernetes scheduler places pods, where the defaults fall short, and how to increase node utilization using Configurable Scheduler Profiles on AKS. 1. [How does kube-scheduler work?](#how-does-the-default-kubernetes-scheduler-place-pods) -2. [How to increase AKS cluster CPU utilization with Configurable Scheduler](#increase-aks-cpu-utilization) -3. [How to increase AKS cluster GPU and CPU utilization with Configurable Scheduler](#increase-aks-gpu-utilization) +2. [How Increase node utilization and operator control](#configurable-scheduler-profiles-on-aks) +4. [How to increase AKS cluster CPU utilization with Configurable Scheduler](#increase-aks-cpu-utilization) +5. [How to increase AKS cluster GPU and CPU utilization with Configurable Scheduler](#increase-aks-gpu-utilization) +6. [How does Configurable Scheduler Profiles interact with autoscalers?](#faq) @@ -41,11 +43,11 @@ Once a node is selected, the binding cycle can process multiple pods in parallel The default scheduler is primarily designed for general-purpose workloads that prioritizes nodes with the most available resources using the _LeastAllocated_ scoring strategy. This spreads pods across nodes, even when they could safely be packed more densely. While this works well for many services, the default scheduling criteria, and their fixed priority order, are not suitable for workloads that demand optimizing GPU and CPU utilization. In these scenarios, spreading pods across nodes can lead to fragmented resources, underutilized GPUs, and increased infrastructure cost. -Today, the default scheduler on AKS lacks the flexibility for users to change which criteria should be prioritized, or ignored, in the scheduling cycle on a per cluster basis. This rigidity often forces users to either accept suboptimal placement or manage a separate custom scheduler, both of which increase operational complexity. Starting with Kubernetes v1.33, AKS introduces Configurable Scheduler Profiles, enabling customized scheduler behavior without maintaining a separate scheduler. Now, users can adjust the `NodeResourcesFit` plugin from the default configuration to favor nodes with higher utilization to achieve more efficient bin‑packing and reduce infrastructure cost. +Today, the default scheduler on AKS lacks the flexibility for users to change which criteria should be prioritized, or ignored, in the scheduling cycle on a per cluster basis. This rigidity often forces users to either accept suboptimal placement or manage a separate custom scheduler, both of which increase operational complexity. Starting with Kubernetes v1.33, AKS introduces Configurable Scheduler Profiles - an AKS-managed CRD - that exposes the upstream scheduling framework without maintaining a separate scheduler. Now, users can adjust the `NodeResourcesFit` plugin from the default configuration to favor nodes with higher utilization to achieve more efficient bin‑packing and reduce infrastructure cost. ## Configurable Scheduler Profiles on AKS -[Configurable Scheduler Profiles on AKS][concepts-scheduler-configuration] allows customers to benefit from the extensibility of the [scheduling framework][scheduling-framework-interfaces] while reducing the operational overhead of adopting a second scheduler or defining a custom scheduler. Configurable Scheduler Profiles use a Custom Resource Definition (CRD) that lets users define custom scheduler profiles with their own scheduling logic. A dedicated controller continuously reconciles these user-defined configurations with the underlying kube-scheduler deployment, validating changes and applying them transparently. If a configuration causes the scheduler to become unhealthy, the controller automatically rolls back to the last known good state to ensure cluster stability. This means incorrect plugins or values will not be applied. +[Configurable Scheduler Profiles on AKS][concepts-scheduler-configuration] allows customers to benefit from the extensibility of the [scheduling framework][scheduling-framework-interfaces] while reducing the operational overhead of adopting a second scheduler or defining a custom scheduler. Configurable Scheduler Profiles use a Custom Resource Definition (CRD) that lets users define custom scheduler profiles with their own scheduling logic. A dedicated controller continuously reconciles these user-defined configurations with the underlying kube-scheduler deployment, validating changes and applying them transparently. If a configuration causes the scheduler to become unhealthy, the controller automatically rolls back to the last known good state to ensure cluster stability. If the new configuration causes the scheduler to fail health checks, the controller reverts to the last known good configuration." ![Architecture diagram showing how Configurable Scheduler Profiles use a CRD and controller to reconcile user-defined profiles with the kube-scheduler deployment](./config-scheduler-profiles.png) @@ -175,10 +177,17 @@ spec: 1. Which Bin packing strategy does AKS recommend to increase node utilization? AKS recommends using the scoring strategy `RequestedToCapacityRatio` because it provides a more granular scoring approach allowing users to define an ideal utilization curve for their respective nodes. For example, this bin packing strategy allows users to configure a target utilization of 85%. 2. How does Configurable Scheduler Profiles interact with autoscalers such as Node Auto Provisioning (NAP), Cluster Autoscaler (CAS), and Vertical Pod Autoscaler (VPA)? These components are complementary to each other. Configurable Scheduler Profiles influence how pods are placed on nodes, while autoscalers make scaling decisions based on resource utilization and pending pods. - - **Node Auto Provisioning (NAP)** is triggered when pods are unschedulable. If a suitable node already exists, that pod will be scheduled with the defined Configurable Scheduler Profile. If no suitable node exists, NAP provisions new capacity, after which the pod is scheduled according to the selected profile. + - **Node Auto Provisioning (NAP)** is triggered when pods are unschedulable. If a suitable node already exists, that pod will be scheduled with the defined Configurable Scheduler Profile. If no suitable node exists, NAP provisions new capacity and schedules pod. - **Cluster Autoscaler (CA)** manages node scale-up and scale-down. On scale-up, CA is triggered when there aren't any suitable nodes available for the pending pod. Using Configurable Scheduler Profiles ensures nodes are only scaled when provisioned resources are no longer suitable. On scale-down, CA is triggered when nodes fall below utilization thresholds, the default is 50%. As active nodes are packed more efficiently, underutilized nodes become easier candidates for removal. - **VPA** optimizes resource utilization patterns in pods. As pods are recreated with updated CPU and memory requests, they are scheduled using the configured scheduler profile, allowing placement decisions to reflect the new resource requirements. -3. What if a resource is omitted in the `scoringStrategy` like `memory`? If a resource is omitted in the `scoringStrategy`, then that resource will not be considered in the filter or scoring cycles of the defined Configurable Scheduler Profile. If that resource should be considered, but with a reduced influence on the final score, it can be included with reduced weight. +3. What if a resource, like `memory`, is omitted in the `scoringStrategy`? If a resource is omitted in the `scoringStrategy`, then that resource will not be considered in the filter or scoring cycles of the defined Configurable Scheduler Profile. If that resource should be considered, but with a reduced influence on the final score, it can be included with reduced weight. +4. Can multiple Configurable Scheduler Profiles be used for different workloads on the same cluster? Yes, mulitple scheduling profiles can coexist in a single cluster. This allows different placement strategies (for example, cost‑optimized vs. latency‑sensitive workloads) to run side‑by‑side. Visit the documentation for a [multiple scheduler profiles example][configure-multi-config] + - Multiple profiles can be defined centrally in a single scheduler configuration. + - Individual workloads select a profile via `schedulerName` in the pod spec. +6. How do I monitor whether my scheduler profile is improving utilization? These help confirm the scheduler is behaving correctly. Ultimately, you should see higher average node utilization, reduced variance between nodes, fewer lightly‑utilized nodes over time + - Node‑level utilization metrics: CPU and memory utilization per node and Distribution of pods across nodes using Azure Monitor Container Insights and `kubectl top nodes` for quick validation + - Autoscaler outcomes: fewer scale‑ups during normal load and more decisive scale‑downs after demand drops + - Cost Metrics: reduced idle costs when using [Cost-Analysis add-on][#aks-cost-analysis-add-on] ## Next Steps: Optimize Azure resources and test Configurable Scheduler Profiles on AKS @@ -198,3 +207,5 @@ With Configurable Scheduler Profiles, teams gain fine-grained control over pod p [cast-ai-k8s-cost-report]: https://cast.ai/reports/kubernetes-cost-benchmark/ [configure-requested-to-capacity]: https://learn.microsoft.com/azure/aks/configure-node-binpack-scheduler?tabs=new-cluster#configure-node-bin-packing-with-requestedtocapacity-plugin [configure-most-allocated]: https://learn.microsoft.com/azure/aks/configure-node-binpack-scheduler?tabs=new-cluster#configure-node-bin-packing-with-mostallocated-plugin +[configure-multi-config]: https://learn.microsoft.com/azure/aks/configure-aks-scheduler?tabs=new-cluster#configure-multiple-scheduler-profiles +[#aks-cost-analysis-add-on]: https://learn.microsoft.com/azure/aks/cost-analysis From 8a9ecb883d043f8589949a2cc57bd79f15ea6393 Mon Sep 17 00:00:00 2001 From: colinmixonn <109253437+colinmixonn@users.noreply.github.com> Date: Fri, 27 Mar 2026 12:11:22 -0700 Subject: [PATCH 121/164] Fix formatting and enhance scheduler profile metrics section Corrected formatting and improved clarity of scheduler profile monitoring metrics. --- .../index.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/website/blog/2026-03-31-aks-config-scheduler-profiles-preview/index.md b/website/blog/2026-03-31-aks-config-scheduler-profiles-preview/index.md index 570283632..268fb37a8 100644 --- a/website/blog/2026-03-31-aks-config-scheduler-profiles-preview/index.md +++ b/website/blog/2026-03-31-aks-config-scheduler-profiles-preview/index.md @@ -185,9 +185,9 @@ spec: - Multiple profiles can be defined centrally in a single scheduler configuration. - Individual workloads select a profile via `schedulerName` in the pod spec. 6. How do I monitor whether my scheduler profile is improving utilization? These help confirm the scheduler is behaving correctly. Ultimately, you should see higher average node utilization, reduced variance between nodes, fewer lightly‑utilized nodes over time - - Node‑level utilization metrics: CPU and memory utilization per node and Distribution of pods across nodes using Azure Monitor Container Insights and `kubectl top nodes` for quick validation - - Autoscaler outcomes: fewer scale‑ups during normal load and more decisive scale‑downs after demand drops - - Cost Metrics: reduced idle costs when using [Cost-Analysis add-on][#aks-cost-analysis-add-on] + - Node‑level utilization metrics: CPU and memory utilization per node and Distribution of pods across nodes using Azure Monitor Container Insights and `kubectl top nodes` for quick validation + - Autoscaler outcomes: fewer scale‑ups during normal load and more decisive scale‑downs after demand drops + - Cost Metrics: reduced idle costs when using [Cost-Analysis add-on][#aks-cost-analysis-add-on] ## Next Steps: Optimize Azure resources and test Configurable Scheduler Profiles on AKS From a5d0ca3fe62e7d9bce947ea302c6fa957b323112 Mon Sep 17 00:00:00 2001 From: colinmixonn <109253437+colinmixonn@users.noreply.github.com> Date: Fri, 27 Mar 2026 13:34:32 -0700 Subject: [PATCH 122/164] Fix links and improve clarity in blog post --- .../index.md | 28 +++++++++---------- 1 file changed, 14 insertions(+), 14 deletions(-) diff --git a/website/blog/2026-03-31-aks-config-scheduler-profiles-preview/index.md b/website/blog/2026-03-31-aks-config-scheduler-profiles-preview/index.md index 268fb37a8..257ee9a6c 100644 --- a/website/blog/2026-03-31-aks-config-scheduler-profiles-preview/index.md +++ b/website/blog/2026-03-31-aks-config-scheduler-profiles-preview/index.md @@ -13,10 +13,10 @@ As reported by CastAI, on average Kubernetes clusters only reach [10% CPU utiliz This blog explains how the default Kubernetes scheduler places pods, where the defaults fall short, and how to increase node utilization using Configurable Scheduler Profiles on AKS. 1. [How does kube-scheduler work?](#how-does-the-default-kubernetes-scheduler-place-pods) -2. [How Increase node utilization and operator control](#configurable-scheduler-profiles-on-aks) -4. [How to increase AKS cluster CPU utilization with Configurable Scheduler](#increase-aks-cpu-utilization) -5. [How to increase AKS cluster GPU and CPU utilization with Configurable Scheduler](#increase-aks-gpu-utilization) -6. [How does Configurable Scheduler Profiles interact with autoscalers?](#faq) +2. [Use Configurable Scheduler Profiles to ncrease node utilization and operator control](#configurable-scheduler-profiles-on-aks) +3. [How to increase AKS cluster CPU utilization with Configurable Scheduler](#increase-aks-cpu-utilization) +4. [How to increase AKS cluster GPU and CPU utilization with Configurable Scheduler](#increase-aks-gpu-utilization) +5. [How do Configurable Scheduler Profiles interact with autoscalers?](#faq) @@ -41,13 +41,13 @@ Once a node is selected, the binding cycle can process multiple pods in parallel ### Limitations of the default Kubernetes scheduler -The default scheduler is primarily designed for general-purpose workloads that prioritizes nodes with the most available resources using the _LeastAllocated_ scoring strategy. This spreads pods across nodes, even when they could safely be packed more densely. While this works well for many services, the default scheduling criteria, and their fixed priority order, are not suitable for workloads that demand optimizing GPU and CPU utilization. In these scenarios, spreading pods across nodes can lead to fragmented resources, underutilized GPUs, and increased infrastructure cost. +The default scheduler is primarily designed for general-purpose workloads that prioritize nodes with the most available resources using the _LeastAllocated_ scoring strategy. This spreads pods across nodes, even when they could safely be packed more densely. While this works well for many services, the default scheduling criteria, and their fixed priority order, are not suitable for workloads that demand optimizing GPU and CPU utilization. In these scenarios, spreading pods across nodes can lead to fragmented resources, underutilized GPUs, and increased infrastructure cost. Today, the default scheduler on AKS lacks the flexibility for users to change which criteria should be prioritized, or ignored, in the scheduling cycle on a per cluster basis. This rigidity often forces users to either accept suboptimal placement or manage a separate custom scheduler, both of which increase operational complexity. Starting with Kubernetes v1.33, AKS introduces Configurable Scheduler Profiles - an AKS-managed CRD - that exposes the upstream scheduling framework without maintaining a separate scheduler. Now, users can adjust the `NodeResourcesFit` plugin from the default configuration to favor nodes with higher utilization to achieve more efficient bin‑packing and reduce infrastructure cost. ## Configurable Scheduler Profiles on AKS -[Configurable Scheduler Profiles on AKS][concepts-scheduler-configuration] allows customers to benefit from the extensibility of the [scheduling framework][scheduling-framework-interfaces] while reducing the operational overhead of adopting a second scheduler or defining a custom scheduler. Configurable Scheduler Profiles use a Custom Resource Definition (CRD) that lets users define custom scheduler profiles with their own scheduling logic. A dedicated controller continuously reconciles these user-defined configurations with the underlying kube-scheduler deployment, validating changes and applying them transparently. If a configuration causes the scheduler to become unhealthy, the controller automatically rolls back to the last known good state to ensure cluster stability. If the new configuration causes the scheduler to fail health checks, the controller reverts to the last known good configuration." +[Configurable Scheduler Profiles on AKS][concepts-scheduler-configuration] allows customers to benefit from the extensibility of the [scheduling framework][scheduling-framework-interfaces] while reducing the operational overhead of adopting a second scheduler or defining a custom scheduler. Configurable Scheduler Profiles use a Custom Resource Definition (CRD) that lets users define custom scheduler profiles with their own scheduling logic. A dedicated controller continuously reconciles these user-defined configurations with the underlying kube-scheduler deployment, validating changes and applying them transparently. If a new configuration causes the scheduler to become unhealthy, the controller automatically reverts to the last known good state to ensure cluster stability. ![Architecture diagram showing how Configurable Scheduler Profiles use a CRD and controller to reconcile user-defined profiles with the kube-scheduler deployment](./config-scheduler-profiles.png) @@ -63,7 +63,7 @@ While this experiment uses intentionally simple, CPU-bound containers to isolate The key takeaway is that each profile expresses a distinct scheduling intent. The next two sections detail how MostAllocated and RequestedToCapacityRatio achieve these outcomes. -### Increase AKS CPU Utilization +### Increase AKS CPU utilization `RequestedToCapacityRatio` scores nodes based on the ratio of requested resources to total node capacity after the pod is _hypothetically_ placed. This strategy enables more fine-grained bin‑packing by allowing operators to define an ideal utilization curve for their nodes rather than simply preferring the most or least utilized nodes. @@ -72,7 +72,7 @@ By shaping the scoring curve to target a range of 50-85% CPU utilization, operat **This bin packing profile is configured to favor nodes within a utilization band of 50-85%, avoiding empty nodes, and severely deprioritizing nearly full nodes at 90% utilization or more, to limit oversaturated nodes. Given this level of configuration detail, `RequestedToCapacityRatio` is the recommended scoring strategy for node bin‑packing on AKS for production clusters.** :::note -Scoring strategy can also be used for GPU. Adjust resources, resource weights, utilization thresholds, and plugin parameters to match your VM SKUs, workload patterns, and cluster topology. +Scoring strategy can also be used for GPU by changing the target resource. Adjust resources, resource weights, utilization thresholds, and plugin parameters to match your VM SKUs, workload patterns, and cluster topology. ::: ```yaml @@ -120,7 +120,7 @@ spec: score: 0 ``` -### Increase AKS GPU Utilization +### Increase AKS GPU utilization `MostAllocated` scores nodes based on their current resource utilization, favoring nodes that are already more heavily utilized. Unlike `RequestedToCapacityRatio`, it does not consider node capacity in node scoring, making it more suitable for an aggressive cost-optimization scheduling strategy. When paired with MostAllocated, `NodeResourcesBalancedAllocation` complements the behavior because it encourages pod placement on nodes with user-defined proportional utilization, helping reduce bottlenecks caused by asymmetric resource pressure. @@ -181,15 +181,15 @@ spec: - **Cluster Autoscaler (CA)** manages node scale-up and scale-down. On scale-up, CA is triggered when there aren't any suitable nodes available for the pending pod. Using Configurable Scheduler Profiles ensures nodes are only scaled when provisioned resources are no longer suitable. On scale-down, CA is triggered when nodes fall below utilization thresholds, the default is 50%. As active nodes are packed more efficiently, underutilized nodes become easier candidates for removal. - **VPA** optimizes resource utilization patterns in pods. As pods are recreated with updated CPU and memory requests, they are scheduled using the configured scheduler profile, allowing placement decisions to reflect the new resource requirements. 3. What if a resource, like `memory`, is omitted in the `scoringStrategy`? If a resource is omitted in the `scoringStrategy`, then that resource will not be considered in the filter or scoring cycles of the defined Configurable Scheduler Profile. If that resource should be considered, but with a reduced influence on the final score, it can be included with reduced weight. -4. Can multiple Configurable Scheduler Profiles be used for different workloads on the same cluster? Yes, mulitple scheduling profiles can coexist in a single cluster. This allows different placement strategies (for example, cost‑optimized vs. latency‑sensitive workloads) to run side‑by‑side. Visit the documentation for a [multiple scheduler profiles example][configure-multi-config] - - Multiple profiles can be defined centrally in a single scheduler configuration. - - Individual workloads select a profile via `schedulerName` in the pod spec. -6. How do I monitor whether my scheduler profile is improving utilization? These help confirm the scheduler is behaving correctly. Ultimately, you should see higher average node utilization, reduced variance between nodes, fewer lightly‑utilized nodes over time +4. Can multiple Configurable Scheduler Profiles be used for different workloads on the same cluster? Yes, multiple scheduling profiles can coexist in a single cluster. This allows different placement strategies (for example, cost‑optimized vs. latency‑sensitive workloads) to run side‑by‑side. Visit the documentation for a [multiple scheduler profiles example][configure-multi-config] + - Multiple profiles can be defined centrally in a single scheduler configuration. + - Individual workloads select a profile via `schedulerName` in the pod spec. +5. How do I monitor whether my scheduler profile is improving utilization? These help confirm the scheduler is behaving correctly. Ultimately, you should see higher average node utilization, reduced variance between nodes, fewer lightly‑utilized nodes over time - Node‑level utilization metrics: CPU and memory utilization per node and Distribution of pods across nodes using Azure Monitor Container Insights and `kubectl top nodes` for quick validation - Autoscaler outcomes: fewer scale‑ups during normal load and more decisive scale‑downs after demand drops - Cost Metrics: reduced idle costs when using [Cost-Analysis add-on][#aks-cost-analysis-add-on] -## Next Steps: Optimize Azure resources and test Configurable Scheduler Profiles on AKS +## Next steps: Optimize Azure resources and test Configurable Scheduler Profiles on AKS With Configurable Scheduler Profiles, teams gain fine-grained control over pod placement strategies like bin-packing, topology distribution, and resource-based scoring that directly addresses challenges related to application resilience and resource utilization for their AKS clusters. By leveraging these scheduling plugins, AKS users can ensure their workloads make full use of available GPU capacity, reduce idle costs, and avoid costly overprovisioning. This not only improves ROI but also accelerates development by allowing more jobs to run concurrently and reliably. From f1320abff30890b05a316832ff2f7e696544b4e2 Mon Sep 17 00:00:00 2001 From: colinmixonn <109253437+colinmixonn@users.noreply.github.com> Date: Fri, 27 Mar 2026 14:08:04 -0700 Subject: [PATCH 123/164] Update blog post links for clarity and accuracy --- .../2026-03-31-aks-config-scheduler-profiles-preview/index.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/website/blog/2026-03-31-aks-config-scheduler-profiles-preview/index.md b/website/blog/2026-03-31-aks-config-scheduler-profiles-preview/index.md index 257ee9a6c..154223351 100644 --- a/website/blog/2026-03-31-aks-config-scheduler-profiles-preview/index.md +++ b/website/blog/2026-03-31-aks-config-scheduler-profiles-preview/index.md @@ -14,8 +14,8 @@ This blog explains how the default Kubernetes scheduler places pods, where the d 1. [How does kube-scheduler work?](#how-does-the-default-kubernetes-scheduler-place-pods) 2. [Use Configurable Scheduler Profiles to ncrease node utilization and operator control](#configurable-scheduler-profiles-on-aks) -3. [How to increase AKS cluster CPU utilization with Configurable Scheduler](#increase-aks-cpu-utilization) -4. [How to increase AKS cluster GPU and CPU utilization with Configurable Scheduler](#increase-aks-gpu-utilization) +3. [Increase AKS cluster CPU utilization up to 85% with Configurable Scheduler](#increase-aks-cpu-utilization) +4. [Increase AKS cluster GPU or CPU utilization while balancing memory with Configurable Scheduler](#increase-aks-gpu-utilization) 5. [How do Configurable Scheduler Profiles interact with autoscalers?](#faq) From a0017476dda3f99c3cfbbfb0919077baeffe3299 Mon Sep 17 00:00:00 2001 From: colinmixonn <109253437+colinmixonn@users.noreply.github.com> Date: Mon, 30 Mar 2026 10:54:19 -0700 Subject: [PATCH 124/164] Revise Kubernetes utilization statistics and fix typo Updated statistics on Kubernetes container utilization and corrected a typo in the section about Configurable Scheduler Profiles. --- .../index.md | 8 ++++++-- 1 file changed, 6 insertions(+), 2 deletions(-) diff --git a/website/blog/2026-03-31-aks-config-scheduler-profiles-preview/index.md b/website/blog/2026-03-31-aks-config-scheduler-profiles-preview/index.md index 154223351..a36f09ff2 100644 --- a/website/blog/2026-03-31-aks-config-scheduler-profiles-preview/index.md +++ b/website/blog/2026-03-31-aks-config-scheduler-profiles-preview/index.md @@ -6,14 +6,14 @@ authors: [colin-mixon] tags: [ai, performance, scheduler, best-practices, cost] --- -As reported by CastAI, on average Kubernetes clusters only reach [10% CPU utilization][cast-ai-k8s-cost-report] and Datadog finds most Kubernetes containers use less than [25% of their requested CPU][datadog-state-of-containers]. This data signals that underutilized resources are materially contributing to increased infrastructure cost. While there are many factors that impact node utilization, as a core component of the Kubernetes control plane, the kube-scheduler has a big influence on node utilization. +In 2025 Datadog finds most Kubernetes containers use less than [25% of their requested CPU][datadog-state-of-containers] and in 2023 Weights and Biases foound that nearly a third of our users are [averaging less than 15% utilization.][wb-gpu-utilization]. This data signals that underutilized resources materially contribute to increased infrastructure cost. While there are many factors that impact node utilization, as a core component of the Kubernetes control plane, the kube-scheduler has a big influence on node utilization. [Configurable Scheduler Profiles][concepts-scheduler-configuration] on AKS lets customers configure their own scheduling logic: enable specific plugins, adjust plugin priorities, and tune parameter weights. The result: higher node density, better GPU utilization, and lower infrastructure costs. This blog explains how the default Kubernetes scheduler places pods, where the defaults fall short, and how to increase node utilization using Configurable Scheduler Profiles on AKS. 1. [How does kube-scheduler work?](#how-does-the-default-kubernetes-scheduler-place-pods) -2. [Use Configurable Scheduler Profiles to ncrease node utilization and operator control](#configurable-scheduler-profiles-on-aks) +2. [Use Configurable Scheduler Profiles to increase node utilization and operator control](#configurable-scheduler-profiles-on-aks) 3. [Increase AKS cluster CPU utilization up to 85% with Configurable Scheduler](#increase-aks-cpu-utilization) 4. [Increase AKS cluster GPU or CPU utilization while balancing memory with Configurable Scheduler](#increase-aks-gpu-utilization) 5. [How do Configurable Scheduler Profiles interact with autoscalers?](#faq) @@ -39,6 +39,8 @@ Once a node is selected, the binding cycle can process multiple pods in parallel ![Diagram of the kube-scheduler workflow showing pods entering the scheduling cycle and binding cycle to assign a pod on a node](./kube-scheduler-scheduling-phases-diagram.png) +For a deep dive on the Kubernetes Scheduler visit technical blog from SIG Scheduling contributor and AKS Upstream Engineer, Heba Elayoty, [Deep Dive into the Kubernetes Scheduler Framework][deep-dive-scheduler-framework]. + ### Limitations of the default Kubernetes scheduler The default scheduler is primarily designed for general-purpose workloads that prioritize nodes with the most available resources using the _LeastAllocated_ scoring strategy. This spreads pods across nodes, even when they could safely be packed more densely. While this works well for many services, the default scheduling criteria, and their fixed priority order, are not suitable for workloads that demand optimizing GPU and CPU utilization. In these scenarios, spreading pods across nodes can lead to fragmented resources, underutilized GPUs, and increased infrastructure cost. @@ -209,3 +211,5 @@ With Configurable Scheduler Profiles, teams gain fine-grained control over pod p [configure-most-allocated]: https://learn.microsoft.com/azure/aks/configure-node-binpack-scheduler?tabs=new-cluster#configure-node-bin-packing-with-mostallocated-plugin [configure-multi-config]: https://learn.microsoft.com/azure/aks/configure-aks-scheduler?tabs=new-cluster#configure-multiple-scheduler-profiles [#aks-cost-analysis-add-on]: https://learn.microsoft.com/azure/aks/cost-analysis +[wb-gpu-utilization]: https://wandb.ai/wandb_fc/articles/reports/Monitor-Improve-GPU-Usage-for-Model-Training--Vmlldzo1NDQzNjM3 +[deep-dive-scheduler-framework]: https://helayoty.org/blog/inside-kube-scheduler-the-plugin?r=h58xa From 9f2cee7dc41ff4f951722b8f52dce5ad64d7328c Mon Sep 17 00:00:00 2001 From: colinmixonn <109253437+colinmixonn@users.noreply.github.com> Date: Mon, 30 Mar 2026 11:19:43 -0700 Subject: [PATCH 125/164] Fix typos and enhance introduction clarity Corrected typos and improved clarity in the introduction. --- .../2026-03-31-aks-config-scheduler-profiles-preview/index.md | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/website/blog/2026-03-31-aks-config-scheduler-profiles-preview/index.md b/website/blog/2026-03-31-aks-config-scheduler-profiles-preview/index.md index a36f09ff2..38c887fd9 100644 --- a/website/blog/2026-03-31-aks-config-scheduler-profiles-preview/index.md +++ b/website/blog/2026-03-31-aks-config-scheduler-profiles-preview/index.md @@ -6,7 +6,7 @@ authors: [colin-mixon] tags: [ai, performance, scheduler, best-practices, cost] --- -In 2025 Datadog finds most Kubernetes containers use less than [25% of their requested CPU][datadog-state-of-containers] and in 2023 Weights and Biases foound that nearly a third of our users are [averaging less than 15% utilization.][wb-gpu-utilization]. This data signals that underutilized resources materially contribute to increased infrastructure cost. While there are many factors that impact node utilization, as a core component of the Kubernetes control plane, the kube-scheduler has a big influence on node utilization. +In 2025, Datadog found most Kubernetes containers use less than [25% of their requested CPU][datadog-state-of-containers] and in 2023 Weights and Biases foound that nearly a third of GPU users are [averaging less than 15% utilization.][wb-gpu-utilization]. This data signals that underutilized resources materially contribute to increased infrastructure cost. While there are many factors that impact node utilization, as a core component of the Kubernetes control plane, the kube-scheduler has a big influence on node utilization. [Configurable Scheduler Profiles][concepts-scheduler-configuration] on AKS lets customers configure their own scheduling logic: enable specific plugins, adjust plugin priorities, and tune parameter weights. The result: higher node density, better GPU utilization, and lower infrastructure costs. @@ -206,7 +206,6 @@ With Configurable Scheduler Profiles, teams gain fine-grained control over pod p [supported-in-tree-scheduling-plugins]: https://learn.microsoft.com/azure/aks/concepts-scheduler-configuration#supported-in-tree-scheduling-plugins [node-bin-packing-configurations]: https://learn.microsoft.com/azure/aks/configure-node-binpack-scheduler?tabs=new-cluster [datadog-state-of-containers]: https://www.datadoghq.com/state-of-containers-and-serverless/ -[cast-ai-k8s-cost-report]: https://cast.ai/reports/kubernetes-cost-benchmark/ [configure-requested-to-capacity]: https://learn.microsoft.com/azure/aks/configure-node-binpack-scheduler?tabs=new-cluster#configure-node-bin-packing-with-requestedtocapacity-plugin [configure-most-allocated]: https://learn.microsoft.com/azure/aks/configure-node-binpack-scheduler?tabs=new-cluster#configure-node-bin-packing-with-mostallocated-plugin [configure-multi-config]: https://learn.microsoft.com/azure/aks/configure-aks-scheduler?tabs=new-cluster#configure-multiple-scheduler-profiles From ea050e9b09d3af2236548b742defe59a30cf33b9 Mon Sep 17 00:00:00 2001 From: colinmixonn <109253437+colinmixonn@users.noreply.github.com> Date: Mon, 30 Mar 2026 11:39:50 -0700 Subject: [PATCH 126/164] Fix typos and enhance clarity in AKS blog post Corrected typos and improved clarity in the blog post about AKS Configurable Scheduler Profiles. --- .../index.md | 14 +++++++------- 1 file changed, 7 insertions(+), 7 deletions(-) diff --git a/website/blog/2026-03-31-aks-config-scheduler-profiles-preview/index.md b/website/blog/2026-03-31-aks-config-scheduler-profiles-preview/index.md index 38c887fd9..287649faf 100644 --- a/website/blog/2026-03-31-aks-config-scheduler-profiles-preview/index.md +++ b/website/blog/2026-03-31-aks-config-scheduler-profiles-preview/index.md @@ -6,7 +6,7 @@ authors: [colin-mixon] tags: [ai, performance, scheduler, best-practices, cost] --- -In 2025, Datadog found most Kubernetes containers use less than [25% of their requested CPU][datadog-state-of-containers] and in 2023 Weights and Biases foound that nearly a third of GPU users are [averaging less than 15% utilization.][wb-gpu-utilization]. This data signals that underutilized resources materially contribute to increased infrastructure cost. While there are many factors that impact node utilization, as a core component of the Kubernetes control plane, the kube-scheduler has a big influence on node utilization. +In 2025, Datadog found most Kubernetes containers use less than [25% of their requested CPU][datadog-state-of-containers], and in 2023 Weights and Biases found that nearly a third of GPU users [average less than 15% utilization][wb-gpu-utilization]. This data signals that underutilized resources materially contribute to increased infrastructure cost. While there are many factors that impact node utilization, as a core component of the Kubernetes control plane, the kube-scheduler has a big influence on node utilization. [Configurable Scheduler Profiles][concepts-scheduler-configuration] on AKS lets customers configure their own scheduling logic: enable specific plugins, adjust plugin priorities, and tune parameter weights. The result: higher node density, better GPU utilization, and lower infrastructure costs. @@ -67,7 +67,7 @@ The key takeaway is that each profile expresses a distinct scheduling intent. Th ### Increase AKS CPU utilization -`RequestedToCapacityRatio` scores nodes based on the ratio of requested resources to total node capacity after the pod is _hypothetically_ placed. This strategy enables more fine-grained bin‑packing by allowing operators to define an ideal utilization curve for their nodes rather than simply preferring the most or least utilized nodes. +`RequestedToCapacityRatio` scores nodes based on the ratio of requested resources to total node capacity after the pod is _hypothetically_ placed. This strategy enables more fine-grained bin‑packing by allowing operators to define an ideal utilization curve for their nodes rather than simply preferring the most or least utilized nodes. Lastly, it is critical to note that `PodTopologySpread` is disabled in this profile because bin-packing and zone-spreading are opposing goals and the scheduling logic may prioritize pod spreading. If you need both high utilization AND zone resilience, define a new profile to achieve both goals as the default scheduler's behavoir resort to pod spreading. By shaping the scoring curve to target a range of 50-85% CPU utilization, operators can increase pod density on provisioned nodes while preserving headroom for bursts, background processes, and system components in CPU-based workloads. [Configure node bin-packing][configure-requested-to-capacity] using the `RequestedToCapacityRatio` strategy to improve utilization and reduce infrastructure costs. @@ -124,9 +124,9 @@ spec: ### Increase AKS GPU utilization -`MostAllocated` scores nodes based on their current resource utilization, favoring nodes that are already more heavily utilized. Unlike `RequestedToCapacityRatio`, it does not consider node capacity in node scoring, making it more suitable for an aggressive cost-optimization scheduling strategy. When paired with MostAllocated, `NodeResourcesBalancedAllocation` complements the behavior because it encourages pod placement on nodes with user-defined proportional utilization, helping reduce bottlenecks caused by asymmetric resource pressure. +`MostAllocated` scores nodes based on their current resource utilization, favoring nodes that are already more heavily utilized. Unlike `RequestedToCapacityRatio`, it does not consider node capacity in node scoring, making it more suitable for an aggressive cost-optimization scheduling strategy. When paired with MostAllocated, `NodeResourcesBalancedAllocation` complements the behavior because it encourages pod placement on nodes with user-defined proportional utilization, helping reduce bottlenecks caused by asymmetric resource pressure. Lastly, it is critical to note that `PodTopologySpread` is disabled in this profile because bin-packing and zone-spreading are opposing goals and the scheduling logic may prioritize pod spreading. -When combined, these plugins favor GPU‑bound nodes with balanced CPU and memory usage over nodes with large amounts of unused memory or fragmented resources. This results in more efficient GPU placement and fewer partially utilized nodes.[Configure node bin-packing][configure-most-allocated] using the MostAllocated strategy to improve utilization and reduce infrastructure costs. +When combined, these plugins favor GPU‑bound nodes with balanced CPU and memory usage over nodes with large amounts of unused memory or fragmented resources. This results in more efficient GPU placement and fewer partially utilized nodes. [Configure node bin-packing][configure-most-allocated] using the MostAllocated strategy to improve utilization and reduce infrastructure costs. **This scheduler configuration maximizes GPU utilization by consolidating smaller jobs onto fewer nodes, reducing idle accelerator capacity while maintaining reasonable CPU and memory balance.** @@ -178,7 +178,7 @@ spec: ### FAQ 1. Which Bin packing strategy does AKS recommend to increase node utilization? AKS recommends using the scoring strategy `RequestedToCapacityRatio` because it provides a more granular scoring approach allowing users to define an ideal utilization curve for their respective nodes. For example, this bin packing strategy allows users to configure a target utilization of 85%. -2. How does Configurable Scheduler Profiles interact with autoscalers such as Node Auto Provisioning (NAP), Cluster Autoscaler (CAS), and Vertical Pod Autoscaler (VPA)? These components are complementary to each other. Configurable Scheduler Profiles influence how pods are placed on nodes, while autoscalers make scaling decisions based on resource utilization and pending pods. +2. How does Configurable Scheduler Profiles interact with autoscalers such as Node Auto Provisioning (NAP), Cluster Autoscaler (CA), and Vertical Pod Autoscaler (VPA)? These components are complementary to each other. Configurable Scheduler Profiles influence how pods are placed on nodes, while autoscalers make scaling decisions based on resource utilization and pending pods. - **Node Auto Provisioning (NAP)** is triggered when pods are unschedulable. If a suitable node already exists, that pod will be scheduled with the defined Configurable Scheduler Profile. If no suitable node exists, NAP provisions new capacity and schedules pod. - **Cluster Autoscaler (CA)** manages node scale-up and scale-down. On scale-up, CA is triggered when there aren't any suitable nodes available for the pending pod. Using Configurable Scheduler Profiles ensures nodes are only scaled when provisioned resources are no longer suitable. On scale-down, CA is triggered when nodes fall below utilization thresholds, the default is 50%. As active nodes are packed more efficiently, underutilized nodes become easier candidates for removal. - **VPA** optimizes resource utilization patterns in pods. As pods are recreated with updated CPU and memory requests, they are scheduled using the configured scheduler profile, allowing placement decisions to reflect the new resource requirements. @@ -189,7 +189,7 @@ spec: 5. How do I monitor whether my scheduler profile is improving utilization? These help confirm the scheduler is behaving correctly. Ultimately, you should see higher average node utilization, reduced variance between nodes, fewer lightly‑utilized nodes over time - Node‑level utilization metrics: CPU and memory utilization per node and Distribution of pods across nodes using Azure Monitor Container Insights and `kubectl top nodes` for quick validation - Autoscaler outcomes: fewer scale‑ups during normal load and more decisive scale‑downs after demand drops - - Cost Metrics: reduced idle costs when using [Cost-Analysis add-on][#aks-cost-analysis-add-on] + - Cost Metrics: reduced idle costs when using [Cost-Analysis add-on][aks-cost-analysis-add-on] ## Next steps: Optimize Azure resources and test Configurable Scheduler Profiles on AKS @@ -209,6 +209,6 @@ With Configurable Scheduler Profiles, teams gain fine-grained control over pod p [configure-requested-to-capacity]: https://learn.microsoft.com/azure/aks/configure-node-binpack-scheduler?tabs=new-cluster#configure-node-bin-packing-with-requestedtocapacity-plugin [configure-most-allocated]: https://learn.microsoft.com/azure/aks/configure-node-binpack-scheduler?tabs=new-cluster#configure-node-bin-packing-with-mostallocated-plugin [configure-multi-config]: https://learn.microsoft.com/azure/aks/configure-aks-scheduler?tabs=new-cluster#configure-multiple-scheduler-profiles -[#aks-cost-analysis-add-on]: https://learn.microsoft.com/azure/aks/cost-analysis +[aks-cost-analysis-add-on]: https://learn.microsoft.com/azure/aks/cost-analysis [wb-gpu-utilization]: https://wandb.ai/wandb_fc/articles/reports/Monitor-Improve-GPU-Usage-for-Model-Training--Vmlldzo1NDQzNjM3 [deep-dive-scheduler-framework]: https://helayoty.org/blog/inside-kube-scheduler-the-plugin?r=h58xa From 0caf69288d612e66aa1854dad0201e57b9cfee5a Mon Sep 17 00:00:00 2001 From: colinmixonn <109253437+colinmixonn@users.noreply.github.com> Date: Mon, 30 Mar 2026 12:02:11 -0700 Subject: [PATCH 127/164] Enhance clarity on AKS GPU utilization strategies Clarified the explanation of the MostAllocated scheduling strategy and its impact on GPU utilization. Emphasized the importance of balancing CPU and memory usage for efficient GPU placement. --- .../2026-03-31-aks-config-scheduler-profiles-preview/index.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/website/blog/2026-03-31-aks-config-scheduler-profiles-preview/index.md b/website/blog/2026-03-31-aks-config-scheduler-profiles-preview/index.md index 287649faf..87be5f33f 100644 --- a/website/blog/2026-03-31-aks-config-scheduler-profiles-preview/index.md +++ b/website/blog/2026-03-31-aks-config-scheduler-profiles-preview/index.md @@ -124,7 +124,7 @@ spec: ### Increase AKS GPU utilization -`MostAllocated` scores nodes based on their current resource utilization, favoring nodes that are already more heavily utilized. Unlike `RequestedToCapacityRatio`, it does not consider node capacity in node scoring, making it more suitable for an aggressive cost-optimization scheduling strategy. When paired with MostAllocated, `NodeResourcesBalancedAllocation` complements the behavior because it encourages pod placement on nodes with user-defined proportional utilization, helping reduce bottlenecks caused by asymmetric resource pressure. Lastly, it is critical to note that `PodTopologySpread` is disabled in this profile because bin-packing and zone-spreading are opposing goals and the scheduling logic may prioritize pod spreading. +`MostAllocated` scores nodes based on their current resource utilization, favoring nodes that are already more heavily utilized. Unlike `RequestedToCapacityRatio`, it does not consider node capacity in node scoring, making it more suitable for an aggressive cost-optimization scheduling strategy. When paired with MostAllocated, `NodeResourcesBalancedAllocation` complements the behavior because it encourages pod placement on nodes with user-defined proportional utilization, helping reduce bottlenecks caused by asymmetric resource pressure. Lastly, it is critical to note that `PodTopologySpread` is disabled in this profile because bin-packing and zone-spreading are opposing goals and the scheduling logic may prioritize pod spreading. When combined, these plugins favor GPU‑bound nodes with balanced CPU and memory usage over nodes with large amounts of unused memory or fragmented resources. This results in more efficient GPU placement and fewer partially utilized nodes. [Configure node bin-packing][configure-most-allocated] using the MostAllocated strategy to improve utilization and reduce infrastructure costs. From bf6ed5a7013412df2f2d3befb5de30e8c076d887 Mon Sep 17 00:00:00 2001 From: colinmixonn <109253437+colinmixonn@users.noreply.github.com> Date: Mon, 30 Mar 2026 13:00:22 -0700 Subject: [PATCH 128/164] Apply suggestions from code review Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> --- .../index.md | 14 +++++++------- 1 file changed, 7 insertions(+), 7 deletions(-) diff --git a/website/blog/2026-03-31-aks-config-scheduler-profiles-preview/index.md b/website/blog/2026-03-31-aks-config-scheduler-profiles-preview/index.md index 87be5f33f..ef4e2ca31 100644 --- a/website/blog/2026-03-31-aks-config-scheduler-profiles-preview/index.md +++ b/website/blog/2026-03-31-aks-config-scheduler-profiles-preview/index.md @@ -49,7 +49,7 @@ Today, the default scheduler on AKS lacks the flexibility for users to change wh ## Configurable Scheduler Profiles on AKS -[Configurable Scheduler Profiles on AKS][concepts-scheduler-configuration] allows customers to benefit from the extensibility of the [scheduling framework][scheduling-framework-interfaces] while reducing the operational overhead of adopting a second scheduler or defining a custom scheduler. Configurable Scheduler Profiles use a Custom Resource Definition (CRD) that lets users define custom scheduler profiles with their own scheduling logic. A dedicated controller continuously reconciles these user-defined configurations with the underlying kube-scheduler deployment, validating changes and applying them transparently. If a new configuration causes the scheduler to become unhealthy, the controller automatically reverts to the last known good state to ensure cluster stability. +[Configurable Scheduler Profiles on AKS][concepts-scheduler-configuration] allow customers to benefit from the extensibility of the [scheduling framework][scheduling-framework-interfaces] while reducing the operational overhead of adopting a second scheduler or defining a custom scheduler. Configurable Scheduler Profiles use a Custom Resource Definition (CRD) that lets users define custom scheduler profiles with their own scheduling logic. A dedicated controller continuously reconciles these user-defined configurations with the underlying kube-scheduler deployment, validating changes and applying them transparently. If a new configuration causes the scheduler to become unhealthy, the controller automatically reverts to the last known good state to ensure cluster stability. ![Architecture diagram showing how Configurable Scheduler Profiles use a CRD and controller to reconcile user-defined profiles with the kube-scheduler deployment](./config-scheduler-profiles.png) @@ -67,7 +67,7 @@ The key takeaway is that each profile expresses a distinct scheduling intent. Th ### Increase AKS CPU utilization -`RequestedToCapacityRatio` scores nodes based on the ratio of requested resources to total node capacity after the pod is _hypothetically_ placed. This strategy enables more fine-grained bin‑packing by allowing operators to define an ideal utilization curve for their nodes rather than simply preferring the most or least utilized nodes. Lastly, it is critical to note that `PodTopologySpread` is disabled in this profile because bin-packing and zone-spreading are opposing goals and the scheduling logic may prioritize pod spreading. If you need both high utilization AND zone resilience, define a new profile to achieve both goals as the default scheduler's behavoir resort to pod spreading. +`RequestedToCapacityRatio` scores nodes based on the ratio of requested resources to total node capacity after the pod is _hypothetically_ placed. This strategy enables more fine-grained bin‑packing by allowing operators to define an ideal utilization curve for their nodes rather than simply preferring the most or least utilized nodes. Lastly, it is critical to note that `PodTopologySpread` is disabled in this profile because bin-packing and zone-spreading are opposing goals and the scheduling logic may prioritize pod spreading. If you need both high utilization AND zone resilience, define a new profile to achieve both goals as the default scheduler's behavior resort to pod spreading. By shaping the scoring curve to target a range of 50-85% CPU utilization, operators can increase pod density on provisioned nodes while preserving headroom for bursts, background processes, and system components in CPU-based workloads. [Configure node bin-packing][configure-requested-to-capacity] using the `RequestedToCapacityRatio` strategy to improve utilization and reduce infrastructure costs. @@ -186,10 +186,10 @@ spec: 4. Can multiple Configurable Scheduler Profiles be used for different workloads on the same cluster? Yes, multiple scheduling profiles can coexist in a single cluster. This allows different placement strategies (for example, cost‑optimized vs. latency‑sensitive workloads) to run side‑by‑side. Visit the documentation for a [multiple scheduler profiles example][configure-multi-config] - Multiple profiles can be defined centrally in a single scheduler configuration. - Individual workloads select a profile via `schedulerName` in the pod spec. -5. How do I monitor whether my scheduler profile is improving utilization? These help confirm the scheduler is behaving correctly. Ultimately, you should see higher average node utilization, reduced variance between nodes, fewer lightly‑utilized nodes over time - - Node‑level utilization metrics: CPU and memory utilization per node and Distribution of pods across nodes using Azure Monitor Container Insights and `kubectl top nodes` for quick validation - - Autoscaler outcomes: fewer scale‑ups during normal load and more decisive scale‑downs after demand drops - - Cost Metrics: reduced idle costs when using [Cost-Analysis add-on][aks-cost-analysis-add-on] +5. How do I monitor whether my scheduler profile is improving utilization? Monitor these signals to confirm that the scheduler is behaving correctly. Over time, you should see higher average node utilization, reduced variance between nodes, and fewer lightly utilized nodes. + - Track node‑level utilization metrics, including CPU and memory utilization per node and distribution of pods across nodes, using Azure Monitor Container Insights and `kubectl top nodes` for quick validation. + - Review autoscaler outcomes, looking for fewer scale‑ups during normal load and more decisive scale‑downs after demand drops. + - Measure cost metrics, such as reduced idle costs when you use the [Cost Analysis add-on][aks-cost-analysis-add-on]. ## Next steps: Optimize Azure resources and test Configurable Scheduler Profiles on AKS @@ -211,4 +211,4 @@ With Configurable Scheduler Profiles, teams gain fine-grained control over pod p [configure-multi-config]: https://learn.microsoft.com/azure/aks/configure-aks-scheduler?tabs=new-cluster#configure-multiple-scheduler-profiles [aks-cost-analysis-add-on]: https://learn.microsoft.com/azure/aks/cost-analysis [wb-gpu-utilization]: https://wandb.ai/wandb_fc/articles/reports/Monitor-Improve-GPU-Usage-for-Model-Training--Vmlldzo1NDQzNjM3 -[deep-dive-scheduler-framework]: https://helayoty.org/blog/inside-kube-scheduler-the-plugin?r=h58xa +[deep-dive-scheduler-framework]: https://helayoty.org/blog/inside-kube-scheduler-the-plugin From 4552ced43fbe7f1252ce67ec9f2c1d9be41c7c03 Mon Sep 17 00:00:00 2001 From: colinmixonn <109253437+colinmixonn@users.noreply.github.com> Date: Mon, 30 Mar 2026 14:11:28 -0700 Subject: [PATCH 129/164] Enhance AKS scheduler profiles documentation Added a comparison table for AKS configurable scheduler profiles detailing their scheduling intents and operator benefits. --- .../index.md | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/website/blog/2026-03-31-aks-config-scheduler-profiles-preview/index.md b/website/blog/2026-03-31-aks-config-scheduler-profiles-preview/index.md index ef4e2ca31..ce3fd5810 100644 --- a/website/blog/2026-03-31-aks-config-scheduler-profiles-preview/index.md +++ b/website/blog/2026-03-31-aks-config-scheduler-profiles-preview/index.md @@ -64,6 +64,11 @@ While this experiment uses intentionally simple, CPU-bound containers to isolate ![Table showing increased node utilization with the node bin packing scheduler profiles versus the pod distribution using the default scheduler](./default-config-scheduler-comparison.png) The key takeaway is that each profile expresses a distinct scheduling intent. The next two sections detail how MostAllocated and RequestedToCapacityRatio achieve these outcomes. +| Scheduler / Profile | Scheduling intent | Operator Benefits | +|---|---|---| +| Default scheduler
NodeResourcesFit: LeastAllocated | Balance and hotspot reduction | No tuning | +| Configurable Scheduler Profile
NodeResourcesFit: MostAllocated | Maximize consolidation / bin‑packing | Maximum node utilization, highest cost reduction potential | +| Configurable Scheduler Profile
NodeResourcesFit: RequestedToCapacityRatio | Targeted utilization with headroom | Stronger control over consolidation and burst headroom, finer control than `MostAllocated` | ### Increase AKS CPU utilization From 5c54c3bb93bf5638c7121cf8fd55c07ea4f604a9 Mon Sep 17 00:00:00 2001 From: colinmixonn <109253437+colinmixonn@users.noreply.github.com> Date: Mon, 30 Mar 2026 14:22:32 -0700 Subject: [PATCH 130/164] Refine FAQ and descriptions for Configurable Scheduler Profiles Updated FAQ section for clarity and improved phrasing. Enhanced descriptions of Configurable Scheduler Profiles and their interactions with autoscalers. --- .../index.md | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/website/blog/2026-03-31-aks-config-scheduler-profiles-preview/index.md b/website/blog/2026-03-31-aks-config-scheduler-profiles-preview/index.md index ce3fd5810..3b44d32ed 100644 --- a/website/blog/2026-03-31-aks-config-scheduler-profiles-preview/index.md +++ b/website/blog/2026-03-31-aks-config-scheduler-profiles-preview/index.md @@ -16,7 +16,7 @@ This blog explains how the default Kubernetes scheduler places pods, where the d 2. [Use Configurable Scheduler Profiles to increase node utilization and operator control](#configurable-scheduler-profiles-on-aks) 3. [Increase AKS cluster CPU utilization up to 85% with Configurable Scheduler](#increase-aks-cpu-utilization) 4. [Increase AKS cluster GPU or CPU utilization while balancing memory with Configurable Scheduler](#increase-aks-gpu-utilization) -5. [How do Configurable Scheduler Profiles interact with autoscalers?](#faq) +5. [FAQ: How do Configurable Scheduler Profiles interact with autoscalers?](#faq) @@ -68,11 +68,11 @@ The key takeaway is that each profile expresses a distinct scheduling intent. Th |---|---|---| | Default scheduler
NodeResourcesFit: LeastAllocated | Balance and hotspot reduction | No tuning | | Configurable Scheduler Profile
NodeResourcesFit: MostAllocated | Maximize consolidation / bin‑packing | Maximum node utilization, highest cost reduction potential | -| Configurable Scheduler Profile
NodeResourcesFit: RequestedToCapacityRatio | Targeted utilization with headroom | Stronger control over consolidation and burst headroom, finer control than `MostAllocated` | +| Configurable Scheduler Profile
NodeResourcesFit: RequestedToCapacityRatio | Targeted utilization with headroom | Increased utilization with stronger control over consolidation and burst headroom than `MostAllocated` | ### Increase AKS CPU utilization -`RequestedToCapacityRatio` scores nodes based on the ratio of requested resources to total node capacity after the pod is _hypothetically_ placed. This strategy enables more fine-grained bin‑packing by allowing operators to define an ideal utilization curve for their nodes rather than simply preferring the most or least utilized nodes. Lastly, it is critical to note that `PodTopologySpread` is disabled in this profile because bin-packing and zone-spreading are opposing goals and the scheduling logic may prioritize pod spreading. If you need both high utilization AND zone resilience, define a new profile to achieve both goals as the default scheduler's behavior resort to pod spreading. +`RequestedToCapacityRatio` scores nodes based on the ratio of requested resources to total node capacity after the pod is _hypothetically_ placed. This strategy enables more fine-grained bin‑packing by allowing operators to define an ideal utilization curve for their nodes rather than simply preferring the most or least utilized nodes. Lastly, it is critical to note that `PodTopologySpread` is disabled in this profile because bin-packing and zone-spreading are opposing goals and the scheduling logic may prioritize pod spreading. If you need both high utilization _and_ zone resilience, define a new profile to achieve both goals. By shaping the scoring curve to target a range of 50-85% CPU utilization, operators can increase pod density on provisioned nodes while preserving headroom for bursts, background processes, and system components in CPU-based workloads. [Configure node bin-packing][configure-requested-to-capacity] using the `RequestedToCapacityRatio` strategy to improve utilization and reduce infrastructure costs. @@ -184,7 +184,7 @@ spec: 1. Which Bin packing strategy does AKS recommend to increase node utilization? AKS recommends using the scoring strategy `RequestedToCapacityRatio` because it provides a more granular scoring approach allowing users to define an ideal utilization curve for their respective nodes. For example, this bin packing strategy allows users to configure a target utilization of 85%. 2. How does Configurable Scheduler Profiles interact with autoscalers such as Node Auto Provisioning (NAP), Cluster Autoscaler (CA), and Vertical Pod Autoscaler (VPA)? These components are complementary to each other. Configurable Scheduler Profiles influence how pods are placed on nodes, while autoscalers make scaling decisions based on resource utilization and pending pods. - - **Node Auto Provisioning (NAP)** is triggered when pods are unschedulable. If a suitable node already exists, that pod will be scheduled with the defined Configurable Scheduler Profile. If no suitable node exists, NAP provisions new capacity and schedules pod. + - **Node Auto Provisioning (NAP)** is triggered when pods are unschedulable. If a suitable node already exists, that pod will be scheduled with the defined Configurable Scheduler Profile. If no suitable node exists, NAP provisions new capacity and schedules the pod. - **Cluster Autoscaler (CA)** manages node scale-up and scale-down. On scale-up, CA is triggered when there aren't any suitable nodes available for the pending pod. Using Configurable Scheduler Profiles ensures nodes are only scaled when provisioned resources are no longer suitable. On scale-down, CA is triggered when nodes fall below utilization thresholds, the default is 50%. As active nodes are packed more efficiently, underutilized nodes become easier candidates for removal. - **VPA** optimizes resource utilization patterns in pods. As pods are recreated with updated CPU and memory requests, they are scheduled using the configured scheduler profile, allowing placement decisions to reflect the new resource requirements. 3. What if a resource, like `memory`, is omitted in the `scoringStrategy`? If a resource is omitted in the `scoringStrategy`, then that resource will not be considered in the filter or scoring cycles of the defined Configurable Scheduler Profile. If that resource should be considered, but with a reduced influence on the final score, it can be included with reduced weight. @@ -200,7 +200,7 @@ spec: With Configurable Scheduler Profiles, teams gain fine-grained control over pod placement strategies like bin-packing, topology distribution, and resource-based scoring that directly addresses challenges related to application resilience and resource utilization for their AKS clusters. By leveraging these scheduling plugins, AKS users can ensure their workloads make full use of available GPU capacity, reduce idle costs, and avoid costly overprovisioning. This not only improves ROI but also accelerates development by allowing more jobs to run concurrently and reliably. -- For best practices using the kube-scheduler visit [kube-scheduler best practices][best-practices-advanced-scheduler] +- For additional guidance and best practices, see [kube-scheduler best practices][best-practices-advanced-scheduler] - Increase node utilization using [Configurable Scheduler Profiles][node-bin-packing-configurations] - If additional capabilities or ML frameworks are needed to schedule and queue batch workloads, you can [install and configure Kueue on AKS][kueue-overview] to ensure efficient, policy-driven scheduling in AKS clusters. From 3c0bab94afe7df7743ae60b768a9d0608bffa843 Mon Sep 17 00:00:00 2001 From: colinmixonn <109253437+colinmixonn@users.noreply.github.com> Date: Mon, 30 Mar 2026 14:29:17 -0700 Subject: [PATCH 131/164] Revise scheduler profiles table and descriptions Updated the table format for scheduler profiles and clarified the benefits of the 'RequestedToCapacityRatio' strategy. --- .../index.md | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/website/blog/2026-03-31-aks-config-scheduler-profiles-preview/index.md b/website/blog/2026-03-31-aks-config-scheduler-profiles-preview/index.md index 3b44d32ed..cd339e2a7 100644 --- a/website/blog/2026-03-31-aks-config-scheduler-profiles-preview/index.md +++ b/website/blog/2026-03-31-aks-config-scheduler-profiles-preview/index.md @@ -64,11 +64,11 @@ While this experiment uses intentionally simple, CPU-bound containers to isolate ![Table showing increased node utilization with the node bin packing scheduler profiles versus the pod distribution using the default scheduler](./default-config-scheduler-comparison.png) The key takeaway is that each profile expresses a distinct scheduling intent. The next two sections detail how MostAllocated and RequestedToCapacityRatio achieve these outcomes. -| Scheduler / Profile | Scheduling intent | Operator Benefits | -|---|---|---| -| Default scheduler
NodeResourcesFit: LeastAllocated | Balance and hotspot reduction | No tuning | -| Configurable Scheduler Profile
NodeResourcesFit: MostAllocated | Maximize consolidation / bin‑packing | Maximum node utilization, highest cost reduction potential | -| Configurable Scheduler Profile
NodeResourcesFit: RequestedToCapacityRatio | Targeted utilization with headroom | Increased utilization with stronger control over consolidation and burst headroom than `MostAllocated` | +| Scheduler | Profile | Scheduling intent | Operator Benefits | +|---|---|---|---| +| Default scheduler | NodeResourcesFit: LeastAllocated | Balance and hotspot reduction | No tuning | +| Configurable Scheduler Profile | NodeResourcesFit: MostAllocated | Maximize consolidation / bin‑packing | Maximum node utilization, highest cost reduction potential | +| Configurable Scheduler Profile | NodeResourcesFit: RequestedToCapacityRatio | Targeted utilization with headroom | ✅**Recommended Strategy** Increased utilization with stronger control over consolidation and burst headroom than `MostAllocated` | ### Increase AKS CPU utilization From dff3207e59dee4a6d2eb60e2d704a7ce3cd72d73 Mon Sep 17 00:00:00 2001 From: colinmixonn <109253437+colinmixonn@users.noreply.github.com> Date: Mon, 30 Mar 2026 14:47:03 -0700 Subject: [PATCH 132/164] Refactor FAQ section in AKS Configurable Scheduler Profiles Reformatted FAQ section for clarity and improved readability. --- .../index.md | 17 ++++++++++++++--- 1 file changed, 14 insertions(+), 3 deletions(-) diff --git a/website/blog/2026-03-31-aks-config-scheduler-profiles-preview/index.md b/website/blog/2026-03-31-aks-config-scheduler-profiles-preview/index.md index cd339e2a7..ab64b6914 100644 --- a/website/blog/2026-03-31-aks-config-scheduler-profiles-preview/index.md +++ b/website/blog/2026-03-31-aks-config-scheduler-profiles-preview/index.md @@ -182,15 +182,26 @@ spec: ### FAQ -1. Which Bin packing strategy does AKS recommend to increase node utilization? AKS recommends using the scoring strategy `RequestedToCapacityRatio` because it provides a more granular scoring approach allowing users to define an ideal utilization curve for their respective nodes. For example, this bin packing strategy allows users to configure a target utilization of 85%. -2. How does Configurable Scheduler Profiles interact with autoscalers such as Node Auto Provisioning (NAP), Cluster Autoscaler (CA), and Vertical Pod Autoscaler (VPA)? These components are complementary to each other. Configurable Scheduler Profiles influence how pods are placed on nodes, while autoscalers make scaling decisions based on resource utilization and pending pods. +1. Which Bin packing strategy does AKS recommend to increase node utilization? + +AKS recommends using the scoring strategy `RequestedToCapacityRatio` because it provides a more granular scoring approach allowing users to define an ideal utilization curve for their respective nodes. For example, this bin packing strategy allows users to configure a target utilization of 85%. + +2. How does Configurable Scheduler Profiles interact with autoscalers such as Node Auto Provisioning (NAP), Cluster Autoscaler (CA), and Vertical Pod Autoscaler (VPA)? + +These components are complementary to each other. Configurable Scheduler Profiles influence how pods are placed on nodes, while autoscalers make scaling decisions based on resource utilization and pending pods. + - **Node Auto Provisioning (NAP)** is triggered when pods are unschedulable. If a suitable node already exists, that pod will be scheduled with the defined Configurable Scheduler Profile. If no suitable node exists, NAP provisions new capacity and schedules the pod. - **Cluster Autoscaler (CA)** manages node scale-up and scale-down. On scale-up, CA is triggered when there aren't any suitable nodes available for the pending pod. Using Configurable Scheduler Profiles ensures nodes are only scaled when provisioned resources are no longer suitable. On scale-down, CA is triggered when nodes fall below utilization thresholds, the default is 50%. As active nodes are packed more efficiently, underutilized nodes become easier candidates for removal. - **VPA** optimizes resource utilization patterns in pods. As pods are recreated with updated CPU and memory requests, they are scheduled using the configured scheduler profile, allowing placement decisions to reflect the new resource requirements. -3. What if a resource, like `memory`, is omitted in the `scoringStrategy`? If a resource is omitted in the `scoringStrategy`, then that resource will not be considered in the filter or scoring cycles of the defined Configurable Scheduler Profile. If that resource should be considered, but with a reduced influence on the final score, it can be included with reduced weight. + +3. What if a resource, like `memory`, is omitted in the `scoringStrategy`? + +If a resource is omitted in the `scoringStrategy`, then that resource will not be considered in the filter or scoring cycles of the defined Configurable Scheduler Profile. If that resource should be considered, but with a reduced influence on the final score, it can be included with reduced weight. + 4. Can multiple Configurable Scheduler Profiles be used for different workloads on the same cluster? Yes, multiple scheduling profiles can coexist in a single cluster. This allows different placement strategies (for example, cost‑optimized vs. latency‑sensitive workloads) to run side‑by‑side. Visit the documentation for a [multiple scheduler profiles example][configure-multi-config] - Multiple profiles can be defined centrally in a single scheduler configuration. - Individual workloads select a profile via `schedulerName` in the pod spec. + 5. How do I monitor whether my scheduler profile is improving utilization? Monitor these signals to confirm that the scheduler is behaving correctly. Over time, you should see higher average node utilization, reduced variance between nodes, and fewer lightly utilized nodes. - Track node‑level utilization metrics, including CPU and memory utilization per node and distribution of pods across nodes, using Azure Monitor Container Insights and `kubectl top nodes` for quick validation. - Review autoscaler outcomes, looking for fewer scale‑ups during normal load and more decisive scale‑downs after demand drops. From a8debba90a203f09fbd0143c6058bee73bd4e5c1 Mon Sep 17 00:00:00 2001 From: colinmixonn <109253437+colinmixonn@users.noreply.github.com> Date: Tue, 31 Mar 2026 16:55:07 -0700 Subject: [PATCH 133/164] Enhance clarity and consistency in AKS scheduler blog Refine language for clarity and consistency in the blog post about AKS Configurable Scheduler Profiles, ensuring technical accuracy and improving readability. --- .../index.md | 11 ++++++----- 1 file changed, 6 insertions(+), 5 deletions(-) diff --git a/website/blog/2026-03-31-aks-config-scheduler-profiles-preview/index.md b/website/blog/2026-03-31-aks-config-scheduler-profiles-preview/index.md index ab64b6914..81ee88f93 100644 --- a/website/blog/2026-03-31-aks-config-scheduler-profiles-preview/index.md +++ b/website/blog/2026-03-31-aks-config-scheduler-profiles-preview/index.md @@ -57,7 +57,7 @@ A profile is a set of one or more in-tree scheduling plugins and configurations ## Increase node utilization and operator control -In this simple scale-out scenario, when you manually increase replicas from 8 to 30 with identical pod specs, the default scheduler distributes pods evenly across nodes. Configurable Scheduler Profiles that use the `NodeResourcesFit` plugin show a visible consolidation pattern that differs from the default scheduler's logic. Instead of spreading pods evenly, the scheduler begins to intentionally concentrate workloads onto fewer nodes. This shift occurs without changing the workload, node size, or autoscaling behavior - only the scheduler’s scoring logic. +In this simple scale-out scenario, when you manually increase replicas from 8 to 30 with identical pod specs, the default scheduler distributes pods evenly across nodes. Configurable Scheduler Profiles that use the `NodeResourcesFit` plugin show a visible consolidation pattern that differs from the default scheduler's logic. Instead of spreading pods evenly, the scheduler intentionally concentrates workloads onto fewer nodes. This shift occurs without changing the workload, node size, or autoscaling behavior - only the scheduler’s scoring logic. While this experiment uses intentionally simple, CPU-bound containers to isolate scheduling behavior, the placement patterns observed here generalize to more complex workloads where consolidation and utilization efficiency matter. This change in distribution shape enables downstream efficiencies: improved control for platform engineers, efficient resource usage, and cost optimization that are difficult to achieve when pods are evenly spread. @@ -184,11 +184,11 @@ spec: 1. Which Bin packing strategy does AKS recommend to increase node utilization? -AKS recommends using the scoring strategy `RequestedToCapacityRatio` because it provides a more granular scoring approach allowing users to define an ideal utilization curve for their respective nodes. For example, this bin packing strategy allows users to configure a target utilization of 85%. + AKS recommends using the scoring strategy `RequestedToCapacityRatio` because it provides a more granular scoring approach allowing users to define an ideal utilization curve for their respective nodes. For example, this bin packing strategy allows users to configure a target utilization of 85%. 2. How does Configurable Scheduler Profiles interact with autoscalers such as Node Auto Provisioning (NAP), Cluster Autoscaler (CA), and Vertical Pod Autoscaler (VPA)? -These components are complementary to each other. Configurable Scheduler Profiles influence how pods are placed on nodes, while autoscalers make scaling decisions based on resource utilization and pending pods. + These components are complementary to each other. Configurable Scheduler Profiles influence how pods are placed on nodes, while autoscalers make scaling decisions based on resource utilization and pending pods. - **Node Auto Provisioning (NAP)** is triggered when pods are unschedulable. If a suitable node already exists, that pod will be scheduled with the defined Configurable Scheduler Profile. If no suitable node exists, NAP provisions new capacity and schedules the pod. - **Cluster Autoscaler (CA)** manages node scale-up and scale-down. On scale-up, CA is triggered when there aren't any suitable nodes available for the pending pod. Using Configurable Scheduler Profiles ensures nodes are only scaled when provisioned resources are no longer suitable. On scale-down, CA is triggered when nodes fall below utilization thresholds, the default is 50%. As active nodes are packed more efficiently, underutilized nodes become easier candidates for removal. @@ -196,14 +196,14 @@ These components are complementary to each other. Configurable Scheduler Profile 3. What if a resource, like `memory`, is omitted in the `scoringStrategy`? -If a resource is omitted in the `scoringStrategy`, then that resource will not be considered in the filter or scoring cycles of the defined Configurable Scheduler Profile. If that resource should be considered, but with a reduced influence on the final score, it can be included with reduced weight. + If a resource is omitted in the `scoringStrategy`, then that resource will not be considered in the filter or scoring cycles of the defined Configurable Scheduler Profile. If that resource should be considered, but with a reduced influence on the final score, it can be included with reduced weight. 4. Can multiple Configurable Scheduler Profiles be used for different workloads on the same cluster? Yes, multiple scheduling profiles can coexist in a single cluster. This allows different placement strategies (for example, cost‑optimized vs. latency‑sensitive workloads) to run side‑by‑side. Visit the documentation for a [multiple scheduler profiles example][configure-multi-config] - Multiple profiles can be defined centrally in a single scheduler configuration. - Individual workloads select a profile via `schedulerName` in the pod spec. 5. How do I monitor whether my scheduler profile is improving utilization? Monitor these signals to confirm that the scheduler is behaving correctly. Over time, you should see higher average node utilization, reduced variance between nodes, and fewer lightly utilized nodes. - - Track node‑level utilization metrics, including CPU and memory utilization per node and distribution of pods across nodes, using Azure Monitor Container Insights and `kubectl top nodes` for quick validation. + - Track node‑level utilization metrics, including CPU and memory utilization per node and distribution of pods across nodes, using Azure Monitor Container Insights, and AKS node viewer [tool][aks-node-viewer] or and `kubectl top nodes` for quick validation. - Review autoscaler outcomes, looking for fewer scale‑ups during normal load and more decisive scale‑downs after demand drops. - Measure cost metrics, such as reduced idle costs when you use the [Cost Analysis add-on][aks-cost-analysis-add-on]. @@ -228,3 +228,4 @@ With Configurable Scheduler Profiles, teams gain fine-grained control over pod p [aks-cost-analysis-add-on]: https://learn.microsoft.com/azure/aks/cost-analysis [wb-gpu-utilization]: https://wandb.ai/wandb_fc/articles/reports/Monitor-Improve-GPU-Usage-for-Model-Training--Vmlldzo1NDQzNjM3 [deep-dive-scheduler-framework]: https://helayoty.org/blog/inside-kube-scheduler-the-plugin +[aks-node-viewer]: https://github.com/Azure/aks-node-viewer From e3de1dd72379b0cffb45b07fa4ce76eb6b4baac0 Mon Sep 17 00:00:00 2001 From: colinmixonn <109253437+colinmixonn@users.noreply.github.com> Date: Tue, 31 Mar 2026 17:07:43 -0700 Subject: [PATCH 134/164] Refactor blog post on AKS Configurable Scheduler Profiles Revised blog content for clarity and improved flow regarding Configurable Scheduler Profiles on AKS. --- .../index.md | 11 +++++++---- 1 file changed, 7 insertions(+), 4 deletions(-) diff --git a/website/blog/2026-03-31-aks-config-scheduler-profiles-preview/index.md b/website/blog/2026-03-31-aks-config-scheduler-profiles-preview/index.md index 81ee88f93..fbe775929 100644 --- a/website/blog/2026-03-31-aks-config-scheduler-profiles-preview/index.md +++ b/website/blog/2026-03-31-aks-config-scheduler-profiles-preview/index.md @@ -13,7 +13,7 @@ In 2025, Datadog found most Kubernetes containers use less than [25% of their re This blog explains how the default Kubernetes scheduler places pods, where the defaults fall short, and how to increase node utilization using Configurable Scheduler Profiles on AKS. 1. [How does kube-scheduler work?](#how-does-the-default-kubernetes-scheduler-place-pods) -2. [Use Configurable Scheduler Profiles to increase node utilization and operator control](#configurable-scheduler-profiles-on-aks) +2. [Use Configurable Scheduler Profiles to increase node utilization and operator control](#increase-node-utilization-and-operator-control) 3. [Increase AKS cluster CPU utilization up to 85% with Configurable Scheduler](#increase-aks-cpu-utilization) 4. [Increase AKS cluster GPU or CPU utilization while balancing memory with Configurable Scheduler](#increase-aks-gpu-utilization) 5. [FAQ: How do Configurable Scheduler Profiles interact with autoscalers?](#faq) @@ -57,14 +57,17 @@ A profile is a set of one or more in-tree scheduling plugins and configurations ## Increase node utilization and operator control -In this simple scale-out scenario, when you manually increase replicas from 8 to 30 with identical pod specs, the default scheduler distributes pods evenly across nodes. Configurable Scheduler Profiles that use the `NodeResourcesFit` plugin show a visible consolidation pattern that differs from the default scheduler's logic. Instead of spreading pods evenly, the scheduler intentionally concentrates workloads onto fewer nodes. This shift occurs without changing the workload, node size, or autoscaling behavior - only the scheduler’s scoring logic. +The default scheduler distributes pods evenly across nodes in this simple scale-out scenario, manually increasing CPU-bound replicas from 8 to 30 with identical pod specs. + +Configurable Scheduler Profiles that use the `NodeResourcesFit` plugin show a visible consolidation pattern that differs from the default scheduler. This shift occurs without changing the workload, node size, or autoscaling behavior - only the scheduler’s scoring logic. While this experiment uses intentionally simple, CPU-bound containers to isolate scheduling behavior, the placement patterns observed here generalize to more complex workloads where consolidation and utilization efficiency matter. This change in distribution shape enables downstream efficiencies: improved control for platform engineers, efficient resource usage, and cost optimization that are difficult to achieve when pods are evenly spread. ![Table showing increased node utilization with the node bin packing scheduler profiles versus the pod distribution using the default scheduler](./default-config-scheduler-comparison.png) -The key takeaway is that each profile expresses a distinct scheduling intent. The next two sections detail how MostAllocated and RequestedToCapacityRatio achieve these outcomes. -| Scheduler | Profile | Scheduling intent | Operator Benefits | +**The key takeaway is that each profile expresses a distinct scheduling intent. The next two sections detail how the scoring strategies, MostAllocated and RequestedToCapacityRatio achieve these outcomes.** + +| Scheduler | Scoring Strategy | Scheduling intent | Operator Benefits | |---|---|---|---| | Default scheduler | NodeResourcesFit: LeastAllocated | Balance and hotspot reduction | No tuning | | Configurable Scheduler Profile | NodeResourcesFit: MostAllocated | Maximize consolidation / bin‑packing | Maximum node utilization, highest cost reduction potential | From 10dfecc34dd80ab674666348324257ea902fc9d6 Mon Sep 17 00:00:00 2001 From: colinmixonn <109253437+colinmixonn@users.noreply.github.com> Date: Wed, 1 Apr 2026 11:35:21 -0700 Subject: [PATCH 135/164] Apply suggestions from code review Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> --- .../index.md | 14 ++++++++------ 1 file changed, 8 insertions(+), 6 deletions(-) diff --git a/website/blog/2026-03-31-aks-config-scheduler-profiles-preview/index.md b/website/blog/2026-03-31-aks-config-scheduler-profiles-preview/index.md index fbe775929..c42c495cc 100644 --- a/website/blog/2026-03-31-aks-config-scheduler-profiles-preview/index.md +++ b/website/blog/2026-03-31-aks-config-scheduler-profiles-preview/index.md @@ -71,7 +71,7 @@ While this experiment uses intentionally simple, CPU-bound containers to isolate |---|---|---|---| | Default scheduler | NodeResourcesFit: LeastAllocated | Balance and hotspot reduction | No tuning | | Configurable Scheduler Profile | NodeResourcesFit: MostAllocated | Maximize consolidation / bin‑packing | Maximum node utilization, highest cost reduction potential | -| Configurable Scheduler Profile | NodeResourcesFit: RequestedToCapacityRatio | Targeted utilization with headroom | ✅**Recommended Strategy** Increased utilization with stronger control over consolidation and burst headroom than `MostAllocated` | +| Configurable Scheduler Profile | NodeResourcesFit: RequestedToCapacityRatio | Targeted utilization with headroom | ✅ **Recommended strategy:** Increased utilization with stronger control over consolidation and burst headroom than `MostAllocated` | ### Increase AKS CPU utilization @@ -185,11 +185,11 @@ spec: ### FAQ -1. Which Bin packing strategy does AKS recommend to increase node utilization? +1. Which bin-packing strategy does AKS recommend to increase node utilization? - AKS recommends using the scoring strategy `RequestedToCapacityRatio` because it provides a more granular scoring approach allowing users to define an ideal utilization curve for their respective nodes. For example, this bin packing strategy allows users to configure a target utilization of 85%. +AKS recommends using the scoring strategy `RequestedToCapacityRatio` because it provides a more granular scoring approach allowing users to define an ideal utilization curve for their respective nodes. For example, this bin packing strategy allows users to configure a target utilization of 85%. -2. How does Configurable Scheduler Profiles interact with autoscalers such as Node Auto Provisioning (NAP), Cluster Autoscaler (CA), and Vertical Pod Autoscaler (VPA)? +2. How do Configurable Scheduler Profiles interact with autoscalers such as Node Auto Provisioning (NAP), Cluster Autoscaler (CA), and Vertical Pod Autoscaler (VPA)? These components are complementary to each other. Configurable Scheduler Profiles influence how pods are placed on nodes, while autoscalers make scaling decisions based on resource utilization and pending pods. @@ -201,12 +201,14 @@ spec: If a resource is omitted in the `scoringStrategy`, then that resource will not be considered in the filter or scoring cycles of the defined Configurable Scheduler Profile. If that resource should be considered, but with a reduced influence on the final score, it can be included with reduced weight. -4. Can multiple Configurable Scheduler Profiles be used for different workloads on the same cluster? Yes, multiple scheduling profiles can coexist in a single cluster. This allows different placement strategies (for example, cost‑optimized vs. latency‑sensitive workloads) to run side‑by‑side. Visit the documentation for a [multiple scheduler profiles example][configure-multi-config] +4. Can multiple Configurable Scheduler Profiles be used for different workloads on the same cluster? + + Yes, multiple scheduling profiles can coexist in a single cluster. This allows different placement strategies (for example, cost‑optimized vs. latency‑sensitive workloads) to run side‑by‑side. Visit the documentation for a [multiple scheduler profiles example][configure-multi-config] - Multiple profiles can be defined centrally in a single scheduler configuration. - Individual workloads select a profile via `schedulerName` in the pod spec. 5. How do I monitor whether my scheduler profile is improving utilization? Monitor these signals to confirm that the scheduler is behaving correctly. Over time, you should see higher average node utilization, reduced variance between nodes, and fewer lightly utilized nodes. - - Track node‑level utilization metrics, including CPU and memory utilization per node and distribution of pods across nodes, using Azure Monitor Container Insights, and AKS node viewer [tool][aks-node-viewer] or and `kubectl top nodes` for quick validation. + - Track node‑level utilization metrics, including CPU and memory utilization per node and distribution of pods across nodes, using Azure Monitor Container Insights, the AKS node viewer [tool][aks-node-viewer], or `kubectl top nodes` for quick validation. - Review autoscaler outcomes, looking for fewer scale‑ups during normal load and more decisive scale‑downs after demand drops. - Measure cost metrics, such as reduced idle costs when you use the [Cost Analysis add-on][aks-cost-analysis-add-on]. From 884a80cf013f8b0606074da6742b3e830efb006b Mon Sep 17 00:00:00 2001 From: colinmixonn <109253437+colinmixonn@users.noreply.github.com> Date: Wed, 1 Apr 2026 11:58:12 -0700 Subject: [PATCH 136/164] Refine content on kube-scheduler and scheduler profiles Updated text for clarity and emphasis on the role of kube-scheduler. Enhanced descriptions of Configurable Scheduler Profiles and their benefits for node utilization. --- .../index.md | 20 +++++++++++-------- 1 file changed, 12 insertions(+), 8 deletions(-) diff --git a/website/blog/2026-03-31-aks-config-scheduler-profiles-preview/index.md b/website/blog/2026-03-31-aks-config-scheduler-profiles-preview/index.md index c42c495cc..da47fea8c 100644 --- a/website/blog/2026-03-31-aks-config-scheduler-profiles-preview/index.md +++ b/website/blog/2026-03-31-aks-config-scheduler-profiles-preview/index.md @@ -6,9 +6,9 @@ authors: [colin-mixon] tags: [ai, performance, scheduler, best-practices, cost] --- -In 2025, Datadog found most Kubernetes containers use less than [25% of their requested CPU][datadog-state-of-containers], and in 2023 Weights and Biases found that nearly a third of GPU users [average less than 15% utilization][wb-gpu-utilization]. This data signals that underutilized resources materially contribute to increased infrastructure cost. While there are many factors that impact node utilization, as a core component of the Kubernetes control plane, the kube-scheduler has a big influence on node utilization. +In 2025, Datadog found most Kubernetes containers use less than [25% of their requested CPU][datadog-state-of-containers], and in 2023, Weights and Biases found that nearly a third of GPU users [average less than 15% utilization][wb-gpu-utilization]. This data signals that underutilized resources materially contribute to increased infrastructure cost. While there are many factors that impact node utilization, as a core component of the Kubernetes control plane, the kube-scheduler plays a critical role in node utilization. -[Configurable Scheduler Profiles][concepts-scheduler-configuration] on AKS lets customers configure their own scheduling logic: enable specific plugins, adjust plugin priorities, and tune parameter weights. The result: higher node density, better GPU utilization, and lower infrastructure costs. +[Configurable Scheduler Profiles][concepts-scheduler-configuration] on AKS lets customers configure their own scheduling logic: enable specific plugins, adjust plugin priorities, and tune parameter weights. **The result: higher node density, better GPU utilization, and lower infrastructure costs.** This blog explains how the default Kubernetes scheduler places pods, where the defaults fall short, and how to increase node utilization using Configurable Scheduler Profiles on AKS. @@ -43,9 +43,9 @@ For a deep dive on the Kubernetes Scheduler visit technical blog from SIG Schedu ### Limitations of the default Kubernetes scheduler -The default scheduler is primarily designed for general-purpose workloads that prioritize nodes with the most available resources using the _LeastAllocated_ scoring strategy. This spreads pods across nodes, even when they could safely be packed more densely. While this works well for many services, the default scheduling criteria, and their fixed priority order, are not suitable for workloads that demand optimizing GPU and CPU utilization. In these scenarios, spreading pods across nodes can lead to fragmented resources, underutilized GPUs, and increased infrastructure cost. +The default scheduler is primarily designed for general-purpose workloads. It prioritizes nodes with the most available resources using the _LeastAllocated_ scoring strategy. This strategy spreads pods across nodes, even when they could safely be packed more densely. While this works well for many services, the default scheduling criteria, and their fixed priority order, are not suitable for workloads that demand optimizing GPU and CPU utilization. In these scenarios, spreading pods across nodes can lead to fragmented resources, underutilized GPUs, and increased infrastructure cost. -Today, the default scheduler on AKS lacks the flexibility for users to change which criteria should be prioritized, or ignored, in the scheduling cycle on a per cluster basis. This rigidity often forces users to either accept suboptimal placement or manage a separate custom scheduler, both of which increase operational complexity. Starting with Kubernetes v1.33, AKS introduces Configurable Scheduler Profiles - an AKS-managed CRD - that exposes the upstream scheduling framework without maintaining a separate scheduler. Now, users can adjust the `NodeResourcesFit` plugin from the default configuration to favor nodes with higher utilization to achieve more efficient bin‑packing and reduce infrastructure cost. +Today, the default scheduler on AKS lacks the flexibility for users to change which criteria should be prioritized, or ignored, in the scheduling cycle on a per cluster basis. This rigidity often forces users to either accept suboptimal placement or manage a separate custom scheduler, both of which increase operational complexity. Starting with Kubernetes v1.33, AKS introduces [Configurable Scheduler Profiles][concepts-scheduler-configuration] - an AKS-managed CRD - that exposes the upstream scheduling framework without maintaining a separate scheduler. Now, users can adjust the `NodeResourcesFit` plugin from the default configuration to favor nodes with higher utilization to achieve more efficient bin‑packing and reduce infrastructure cost. ## Configurable Scheduler Profiles on AKS @@ -57,15 +57,19 @@ A profile is a set of one or more in-tree scheduling plugins and configurations ## Increase node utilization and operator control -The default scheduler distributes pods evenly across nodes in this simple scale-out scenario, manually increasing CPU-bound replicas from 8 to 30 with identical pod specs. +In this simple scale-out scenario, manually increasing identitical CPU-bound replicas from 8 to 30, the default scheduler distributes pods evenly across nodes. -Configurable Scheduler Profiles that use the `NodeResourcesFit` plugin show a visible consolidation pattern that differs from the default scheduler. This shift occurs without changing the workload, node size, or autoscaling behavior - only the scheduler’s scoring logic. +Configurable Scheduler Profiles configured for bin-packing show a visible consolidation pattern that differs from the default scheduler, and improves capacity for new pods. This shift occurs without changing the workload, node size, or autoscaling behavior - only the scheduler’s scoring logic. -While this experiment uses intentionally simple, CPU-bound containers to isolate scheduling behavior, the placement patterns observed here generalize to more complex workloads where consolidation and utilization efficiency matter. This change in distribution shape enables downstream efficiencies: improved control for platform engineers, efficient resource usage, and cost optimization that are difficult to achieve when pods are evenly spread. +**Scenario: Increase CPU utilization** ![Table showing increased node utilization with the node bin packing scheduler profiles versus the pod distribution using the default scheduler](./default-config-scheduler-comparison.png) -**The key takeaway is that each profile expresses a distinct scheduling intent. The next two sections detail how the scoring strategies, MostAllocated and RequestedToCapacityRatio achieve these outcomes.** +While this experiment uses intentionally simple, CPU-bound containers to isolate scheduling behavior, the placement patterns observed here can be applied to GPU-bound workloads where consolidation and utilization efficiency matter. In this constrained GPU scenario the bin‑packing scheduler plays a critical role in maximizing usable capacity of 4 GPU's spread across 2 nodes. Consolidating single‑GPU workloads onto the same node instead of spreading them evenly, the scheduler avoids stranding idle GPUs and enables all four GPUs to be actively used before new nodes are required. + +**Scenario: Increase GPU utilization** + +This change in distribution shape enables downstream efficiencies: improved control for platform engineers, efficient resource usage, and cost optimization that are difficult to achieve when pods are evenly spread. **The key takeaway is that each profile expresses a distinct scheduling intent. The next two sections detail how the scoring strategies, MostAllocated and RequestedToCapacityRatio achieve these outcomes.** | Scheduler | Scoring Strategy | Scheduling intent | Operator Benefits | |---|---|---|---| From fca01d2b12dfe2a68241944ffc21a003aabbb8f1 Mon Sep 17 00:00:00 2001 From: colinmixonn <109253437+colinmixonn@users.noreply.github.com> Date: Wed, 1 Apr 2026 12:11:33 -0700 Subject: [PATCH 137/164] Apply suggestions from code review Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> --- .../index.md | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/website/blog/2026-03-31-aks-config-scheduler-profiles-preview/index.md b/website/blog/2026-03-31-aks-config-scheduler-profiles-preview/index.md index da47fea8c..725e50d0b 100644 --- a/website/blog/2026-03-31-aks-config-scheduler-profiles-preview/index.md +++ b/website/blog/2026-03-31-aks-config-scheduler-profiles-preview/index.md @@ -57,7 +57,7 @@ A profile is a set of one or more in-tree scheduling plugins and configurations ## Increase node utilization and operator control -In this simple scale-out scenario, manually increasing identitical CPU-bound replicas from 8 to 30, the default scheduler distributes pods evenly across nodes. +In this simple scale-out scenario, manually increasing identical CPU-bound replicas from 8 to 30, the default scheduler distributes pods evenly across nodes. Configurable Scheduler Profiles configured for bin-packing show a visible consolidation pattern that differs from the default scheduler, and improves capacity for new pods. This shift occurs without changing the workload, node size, or autoscaling behavior - only the scheduler’s scoring logic. @@ -212,10 +212,10 @@ AKS recommends using the scoring strategy `RequestedToCapacityRatio` because it - Individual workloads select a profile via `schedulerName` in the pod spec. 5. How do I monitor whether my scheduler profile is improving utilization? Monitor these signals to confirm that the scheduler is behaving correctly. Over time, you should see higher average node utilization, reduced variance between nodes, and fewer lightly utilized nodes. - - Track node‑level utilization metrics, including CPU and memory utilization per node and distribution of pods across nodes, using Azure Monitor Container Insights, the AKS node viewer [tool][aks-node-viewer], or `kubectl top nodes` for quick validation. - - Review autoscaler outcomes, looking for fewer scale‑ups during normal load and more decisive scale‑downs after demand drops. - - Measure cost metrics, such as reduced idle costs when you use the [Cost Analysis add-on][aks-cost-analysis-add-on]. + - Track node‑level utilization metrics, including CPU and memory utilization per node and distribution of pods across nodes, using Azure Monitor Container Insights, the AKS node viewer [tool][aks-node-viewer], or `kubectl top nodes` for quick validation. + - Review autoscaler outcomes, looking for fewer scale‑ups during normal load and more decisive scale‑downs after demand drops. + - Measure cost metrics, such as reduced idle costs when you use the [Cost Analysis add-on][aks-cost-analysis-add-on]. ## Next steps: Optimize Azure resources and test Configurable Scheduler Profiles on AKS With Configurable Scheduler Profiles, teams gain fine-grained control over pod placement strategies like bin-packing, topology distribution, and resource-based scoring that directly addresses challenges related to application resilience and resource utilization for their AKS clusters. By leveraging these scheduling plugins, AKS users can ensure their workloads make full use of available GPU capacity, reduce idle costs, and avoid costly overprovisioning. This not only improves ROI but also accelerates development by allowing more jobs to run concurrently and reliably. From 0cc3c401bd21721f2cdf5af13593adab8a9482cd Mon Sep 17 00:00:00 2001 From: colinmixonn <109253437+colinmixonn@users.noreply.github.com> Date: Wed, 1 Apr 2026 13:20:27 -0700 Subject: [PATCH 138/164] Revise scenarios and FAQs for scheduler profiles Updated scenarios and FAQs for Configurable Scheduler Profiles in AKS, enhancing clarity and detail on CPU and GPU utilization strategies. --- .../index.md | 36 ++++++++++--------- 1 file changed, 20 insertions(+), 16 deletions(-) diff --git a/website/blog/2026-03-31-aks-config-scheduler-profiles-preview/index.md b/website/blog/2026-03-31-aks-config-scheduler-profiles-preview/index.md index 725e50d0b..e69260b1a 100644 --- a/website/blog/2026-03-31-aks-config-scheduler-profiles-preview/index.md +++ b/website/blog/2026-03-31-aks-config-scheduler-profiles-preview/index.md @@ -61,13 +61,13 @@ In this simple scale-out scenario, manually increasing identical CPU-bound repli Configurable Scheduler Profiles configured for bin-packing show a visible consolidation pattern that differs from the default scheduler, and improves capacity for new pods. This shift occurs without changing the workload, node size, or autoscaling behavior - only the scheduler’s scoring logic. -**Scenario: Increase CPU utilization** +#### Scenario: Increase CPU utilization ![Table showing increased node utilization with the node bin packing scheduler profiles versus the pod distribution using the default scheduler](./default-config-scheduler-comparison.png) -While this experiment uses intentionally simple, CPU-bound containers to isolate scheduling behavior, the placement patterns observed here can be applied to GPU-bound workloads where consolidation and utilization efficiency matter. In this constrained GPU scenario the bin‑packing scheduler plays a critical role in maximizing usable capacity of 4 GPU's spread across 2 nodes. Consolidating single‑GPU workloads onto the same node instead of spreading them evenly, the scheduler avoids stranding idle GPUs and enables all four GPUs to be actively used before new nodes are required. +While this experiment uses intentionally simple, CPU-bound containers to isolate scheduling behavior, the placement patterns observed here can be applied to GPU-bound workloads where consolidation and utilization efficiency matter. In this constrained GPU scenario the bin‑packing scheduler plays a critical role in maximizing usable capacity of 4 GPU's spread across 2 nodes. Consolidating single‑GPU workloads onto the same node instead of spreading them evenly, the scheduler avoids stranding idle GPUs and enables all four GPUs to be actively used before new nodes are required. -**Scenario: Increase GPU utilization** +#### Scenario: Increase GPU utilization This change in distribution shape enables downstream efficiencies: improved control for platform engineers, efficient resource usage, and cost optimization that are difficult to achieve when pods are evenly spread. **The key takeaway is that each profile expresses a distinct scheduling intent. The next two sections detail how the scoring strategies, MostAllocated and RequestedToCapacityRatio achieve these outcomes.** @@ -191,31 +191,35 @@ spec: 1. Which bin-packing strategy does AKS recommend to increase node utilization? -AKS recommends using the scoring strategy `RequestedToCapacityRatio` because it provides a more granular scoring approach allowing users to define an ideal utilization curve for their respective nodes. For example, this bin packing strategy allows users to configure a target utilization of 85%. + AKS recommends using the scoring strategy `RequestedToCapacityRatio` because it provides a more granular scoring approach allowing users to define an ideal utilization curve for their respective nodes. For example, this bin packing strategy allows users to configure a target utilization of 85%. 2. How do Configurable Scheduler Profiles interact with autoscalers such as Node Auto Provisioning (NAP), Cluster Autoscaler (CA), and Vertical Pod Autoscaler (VPA)? - These components are complementary to each other. Configurable Scheduler Profiles influence how pods are placed on nodes, while autoscalers make scaling decisions based on resource utilization and pending pods. + These components are complementary to each other. Configurable Scheduler Profiles influence how pods are placed on nodes, while autoscalers make scaling decisions based on resource utilization and pending pods. - - **Node Auto Provisioning (NAP)** is triggered when pods are unschedulable. If a suitable node already exists, that pod will be scheduled with the defined Configurable Scheduler Profile. If no suitable node exists, NAP provisions new capacity and schedules the pod. - - **Cluster Autoscaler (CA)** manages node scale-up and scale-down. On scale-up, CA is triggered when there aren't any suitable nodes available for the pending pod. Using Configurable Scheduler Profiles ensures nodes are only scaled when provisioned resources are no longer suitable. On scale-down, CA is triggered when nodes fall below utilization thresholds, the default is 50%. As active nodes are packed more efficiently, underutilized nodes become easier candidates for removal. - - **VPA** optimizes resource utilization patterns in pods. As pods are recreated with updated CPU and memory requests, they are scheduled using the configured scheduler profile, allowing placement decisions to reflect the new resource requirements. + - **Node Auto Provisioning (NAP)** is triggered when pods are unschedulable. If a suitable node already exists, that pod will be scheduled with the defined Configurable Scheduler Profile. If no suitable node exists, NAP provisions new capacity and schedules the pod. + - **Cluster Autoscaler (CA)** manages node scale-up and scale-down. On scale-up, CA is triggered when there aren't any suitable nodes available for the pending pod. Using Configurable Scheduler Profiles ensures nodes are only scaled when provisioned resources are no longer suitable. On scale-down, CA is triggered when nodes fall below utilization thresholds, the default is 50%. As active nodes are packed more efficiently, underutilized nodes become easier candidates for removal. + - **Vertical Pod Autoscaler (VPA)** optimizes resource utilization patterns in pods. As pods are recreated with updated CPU and memory requests, they are scheduled using the configured scheduler profile, allowing placement decisions to reflect the new resource requirements. 3. What if a resource, like `memory`, is omitted in the `scoringStrategy`? - If a resource is omitted in the `scoringStrategy`, then that resource will not be considered in the filter or scoring cycles of the defined Configurable Scheduler Profile. If that resource should be considered, but with a reduced influence on the final score, it can be included with reduced weight. + If a resource is omitted in the `scoringStrategy`, then that resource will not be considered in the filter or scoring cycles of the defined Configurable Scheduler Profile. If that resource should be considered, but with a reduced influence on the final score, it can be included with reduced weight. 4. Can multiple Configurable Scheduler Profiles be used for different workloads on the same cluster? - Yes, multiple scheduling profiles can coexist in a single cluster. This allows different placement strategies (for example, cost‑optimized vs. latency‑sensitive workloads) to run side‑by‑side. Visit the documentation for a [multiple scheduler profiles example][configure-multi-config] - - Multiple profiles can be defined centrally in a single scheduler configuration. - - Individual workloads select a profile via `schedulerName` in the pod spec. + Yes, multiple scheduling profiles can coexist in a single cluster. This allows different placement strategies (for example, cost‑optimized vs. latency‑sensitive workloads) to run side‑by‑side. Visit the documentation for a [multiple scheduler profiles example][configure-multi-config]. -5. How do I monitor whether my scheduler profile is improving utilization? Monitor these signals to confirm that the scheduler is behaving correctly. Over time, you should see higher average node utilization, reduced variance between nodes, and fewer lightly utilized nodes. + - Multiple profiles can be defined centrally in a single scheduler configuration. + - Individual workloads select a profile via `schedulerName` in the pod spec. + +5. How do I monitor whether my scheduler profile is improving utilization? + + Monitor these signals to confirm that the scheduler is behaving correctly. Over time, you should see higher average node utilization, reduced variance between nodes, and fewer lightly utilized nodes. + + - Track node‑level utilization metrics, including CPU and memory utilization per node and distribution of pods across nodes, using Azure Monitor Container Insights, the AKS node viewer [tool][aks-node-viewer], or `kubectl top nodes` for quick validation. + - Review autoscaler outcomes, looking for fewer scale‑ups during normal load and more decisive scale‑downs after demand drops. + - Measure cost metrics, such as reduced idle costs when you use the [Cost Analysis add-on][aks-cost-analysis-add-on]. - - Track node‑level utilization metrics, including CPU and memory utilization per node and distribution of pods across nodes, using Azure Monitor Container Insights, the AKS node viewer [tool][aks-node-viewer], or `kubectl top nodes` for quick validation. - - Review autoscaler outcomes, looking for fewer scale‑ups during normal load and more decisive scale‑downs after demand drops. - - Measure cost metrics, such as reduced idle costs when you use the [Cost Analysis add-on][aks-cost-analysis-add-on]. ## Next steps: Optimize Azure resources and test Configurable Scheduler Profiles on AKS With Configurable Scheduler Profiles, teams gain fine-grained control over pod placement strategies like bin-packing, topology distribution, and resource-based scoring that directly addresses challenges related to application resilience and resource utilization for their AKS clusters. By leveraging these scheduling plugins, AKS users can ensure their workloads make full use of available GPU capacity, reduce idle costs, and avoid costly overprovisioning. This not only improves ROI but also accelerates development by allowing more jobs to run concurrently and reliably. From c6284918688c662b1cf36bbff1336e76624b5f06 Mon Sep 17 00:00:00 2001 From: colinmixonn <109253437+colinmixonn@users.noreply.github.com> Date: Wed, 1 Apr 2026 13:45:00 -0700 Subject: [PATCH 139/164] Revise scheduler profiles section for clarity and detail Updated scenarios for CPU and GPU utilization with improved explanations and visual comparisons. Enhanced clarity on scheduling strategies and their benefits. --- .../index.md | 6 ++---- 1 file changed, 2 insertions(+), 4 deletions(-) diff --git a/website/blog/2026-03-31-aks-config-scheduler-profiles-preview/index.md b/website/blog/2026-03-31-aks-config-scheduler-profiles-preview/index.md index e69260b1a..0d796ab5d 100644 --- a/website/blog/2026-03-31-aks-config-scheduler-profiles-preview/index.md +++ b/website/blog/2026-03-31-aks-config-scheduler-profiles-preview/index.md @@ -61,13 +61,11 @@ In this simple scale-out scenario, manually increasing identical CPU-bound repli Configurable Scheduler Profiles configured for bin-packing show a visible consolidation pattern that differs from the default scheduler, and improves capacity for new pods. This shift occurs without changing the workload, node size, or autoscaling behavior - only the scheduler’s scoring logic. -#### Scenario: Increase CPU utilization - ![Table showing increased node utilization with the node bin packing scheduler profiles versus the pod distribution using the default scheduler](./default-config-scheduler-comparison.png) While this experiment uses intentionally simple, CPU-bound containers to isolate scheduling behavior, the placement patterns observed here can be applied to GPU-bound workloads where consolidation and utilization efficiency matter. In this constrained GPU scenario the bin‑packing scheduler plays a critical role in maximizing usable capacity of 4 GPU's spread across 2 nodes. Consolidating single‑GPU workloads onto the same node instead of spreading them evenly, the scheduler avoids stranding idle GPUs and enables all four GPUs to be actively used before new nodes are required. -#### Scenario: Increase GPU utilization +![Table showing increased node utilization with the node bin packing scheduler profiles versus the pod distribution using the default scheduler for gpu's across multiple scheduling iterations](./default-config-scheduler-comparison-gpu.png) This change in distribution shape enables downstream efficiencies: improved control for platform engineers, efficient resource usage, and cost optimization that are difficult to achieve when pods are evenly spread. **The key takeaway is that each profile expresses a distinct scheduling intent. The next two sections detail how the scoring strategies, MostAllocated and RequestedToCapacityRatio achieve these outcomes.** @@ -75,7 +73,7 @@ This change in distribution shape enables downstream efficiencies: improved cont |---|---|---|---| | Default scheduler | NodeResourcesFit: LeastAllocated | Balance and hotspot reduction | No tuning | | Configurable Scheduler Profile | NodeResourcesFit: MostAllocated | Maximize consolidation / bin‑packing | Maximum node utilization, highest cost reduction potential | -| Configurable Scheduler Profile | NodeResourcesFit: RequestedToCapacityRatio | Targeted utilization with headroom | ✅ **Recommended strategy:** Increased utilization with stronger control over consolidation and burst headroom than `MostAllocated` | +| Configurable Scheduler Profile | NodeResourcesFit: RequestedToCapacityRatio | Targeted utilization with headroom | ✅ **Recommended strategy** Increased utilization with stronger control over consolidation and burst headroom than `MostAllocated` | ### Increase AKS CPU utilization From fa1fd6e05543b11406e648482bccca5314bd63eb Mon Sep 17 00:00:00 2001 From: colinmixonn <109253437+colinmixonn@users.noreply.github.com> Date: Wed, 1 Apr 2026 13:45:38 -0700 Subject: [PATCH 140/164] Add files via upload --- .../default-config-scheduler-comparison-gpu.png | Bin 0 -> 46010 bytes 1 file changed, 0 insertions(+), 0 deletions(-) create mode 100644 website/blog/2026-03-31-aks-config-scheduler-profiles-preview/default-config-scheduler-comparison-gpu.png diff --git a/website/blog/2026-03-31-aks-config-scheduler-profiles-preview/default-config-scheduler-comparison-gpu.png b/website/blog/2026-03-31-aks-config-scheduler-profiles-preview/default-config-scheduler-comparison-gpu.png new file mode 100644 index 0000000000000000000000000000000000000000..964c141f006680dcbaf7d2ad6fc737ec74e421dd GIT binary patch literal 46010 zcmeFZWmHsezc36IA|TxTe4Fl3GIpokebi**z07LT( z>V5wAInP<^eLp7BdS4Iz!ztJ6Jb(~R9o_5@QP(k(u 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of AKS GPU scheduling strategies Clarified the explanation of the 'MostAllocated' scheduling strategy and its interaction with 'NodeResourcesBalancedAllocation'. Revised the text for better readability and accuracy regarding resource utilization and scheduling goals. --- .../2026-03-31-aks-config-scheduler-profiles-preview/index.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/website/blog/2026-03-31-aks-config-scheduler-profiles-preview/index.md b/website/blog/2026-03-31-aks-config-scheduler-profiles-preview/index.md index 0d796ab5d..053d3620c 100644 --- a/website/blog/2026-03-31-aks-config-scheduler-profiles-preview/index.md +++ b/website/blog/2026-03-31-aks-config-scheduler-profiles-preview/index.md @@ -134,7 +134,7 @@ spec: ### Increase AKS GPU utilization -`MostAllocated` scores nodes based on their current resource utilization, favoring nodes that are already more heavily utilized. Unlike `RequestedToCapacityRatio`, it does not consider node capacity in node scoring, making it more suitable for an aggressive cost-optimization scheduling strategy. When paired with MostAllocated, `NodeResourcesBalancedAllocation` complements the behavior because it encourages pod placement on nodes with user-defined proportional utilization, helping reduce bottlenecks caused by asymmetric resource pressure. Lastly, it is critical to note that `PodTopologySpread` is disabled in this profile because bin-packing and zone-spreading are opposing goals and the scheduling logic may prioritize pod spreading. +`MostAllocated` scores nodes based on its current resource utilization, favoring nodes that are already heavily used. `RequestedToCapacityRatio`, on the other hand, scores nodes based on both the resource requests and the remaining node capacity, making `MostAllocated` more suitable for an aggressive cost-optimization scheduling strategy since it inetionally ignores capacity. When paired with MostAllocated, `NodeResourcesBalancedAllocation` complements the behavior because it encourages pod placement on nodes with user-defined proportional utilization, helping reduce bottlenecks caused by asymmetric resource pressure. Lastly, it is critical to note that `PodTopologySpread` is disabled in this profile because bin-packing and zone-spreading are opposing goals. Enabling both can result in spreading over consolidation, weakening the intended packing behavior. When combined, these plugins favor GPU‑bound nodes with balanced CPU and memory usage over nodes with large amounts of unused memory or fragmented resources. This results in more efficient GPU placement and fewer partially utilized nodes. [Configure node bin-packing][configure-most-allocated] using the MostAllocated strategy to improve utilization and reduce infrastructure costs. From 19b1192bfa50bda968e202b3161b811befc64356 Mon Sep 17 00:00:00 2001 From: colinmixonn <109253437+colinmixonn@users.noreply.github.com> Date: Wed, 1 Apr 2026 14:41:34 -0700 Subject: [PATCH 144/164] Update introduction to emphasize resource utilization Revised text to emphasize the impact of underutilized resources on infrastructure costs and highlight the potential for improved resource utilization. --- .../2026-03-31-aks-config-scheduler-profiles-preview/index.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/website/blog/2026-03-31-aks-config-scheduler-profiles-preview/index.md b/website/blog/2026-03-31-aks-config-scheduler-profiles-preview/index.md index 053d3620c..caf37bd8c 100644 --- a/website/blog/2026-03-31-aks-config-scheduler-profiles-preview/index.md +++ b/website/blog/2026-03-31-aks-config-scheduler-profiles-preview/index.md @@ -6,7 +6,7 @@ authors: [colin-mixon] tags: [ai, performance, scheduler, best-practices, cost] --- -In 2025, Datadog found most Kubernetes containers use less than [25% of their requested CPU][datadog-state-of-containers], and in 2023, Weights and Biases found that nearly a third of GPU users [average less than 15% utilization][wb-gpu-utilization]. This data signals that underutilized resources materially contribute to increased infrastructure cost. While there are many factors that impact node utilization, as a core component of the Kubernetes control plane, the kube-scheduler plays a critical role in node utilization. +In 2025, Datadog found most Kubernetes containers use less than [25% of their requested CPU][datadog-state-of-containers], and in 2023, Weights and Biases found that nearly a third of GPU users [average less than 15% utilization][wb-gpu-utilization]. Underutilized resources materially contribute to increased infrastructure cost and this data signals that most users can greatly improve resource utilization. While there are many factors that impact node utilization, as a core component of the Kubernetes control plane, the kube-scheduler plays a critical role in node utilization. [Configurable Scheduler Profiles][concepts-scheduler-configuration] on AKS lets customers configure their own scheduling logic: enable specific plugins, adjust plugin priorities, and tune parameter weights. **The result: higher node density, better GPU utilization, and lower infrastructure costs.** From 38b2b51838258eea473f934e1053aa935d4051f5 Mon Sep 17 00:00:00 2001 From: colinmixonn <109253437+colinmixonn@users.noreply.github.com> Date: Wed, 1 Apr 2026 14:50:41 -0700 Subject: [PATCH 145/164] Apply suggestions from code review Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> --- .../2026-03-31-aks-config-scheduler-profiles-preview/index.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/website/blog/2026-03-31-aks-config-scheduler-profiles-preview/index.md b/website/blog/2026-03-31-aks-config-scheduler-profiles-preview/index.md index caf37bd8c..353c71b85 100644 --- a/website/blog/2026-03-31-aks-config-scheduler-profiles-preview/index.md +++ b/website/blog/2026-03-31-aks-config-scheduler-profiles-preview/index.md @@ -220,7 +220,7 @@ spec: ## Next steps: Optimize Azure resources and test Configurable Scheduler Profiles on AKS -With Configurable Scheduler Profiles, teams gain fine-grained control over pod placement strategies like bin-packing, topology distribution, and resource-based scoring that directly addresses challenges related to application resilience and resource utilization for their AKS clusters. By leveraging these scheduling plugins, AKS users can ensure their workloads make full use of available GPU capacity, reduce idle costs, and avoid costly overprovisioning. This not only improves ROI but also accelerates development by allowing more jobs to run concurrently and reliably. +With Configurable Scheduler Profiles, teams gain fine-grained control over pod placement strategies like bin-packing, topology distribution, and resource-based scoring that directly addresses challenges related to application resilience and resource utilization for their AKS clusters. By leveraging these scheduling plugins, AKS users can ensure their workloads make full use of available GPU capacity, reduce idle costs, and avoid costly overprovisioning. This not only improves return on investment (ROI) but also accelerates development by allowing more jobs to run concurrently and reliably. - For additional guidance and best practices, see [kube-scheduler best practices][best-practices-advanced-scheduler] - Increase node utilization using [Configurable Scheduler Profiles][node-bin-packing-configurations] From 64c03d25424d97672478599531e9a4d050d2e125 Mon Sep 17 00:00:00 2001 From: colinmixonn <109253437+colinmixonn@users.noreply.github.com> Date: Wed, 1 Apr 2026 15:00:55 -0700 Subject: [PATCH 146/164] Enhance clarity and detail in scheduler profiles content Updated text for clarity and improved explanations regarding the bin-packing scheduler's impact on GPU workloads and autoscaler interactions. Added a note about an upcoming blog on scheduling constraints. --- .../index.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/website/blog/2026-03-31-aks-config-scheduler-profiles-preview/index.md b/website/blog/2026-03-31-aks-config-scheduler-profiles-preview/index.md index 353c71b85..31b6a299a 100644 --- a/website/blog/2026-03-31-aks-config-scheduler-profiles-preview/index.md +++ b/website/blog/2026-03-31-aks-config-scheduler-profiles-preview/index.md @@ -63,7 +63,7 @@ Configurable Scheduler Profiles configured for bin-packing show a visible consol ![Table showing increased node utilization with the node bin packing scheduler profiles versus the pod distribution using the default scheduler](./default-config-scheduler-comparison.png) -While this experiment uses intentionally simple, CPU-bound containers to isolate scheduling behavior, the placement patterns observed here can be applied to GPU-bound workloads where consolidation and utilization efficiency matter. In this constrained GPU scenario the bin‑packing scheduler plays a critical role in maximizing usable capacity of 4 GPU's spread across 2 nodes. Consolidating single‑GPU workloads onto the same node instead of spreading them evenly, the scheduler avoids stranding idle GPUs and enables all four GPUs to be actively used before new nodes are required. +While this experiment uses intentionally simple, CPU-bound containers to isolate scheduling behavior, the placement patterns observed here can be applied to GPU-bound workloads where capacity constraints elevate the need for utilization improvements. In this constrained GPU scenario the bin‑packing scheduler plays a critical role in maximizing usable capacity of 4 GPU's spread across 2 nodes. By consolidating single‑GPU workloads onto the same node, the scheduler avoids idle GPUs and enables all four GPUs to be actively used before new nodes are required. ![Table showing increased node utilization with the node bin packing scheduler profiles versus the pod distribution using the default scheduler for gpu's across multiple scheduling iterations](./default-config-scheduler-comparison-gpu.png) @@ -193,7 +193,7 @@ spec: 2. How do Configurable Scheduler Profiles interact with autoscalers such as Node Auto Provisioning (NAP), Cluster Autoscaler (CA), and Vertical Pod Autoscaler (VPA)? - These components are complementary to each other. Configurable Scheduler Profiles influence how pods are placed on nodes, while autoscalers make scaling decisions based on resource utilization and pending pods. + These components are complementary to each other. Configurable Scheduler Profiles influence how pods are placed on nodes, while autoscalers make scaling decisions based on resource utilization and pending pods. Look out for a incoming blog that details how scheduling constraints effect Node Auto Provisoing. - **Node Auto Provisioning (NAP)** is triggered when pods are unschedulable. If a suitable node already exists, that pod will be scheduled with the defined Configurable Scheduler Profile. If no suitable node exists, NAP provisions new capacity and schedules the pod. - **Cluster Autoscaler (CA)** manages node scale-up and scale-down. On scale-up, CA is triggered when there aren't any suitable nodes available for the pending pod. Using Configurable Scheduler Profiles ensures nodes are only scaled when provisioned resources are no longer suitable. On scale-down, CA is triggered when nodes fall below utilization thresholds, the default is 50%. As active nodes are packed more efficiently, underutilized nodes become easier candidates for removal. @@ -214,7 +214,7 @@ spec: Monitor these signals to confirm that the scheduler is behaving correctly. Over time, you should see higher average node utilization, reduced variance between nodes, and fewer lightly utilized nodes. - - Track node‑level utilization metrics, including CPU and memory utilization per node and distribution of pods across nodes, using Azure Monitor Container Insights, the AKS node viewer [tool][aks-node-viewer], or `kubectl top nodes` for quick validation. + - Track node‑level utilization metrics, including CPU and memory utilization per node and distribution of pods across nodes, using Azure Monitor Container Insights, the AKS [node viewer tool][aks-node-viewer], or `kubectl top nodes` for quick validation. - Review autoscaler outcomes, looking for fewer scale‑ups during normal load and more decisive scale‑downs after demand drops. - Measure cost metrics, such as reduced idle costs when you use the [Cost Analysis add-on][aks-cost-analysis-add-on]. From 02fc0c56db2b728f500f57f0d3d0ebd9facebad0 Mon Sep 17 00:00:00 2001 From: colinmixonn <109253437+colinmixonn@users.noreply.github.com> Date: Wed, 1 Apr 2026 17:12:33 -0700 Subject: [PATCH 147/164] updated publishing date --- .../index.md | 2 +- .../config-scheduler-profiles.png | Bin ...efault-config-scheduler-comparison-gpu.png | Bin .../default-config-scheduler-comparison.png | Bin .../hero-image.png | Bin .../index.md | 242 ++++++++++++++++++ ...be-scheduler-scheduling-phases-diagram.png | Bin 7 files changed, 243 insertions(+), 1 deletion(-) rename website/blog/{2026-03-31-aks-config-scheduler-profiles-preview => 2026-04-06-aks-config-scheduler-profiles-preview}/config-scheduler-profiles.png (100%) rename website/blog/{2026-03-31-aks-config-scheduler-profiles-preview => 2026-04-06-aks-config-scheduler-profiles-preview}/default-config-scheduler-comparison-gpu.png (100%) rename website/blog/{2026-03-31-aks-config-scheduler-profiles-preview => 2026-04-06-aks-config-scheduler-profiles-preview}/default-config-scheduler-comparison.png (100%) rename website/blog/{2026-03-31-aks-config-scheduler-profiles-preview => 2026-04-06-aks-config-scheduler-profiles-preview}/hero-image.png (100%) create mode 100644 website/blog/2026-04-06-aks-config-scheduler-profiles-preview/index.md rename website/blog/{2026-03-31-aks-config-scheduler-profiles-preview => 2026-04-06-aks-config-scheduler-profiles-preview}/kube-scheduler-scheduling-phases-diagram.png (100%) diff --git a/website/blog/2026-03-31-aks-config-scheduler-profiles-preview/index.md b/website/blog/2026-03-31-aks-config-scheduler-profiles-preview/index.md index 31b6a299a..bffd858fa 100644 --- a/website/blog/2026-03-31-aks-config-scheduler-profiles-preview/index.md +++ b/website/blog/2026-03-31-aks-config-scheduler-profiles-preview/index.md @@ -1,7 +1,7 @@ --- title: "AKS Configurable Scheduler Profiles (preview)" description: "Improve GPU and CPU utilization, align pod placement to critical workloads, and reduce node costs at scale with Configurable Scheduler Profiles on AKS." -date: 2026-03-31 +date: 2026-04-06 authors: [colin-mixon] tags: [ai, performance, scheduler, best-practices, cost] --- diff --git a/website/blog/2026-03-31-aks-config-scheduler-profiles-preview/config-scheduler-profiles.png b/website/blog/2026-04-06-aks-config-scheduler-profiles-preview/config-scheduler-profiles.png similarity index 100% rename from website/blog/2026-03-31-aks-config-scheduler-profiles-preview/config-scheduler-profiles.png rename to website/blog/2026-04-06-aks-config-scheduler-profiles-preview/config-scheduler-profiles.png diff --git a/website/blog/2026-03-31-aks-config-scheduler-profiles-preview/default-config-scheduler-comparison-gpu.png b/website/blog/2026-04-06-aks-config-scheduler-profiles-preview/default-config-scheduler-comparison-gpu.png similarity index 100% rename from website/blog/2026-03-31-aks-config-scheduler-profiles-preview/default-config-scheduler-comparison-gpu.png rename to website/blog/2026-04-06-aks-config-scheduler-profiles-preview/default-config-scheduler-comparison-gpu.png diff --git a/website/blog/2026-03-31-aks-config-scheduler-profiles-preview/default-config-scheduler-comparison.png b/website/blog/2026-04-06-aks-config-scheduler-profiles-preview/default-config-scheduler-comparison.png similarity index 100% rename from website/blog/2026-03-31-aks-config-scheduler-profiles-preview/default-config-scheduler-comparison.png rename to website/blog/2026-04-06-aks-config-scheduler-profiles-preview/default-config-scheduler-comparison.png diff --git a/website/blog/2026-03-31-aks-config-scheduler-profiles-preview/hero-image.png b/website/blog/2026-04-06-aks-config-scheduler-profiles-preview/hero-image.png similarity index 100% rename from website/blog/2026-03-31-aks-config-scheduler-profiles-preview/hero-image.png rename to website/blog/2026-04-06-aks-config-scheduler-profiles-preview/hero-image.png diff --git a/website/blog/2026-04-06-aks-config-scheduler-profiles-preview/index.md b/website/blog/2026-04-06-aks-config-scheduler-profiles-preview/index.md new file mode 100644 index 000000000..31b6a299a --- /dev/null +++ b/website/blog/2026-04-06-aks-config-scheduler-profiles-preview/index.md @@ -0,0 +1,242 @@ +--- +title: "AKS Configurable Scheduler Profiles (preview)" +description: "Improve GPU and CPU utilization, align pod placement to critical workloads, and reduce node costs at scale with Configurable Scheduler Profiles on AKS." +date: 2026-03-31 +authors: [colin-mixon] +tags: [ai, performance, scheduler, best-practices, cost] +--- + +In 2025, Datadog found most Kubernetes containers use less than [25% of their requested CPU][datadog-state-of-containers], and in 2023, Weights and Biases found that nearly a third of GPU users [average less than 15% utilization][wb-gpu-utilization]. Underutilized resources materially contribute to increased infrastructure cost and this data signals that most users can greatly improve resource utilization. While there are many factors that impact node utilization, as a core component of the Kubernetes control plane, the kube-scheduler plays a critical role in node utilization. + +[Configurable Scheduler Profiles][concepts-scheduler-configuration] on AKS lets customers configure their own scheduling logic: enable specific plugins, adjust plugin priorities, and tune parameter weights. **The result: higher node density, better GPU utilization, and lower infrastructure costs.** + +This blog explains how the default Kubernetes scheduler places pods, where the defaults fall short, and how to increase node utilization using Configurable Scheduler Profiles on AKS. + +1. [How does kube-scheduler work?](#how-does-the-default-kubernetes-scheduler-place-pods) +2. [Use Configurable Scheduler Profiles to increase node utilization and operator control](#increase-node-utilization-and-operator-control) +3. [Increase AKS cluster CPU utilization up to 85% with Configurable Scheduler](#increase-aks-cpu-utilization) +4. [Increase AKS cluster GPU or CPU utilization while balancing memory with Configurable Scheduler](#increase-aks-gpu-utilization) +5. [FAQ: How do Configurable Scheduler Profiles interact with autoscalers?](#faq) + + + +![Diagram showing increased node utilization with the node bin packing scheduler profile](./hero-image.png) + +## How does the default Kubernetes scheduler place pods? + +The Kubernetes scheduler operates in two cycles: a synchronous scheduling cycle and an asynchronous binding cycle. The scheduling cycle has two sub-phases, filtering and scoring, and only manages one pod at a time. + +1. **Filtering** phase removes unsuitable nodes based on hard and soft constraints. +2. **Scoring** phase calculates a score for the remaining nodes; ultimately, the most suitable node has the highest score. + +Once a node is selected, the binding cycle can process multiple pods in parallel. During this phase, the scheduler attempts to bind the pod to the chosen node. If binding a pod to a node fails, the scheduler tries the node with the next highest score. When filtering and scoring nodes, the default scheduler considers several hard and soft constraints with predefined weights, including (but not limited to): + +1. Resource requirements (CPU, memory) +2. Node affinity/anti-affinity +3. Pod affinity/anti-affinity +4. Taints and tolerations +5. TopologySpreadConstraints + +![Diagram of the kube-scheduler workflow showing pods entering the scheduling cycle and binding cycle to assign a pod on a node](./kube-scheduler-scheduling-phases-diagram.png) + +For a deep dive on the Kubernetes Scheduler visit technical blog from SIG Scheduling contributor and AKS Upstream Engineer, Heba Elayoty, [Deep Dive into the Kubernetes Scheduler Framework][deep-dive-scheduler-framework]. + +### Limitations of the default Kubernetes scheduler + +The default scheduler is primarily designed for general-purpose workloads. It prioritizes nodes with the most available resources using the _LeastAllocated_ scoring strategy. This strategy spreads pods across nodes, even when they could safely be packed more densely. While this works well for many services, the default scheduling criteria, and their fixed priority order, are not suitable for workloads that demand optimizing GPU and CPU utilization. In these scenarios, spreading pods across nodes can lead to fragmented resources, underutilized GPUs, and increased infrastructure cost. + +Today, the default scheduler on AKS lacks the flexibility for users to change which criteria should be prioritized, or ignored, in the scheduling cycle on a per cluster basis. This rigidity often forces users to either accept suboptimal placement or manage a separate custom scheduler, both of which increase operational complexity. Starting with Kubernetes v1.33, AKS introduces [Configurable Scheduler Profiles][concepts-scheduler-configuration] - an AKS-managed CRD - that exposes the upstream scheduling framework without maintaining a separate scheduler. Now, users can adjust the `NodeResourcesFit` plugin from the default configuration to favor nodes with higher utilization to achieve more efficient bin‑packing and reduce infrastructure cost. + +## Configurable Scheduler Profiles on AKS + +[Configurable Scheduler Profiles on AKS][concepts-scheduler-configuration] allow customers to benefit from the extensibility of the [scheduling framework][scheduling-framework-interfaces] while reducing the operational overhead of adopting a second scheduler or defining a custom scheduler. Configurable Scheduler Profiles use a Custom Resource Definition (CRD) that lets users define custom scheduler profiles with their own scheduling logic. A dedicated controller continuously reconciles these user-defined configurations with the underlying kube-scheduler deployment, validating changes and applying them transparently. If a new configuration causes the scheduler to become unhealthy, the controller automatically reverts to the last known good state to ensure cluster stability. + +![Architecture diagram showing how Configurable Scheduler Profiles use a CRD and controller to reconcile user-defined profiles with the kube-scheduler deployment](./config-scheduler-profiles.png) + +A profile is a set of one or more in-tree scheduling plugins and configurations that dictate how to schedule a pod. AKS supports 18 in-tree Kubernetes [scheduling plugins][supported-in-tree-scheduling-plugins]. + +## Increase node utilization and operator control + +In this simple scale-out scenario, manually increasing identical CPU-bound replicas from 8 to 30, the default scheduler distributes pods evenly across nodes. + +Configurable Scheduler Profiles configured for bin-packing show a visible consolidation pattern that differs from the default scheduler, and improves capacity for new pods. This shift occurs without changing the workload, node size, or autoscaling behavior - only the scheduler’s scoring logic. + +![Table showing increased node utilization with the node bin packing scheduler profiles versus the pod distribution using the default scheduler](./default-config-scheduler-comparison.png) + +While this experiment uses intentionally simple, CPU-bound containers to isolate scheduling behavior, the placement patterns observed here can be applied to GPU-bound workloads where capacity constraints elevate the need for utilization improvements. In this constrained GPU scenario the bin‑packing scheduler plays a critical role in maximizing usable capacity of 4 GPU's spread across 2 nodes. By consolidating single‑GPU workloads onto the same node, the scheduler avoids idle GPUs and enables all four GPUs to be actively used before new nodes are required. + +![Table showing increased node utilization with the node bin packing scheduler profiles versus the pod distribution using the default scheduler for gpu's across multiple scheduling iterations](./default-config-scheduler-comparison-gpu.png) + +This change in distribution shape enables downstream efficiencies: improved control for platform engineers, efficient resource usage, and cost optimization that are difficult to achieve when pods are evenly spread. **The key takeaway is that each profile expresses a distinct scheduling intent. The next two sections detail how the scoring strategies, MostAllocated and RequestedToCapacityRatio achieve these outcomes.** + +| Scheduler | Scoring Strategy | Scheduling intent | Operator Benefits | +|---|---|---|---| +| Default scheduler | NodeResourcesFit: LeastAllocated | Balance and hotspot reduction | No tuning | +| Configurable Scheduler Profile | NodeResourcesFit: MostAllocated | Maximize consolidation / bin‑packing | Maximum node utilization, highest cost reduction potential | +| Configurable Scheduler Profile | NodeResourcesFit: RequestedToCapacityRatio | Targeted utilization with headroom | ✅ **Recommended strategy** Increased utilization with stronger control over consolidation and burst headroom than `MostAllocated` | + +### Increase AKS CPU utilization + +`RequestedToCapacityRatio` scores nodes based on the ratio of requested resources to total node capacity after the pod is _hypothetically_ placed. This strategy enables more fine-grained bin‑packing by allowing operators to define an ideal utilization curve for their nodes rather than simply preferring the most or least utilized nodes. Lastly, it is critical to note that `PodTopologySpread` is disabled in this profile because bin-packing and zone-spreading are opposing goals and the scheduling logic may prioritize pod spreading. If you need both high utilization _and_ zone resilience, define a new profile to achieve both goals. + +By shaping the scoring curve to target a range of 50-85% CPU utilization, operators can increase pod density on provisioned nodes while preserving headroom for bursts, background processes, and system components in CPU-based workloads. [Configure node bin-packing][configure-requested-to-capacity] using the `RequestedToCapacityRatio` strategy to improve utilization and reduce infrastructure costs. + +**This bin packing profile is configured to favor nodes within a utilization band of 50-85%, avoiding empty nodes, and severely deprioritizing nearly full nodes at 90% utilization or more, to limit oversaturated nodes. Given this level of configuration detail, `RequestedToCapacityRatio` is the recommended scoring strategy for node bin‑packing on AKS for production clusters.** + +:::note +Scoring strategy can also be used for GPU by changing the target resource. Adjust resources, resource weights, utilization thresholds, and plugin parameters to match your VM SKUs, workload patterns, and cluster topology. +::: + +```yaml +apiVersion: aks.azure.com/v1alpha1 +kind: SchedulerConfiguration +metadata: + name: upstream +spec: + rawConfig: | + apiVersion: kubescheduler.config.k8s.io/v1 + kind: KubeSchedulerConfiguration + profiles: + - schedulerName: cpu-binpacking-scheduler + plugins: + multiPoint: + enabled: + - name: NodeResourcesFit + disabled: + - name: PodTopologySpread + pluginConfig: + - name: NodeResourcesFit + args: + apiVersion: kubescheduler.config.k8s.io/v1 + kind: NodeResourcesFitArgs + scoringStrategy: + type: RequestedToCapacityRatio + resources: + - name: cpu + weight: 8 + - name: memory + weight: 1 + requestedToCapacityRatio: + shape: + - utilization: 0 + score: 0 + - utilization: 30 + score: 8 + - utilization: 50 + score: 10 + - utilization: 85 + score: 10 + - utilization: 90 + score: 3 + - utilization: 100 + score: 0 +``` + +### Increase AKS GPU utilization + +`MostAllocated` scores nodes based on its current resource utilization, favoring nodes that are already heavily used. `RequestedToCapacityRatio`, on the other hand, scores nodes based on both the resource requests and the remaining node capacity, making `MostAllocated` more suitable for an aggressive cost-optimization scheduling strategy since it inetionally ignores capacity. When paired with MostAllocated, `NodeResourcesBalancedAllocation` complements the behavior because it encourages pod placement on nodes with user-defined proportional utilization, helping reduce bottlenecks caused by asymmetric resource pressure. Lastly, it is critical to note that `PodTopologySpread` is disabled in this profile because bin-packing and zone-spreading are opposing goals. Enabling both can result in spreading over consolidation, weakening the intended packing behavior. + +When combined, these plugins favor GPU‑bound nodes with balanced CPU and memory usage over nodes with large amounts of unused memory or fragmented resources. This results in more efficient GPU placement and fewer partially utilized nodes. [Configure node bin-packing][configure-most-allocated] using the MostAllocated strategy to improve utilization and reduce infrastructure costs. + +**This scheduler configuration maximizes GPU utilization by consolidating smaller jobs onto fewer nodes, reducing idle accelerator capacity while maintaining reasonable CPU and memory balance.** + +:::note +Adjust resources, resource weights, utilization thresholds, and plugin parameters to match your VM SKUs, workload patterns, and cluster topology. +::: + +```yaml +apiVersion: aks.azure.com/v1alpha1 +kind: SchedulerConfiguration +metadata: + name: upstream +spec: + rawConfig: | + apiVersion: kubescheduler.config.k8s.io/v1 + kind: KubeSchedulerConfiguration + profiles: + - schedulerName: gpu-node-binpacking-scheduler + plugins: + multiPoint: + enabled: + - name: NodeResourcesFit + - name: NodeResourcesBalancedAllocation + disabled: + - name: PodTopologySpread + pluginConfig: + - name: NodeResourcesFit + args: + apiVersion: kubescheduler.config.k8s.io/v1 + kind: NodeResourcesFitArgs + scoringStrategy: + type: MostAllocated + resources: + - name: nvidia.com/gpu + weight: 8 + - name: cpu + weight: 1 + - name: NodeResourcesBalancedAllocation + args: + apiVersion: kubescheduler.config.k8s.io/v1 + kind: NodeResourcesBalancedAllocationArgs + resources: + - name: cpu + weight: 1 + - name: memory + weight: 1 +``` + +### FAQ + +1. Which bin-packing strategy does AKS recommend to increase node utilization? + + AKS recommends using the scoring strategy `RequestedToCapacityRatio` because it provides a more granular scoring approach allowing users to define an ideal utilization curve for their respective nodes. For example, this bin packing strategy allows users to configure a target utilization of 85%. + +2. How do Configurable Scheduler Profiles interact with autoscalers such as Node Auto Provisioning (NAP), Cluster Autoscaler (CA), and Vertical Pod Autoscaler (VPA)? + + These components are complementary to each other. Configurable Scheduler Profiles influence how pods are placed on nodes, while autoscalers make scaling decisions based on resource utilization and pending pods. Look out for a incoming blog that details how scheduling constraints effect Node Auto Provisoing. + + - **Node Auto Provisioning (NAP)** is triggered when pods are unschedulable. If a suitable node already exists, that pod will be scheduled with the defined Configurable Scheduler Profile. If no suitable node exists, NAP provisions new capacity and schedules the pod. + - **Cluster Autoscaler (CA)** manages node scale-up and scale-down. On scale-up, CA is triggered when there aren't any suitable nodes available for the pending pod. Using Configurable Scheduler Profiles ensures nodes are only scaled when provisioned resources are no longer suitable. On scale-down, CA is triggered when nodes fall below utilization thresholds, the default is 50%. As active nodes are packed more efficiently, underutilized nodes become easier candidates for removal. + - **Vertical Pod Autoscaler (VPA)** optimizes resource utilization patterns in pods. As pods are recreated with updated CPU and memory requests, they are scheduled using the configured scheduler profile, allowing placement decisions to reflect the new resource requirements. + +3. What if a resource, like `memory`, is omitted in the `scoringStrategy`? + + If a resource is omitted in the `scoringStrategy`, then that resource will not be considered in the filter or scoring cycles of the defined Configurable Scheduler Profile. If that resource should be considered, but with a reduced influence on the final score, it can be included with reduced weight. + +4. Can multiple Configurable Scheduler Profiles be used for different workloads on the same cluster? + + Yes, multiple scheduling profiles can coexist in a single cluster. This allows different placement strategies (for example, cost‑optimized vs. latency‑sensitive workloads) to run side‑by‑side. Visit the documentation for a [multiple scheduler profiles example][configure-multi-config]. + + - Multiple profiles can be defined centrally in a single scheduler configuration. + - Individual workloads select a profile via `schedulerName` in the pod spec. + +5. How do I monitor whether my scheduler profile is improving utilization? + + Monitor these signals to confirm that the scheduler is behaving correctly. Over time, you should see higher average node utilization, reduced variance between nodes, and fewer lightly utilized nodes. + + - Track node‑level utilization metrics, including CPU and memory utilization per node and distribution of pods across nodes, using Azure Monitor Container Insights, the AKS [node viewer tool][aks-node-viewer], or `kubectl top nodes` for quick validation. + - Review autoscaler outcomes, looking for fewer scale‑ups during normal load and more decisive scale‑downs after demand drops. + - Measure cost metrics, such as reduced idle costs when you use the [Cost Analysis add-on][aks-cost-analysis-add-on]. + +## Next steps: Optimize Azure resources and test Configurable Scheduler Profiles on AKS + +With Configurable Scheduler Profiles, teams gain fine-grained control over pod placement strategies like bin-packing, topology distribution, and resource-based scoring that directly addresses challenges related to application resilience and resource utilization for their AKS clusters. By leveraging these scheduling plugins, AKS users can ensure their workloads make full use of available GPU capacity, reduce idle costs, and avoid costly overprovisioning. This not only improves return on investment (ROI) but also accelerates development by allowing more jobs to run concurrently and reliably. + +- For additional guidance and best practices, see [kube-scheduler best practices][best-practices-advanced-scheduler] +- Increase node utilization using [Configurable Scheduler Profiles][node-bin-packing-configurations] +- If additional capabilities or ML frameworks are needed to schedule and queue batch workloads, you can [install and configure Kueue on AKS][kueue-overview] to ensure efficient, policy-driven scheduling in AKS clusters. + +[concepts-scheduler-configuration]: https://learn.microsoft.com/azure/aks/concepts-scheduler-configuration +[kueue-overview]: https://learn.microsoft.com/azure/aks/kueue-overview +[best-practices-advanced-scheduler]: https://learn.microsoft.com/azure/aks/operator-best-practices-advanced-scheduler +[scheduling-framework-interfaces]: https://kubernetes.io/docs/concepts/scheduling-eviction/scheduling-framework/#interfaces +[supported-in-tree-scheduling-plugins]: https://learn.microsoft.com/azure/aks/concepts-scheduler-configuration#supported-in-tree-scheduling-plugins +[node-bin-packing-configurations]: https://learn.microsoft.com/azure/aks/configure-node-binpack-scheduler?tabs=new-cluster +[datadog-state-of-containers]: https://www.datadoghq.com/state-of-containers-and-serverless/ +[configure-requested-to-capacity]: https://learn.microsoft.com/azure/aks/configure-node-binpack-scheduler?tabs=new-cluster#configure-node-bin-packing-with-requestedtocapacity-plugin +[configure-most-allocated]: https://learn.microsoft.com/azure/aks/configure-node-binpack-scheduler?tabs=new-cluster#configure-node-bin-packing-with-mostallocated-plugin +[configure-multi-config]: https://learn.microsoft.com/azure/aks/configure-aks-scheduler?tabs=new-cluster#configure-multiple-scheduler-profiles +[aks-cost-analysis-add-on]: https://learn.microsoft.com/azure/aks/cost-analysis +[wb-gpu-utilization]: https://wandb.ai/wandb_fc/articles/reports/Monitor-Improve-GPU-Usage-for-Model-Training--Vmlldzo1NDQzNjM3 +[deep-dive-scheduler-framework]: https://helayoty.org/blog/inside-kube-scheduler-the-plugin +[aks-node-viewer]: https://github.com/Azure/aks-node-viewer diff --git a/website/blog/2026-03-31-aks-config-scheduler-profiles-preview/kube-scheduler-scheduling-phases-diagram.png b/website/blog/2026-04-06-aks-config-scheduler-profiles-preview/kube-scheduler-scheduling-phases-diagram.png similarity index 100% rename from website/blog/2026-03-31-aks-config-scheduler-profiles-preview/kube-scheduler-scheduling-phases-diagram.png rename to website/blog/2026-04-06-aks-config-scheduler-profiles-preview/kube-scheduler-scheduling-phases-diagram.png From 51b7cbebe1afc82cf8eb38df1b1ee010b99b1f04 Mon Sep 17 00:00:00 2001 From: colinmixonn <109253437+colinmixonn@users.noreply.github.com> Date: Wed, 1 Apr 2026 17:14:45 -0700 Subject: [PATCH 148/164] Delete website/blog/2026-03-31-aks-config-scheduler-profiles-preview directory --- .../index.md | 242 ------------------ 1 file changed, 242 deletions(-) delete mode 100644 website/blog/2026-03-31-aks-config-scheduler-profiles-preview/index.md diff --git a/website/blog/2026-03-31-aks-config-scheduler-profiles-preview/index.md b/website/blog/2026-03-31-aks-config-scheduler-profiles-preview/index.md deleted file mode 100644 index bffd858fa..000000000 --- a/website/blog/2026-03-31-aks-config-scheduler-profiles-preview/index.md +++ /dev/null @@ -1,242 +0,0 @@ ---- -title: "AKS Configurable Scheduler Profiles (preview)" -description: "Improve GPU and CPU utilization, align pod placement to critical workloads, and reduce node costs at scale with Configurable Scheduler Profiles on AKS." -date: 2026-04-06 -authors: [colin-mixon] -tags: [ai, performance, scheduler, best-practices, cost] ---- - -In 2025, Datadog found most Kubernetes containers use less than [25% of their requested CPU][datadog-state-of-containers], and in 2023, Weights and Biases found that nearly a third of GPU users [average less than 15% utilization][wb-gpu-utilization]. Underutilized resources materially contribute to increased infrastructure cost and this data signals that most users can greatly improve resource utilization. While there are many factors that impact node utilization, as a core component of the Kubernetes control plane, the kube-scheduler plays a critical role in node utilization. - -[Configurable Scheduler Profiles][concepts-scheduler-configuration] on AKS lets customers configure their own scheduling logic: enable specific plugins, adjust plugin priorities, and tune parameter weights. **The result: higher node density, better GPU utilization, and lower infrastructure costs.** - -This blog explains how the default Kubernetes scheduler places pods, where the defaults fall short, and how to increase node utilization using Configurable Scheduler Profiles on AKS. - -1. [How does kube-scheduler work?](#how-does-the-default-kubernetes-scheduler-place-pods) -2. [Use Configurable Scheduler Profiles to increase node utilization and operator control](#increase-node-utilization-and-operator-control) -3. [Increase AKS cluster CPU utilization up to 85% with Configurable Scheduler](#increase-aks-cpu-utilization) -4. [Increase AKS cluster GPU or CPU utilization while balancing memory with Configurable Scheduler](#increase-aks-gpu-utilization) -5. [FAQ: How do Configurable Scheduler Profiles interact with autoscalers?](#faq) - - - -![Diagram showing increased node utilization with the node bin packing scheduler profile](./hero-image.png) - -## How does the default Kubernetes scheduler place pods? - -The Kubernetes scheduler operates in two cycles: a synchronous scheduling cycle and an asynchronous binding cycle. The scheduling cycle has two sub-phases, filtering and scoring, and only manages one pod at a time. - -1. **Filtering** phase removes unsuitable nodes based on hard and soft constraints. -2. **Scoring** phase calculates a score for the remaining nodes; ultimately, the most suitable node has the highest score. - -Once a node is selected, the binding cycle can process multiple pods in parallel. During this phase, the scheduler attempts to bind the pod to the chosen node. If binding a pod to a node fails, the scheduler tries the node with the next highest score. When filtering and scoring nodes, the default scheduler considers several hard and soft constraints with predefined weights, including (but not limited to): - -1. Resource requirements (CPU, memory) -2. Node affinity/anti-affinity -3. Pod affinity/anti-affinity -4. Taints and tolerations -5. TopologySpreadConstraints - -![Diagram of the kube-scheduler workflow showing pods entering the scheduling cycle and binding cycle to assign a pod on a node](./kube-scheduler-scheduling-phases-diagram.png) - -For a deep dive on the Kubernetes Scheduler visit technical blog from SIG Scheduling contributor and AKS Upstream Engineer, Heba Elayoty, [Deep Dive into the Kubernetes Scheduler Framework][deep-dive-scheduler-framework]. - -### Limitations of the default Kubernetes scheduler - -The default scheduler is primarily designed for general-purpose workloads. It prioritizes nodes with the most available resources using the _LeastAllocated_ scoring strategy. This strategy spreads pods across nodes, even when they could safely be packed more densely. While this works well for many services, the default scheduling criteria, and their fixed priority order, are not suitable for workloads that demand optimizing GPU and CPU utilization. In these scenarios, spreading pods across nodes can lead to fragmented resources, underutilized GPUs, and increased infrastructure cost. - -Today, the default scheduler on AKS lacks the flexibility for users to change which criteria should be prioritized, or ignored, in the scheduling cycle on a per cluster basis. This rigidity often forces users to either accept suboptimal placement or manage a separate custom scheduler, both of which increase operational complexity. Starting with Kubernetes v1.33, AKS introduces [Configurable Scheduler Profiles][concepts-scheduler-configuration] - an AKS-managed CRD - that exposes the upstream scheduling framework without maintaining a separate scheduler. Now, users can adjust the `NodeResourcesFit` plugin from the default configuration to favor nodes with higher utilization to achieve more efficient bin‑packing and reduce infrastructure cost. - -## Configurable Scheduler Profiles on AKS - -[Configurable Scheduler Profiles on AKS][concepts-scheduler-configuration] allow customers to benefit from the extensibility of the [scheduling framework][scheduling-framework-interfaces] while reducing the operational overhead of adopting a second scheduler or defining a custom scheduler. Configurable Scheduler Profiles use a Custom Resource Definition (CRD) that lets users define custom scheduler profiles with their own scheduling logic. A dedicated controller continuously reconciles these user-defined configurations with the underlying kube-scheduler deployment, validating changes and applying them transparently. If a new configuration causes the scheduler to become unhealthy, the controller automatically reverts to the last known good state to ensure cluster stability. - -![Architecture diagram showing how Configurable Scheduler Profiles use a CRD and controller to reconcile user-defined profiles with the kube-scheduler deployment](./config-scheduler-profiles.png) - -A profile is a set of one or more in-tree scheduling plugins and configurations that dictate how to schedule a pod. AKS supports 18 in-tree Kubernetes [scheduling plugins][supported-in-tree-scheduling-plugins]. - -## Increase node utilization and operator control - -In this simple scale-out scenario, manually increasing identical CPU-bound replicas from 8 to 30, the default scheduler distributes pods evenly across nodes. - -Configurable Scheduler Profiles configured for bin-packing show a visible consolidation pattern that differs from the default scheduler, and improves capacity for new pods. This shift occurs without changing the workload, node size, or autoscaling behavior - only the scheduler’s scoring logic. - -![Table showing increased node utilization with the node bin packing scheduler profiles versus the pod distribution using the default scheduler](./default-config-scheduler-comparison.png) - -While this experiment uses intentionally simple, CPU-bound containers to isolate scheduling behavior, the placement patterns observed here can be applied to GPU-bound workloads where capacity constraints elevate the need for utilization improvements. In this constrained GPU scenario the bin‑packing scheduler plays a critical role in maximizing usable capacity of 4 GPU's spread across 2 nodes. By consolidating single‑GPU workloads onto the same node, the scheduler avoids idle GPUs and enables all four GPUs to be actively used before new nodes are required. - -![Table showing increased node utilization with the node bin packing scheduler profiles versus the pod distribution using the default scheduler for gpu's across multiple scheduling iterations](./default-config-scheduler-comparison-gpu.png) - -This change in distribution shape enables downstream efficiencies: improved control for platform engineers, efficient resource usage, and cost optimization that are difficult to achieve when pods are evenly spread. **The key takeaway is that each profile expresses a distinct scheduling intent. The next two sections detail how the scoring strategies, MostAllocated and RequestedToCapacityRatio achieve these outcomes.** - -| Scheduler | Scoring Strategy | Scheduling intent | Operator Benefits | -|---|---|---|---| -| Default scheduler | NodeResourcesFit: LeastAllocated | Balance and hotspot reduction | No tuning | -| Configurable Scheduler Profile | NodeResourcesFit: MostAllocated | Maximize consolidation / bin‑packing | Maximum node utilization, highest cost reduction potential | -| Configurable Scheduler Profile | NodeResourcesFit: RequestedToCapacityRatio | Targeted utilization with headroom | ✅ **Recommended strategy** Increased utilization with stronger control over consolidation and burst headroom than `MostAllocated` | - -### Increase AKS CPU utilization - -`RequestedToCapacityRatio` scores nodes based on the ratio of requested resources to total node capacity after the pod is _hypothetically_ placed. This strategy enables more fine-grained bin‑packing by allowing operators to define an ideal utilization curve for their nodes rather than simply preferring the most or least utilized nodes. Lastly, it is critical to note that `PodTopologySpread` is disabled in this profile because bin-packing and zone-spreading are opposing goals and the scheduling logic may prioritize pod spreading. If you need both high utilization _and_ zone resilience, define a new profile to achieve both goals. - -By shaping the scoring curve to target a range of 50-85% CPU utilization, operators can increase pod density on provisioned nodes while preserving headroom for bursts, background processes, and system components in CPU-based workloads. [Configure node bin-packing][configure-requested-to-capacity] using the `RequestedToCapacityRatio` strategy to improve utilization and reduce infrastructure costs. - -**This bin packing profile is configured to favor nodes within a utilization band of 50-85%, avoiding empty nodes, and severely deprioritizing nearly full nodes at 90% utilization or more, to limit oversaturated nodes. Given this level of configuration detail, `RequestedToCapacityRatio` is the recommended scoring strategy for node bin‑packing on AKS for production clusters.** - -:::note -Scoring strategy can also be used for GPU by changing the target resource. Adjust resources, resource weights, utilization thresholds, and plugin parameters to match your VM SKUs, workload patterns, and cluster topology. -::: - -```yaml -apiVersion: aks.azure.com/v1alpha1 -kind: SchedulerConfiguration -metadata: - name: upstream -spec: - rawConfig: | - apiVersion: kubescheduler.config.k8s.io/v1 - kind: KubeSchedulerConfiguration - profiles: - - schedulerName: cpu-binpacking-scheduler - plugins: - multiPoint: - enabled: - - name: NodeResourcesFit - disabled: - - name: PodTopologySpread - pluginConfig: - - name: NodeResourcesFit - args: - apiVersion: kubescheduler.config.k8s.io/v1 - kind: NodeResourcesFitArgs - scoringStrategy: - type: RequestedToCapacityRatio - resources: - - name: cpu - weight: 8 - - name: memory - weight: 1 - requestedToCapacityRatio: - shape: - - utilization: 0 - score: 0 - - utilization: 30 - score: 8 - - utilization: 50 - score: 10 - - utilization: 85 - score: 10 - - utilization: 90 - score: 3 - - utilization: 100 - score: 0 -``` - -### Increase AKS GPU utilization - -`MostAllocated` scores nodes based on its current resource utilization, favoring nodes that are already heavily used. `RequestedToCapacityRatio`, on the other hand, scores nodes based on both the resource requests and the remaining node capacity, making `MostAllocated` more suitable for an aggressive cost-optimization scheduling strategy since it inetionally ignores capacity. When paired with MostAllocated, `NodeResourcesBalancedAllocation` complements the behavior because it encourages pod placement on nodes with user-defined proportional utilization, helping reduce bottlenecks caused by asymmetric resource pressure. Lastly, it is critical to note that `PodTopologySpread` is disabled in this profile because bin-packing and zone-spreading are opposing goals. Enabling both can result in spreading over consolidation, weakening the intended packing behavior. - -When combined, these plugins favor GPU‑bound nodes with balanced CPU and memory usage over nodes with large amounts of unused memory or fragmented resources. This results in more efficient GPU placement and fewer partially utilized nodes. [Configure node bin-packing][configure-most-allocated] using the MostAllocated strategy to improve utilization and reduce infrastructure costs. - -**This scheduler configuration maximizes GPU utilization by consolidating smaller jobs onto fewer nodes, reducing idle accelerator capacity while maintaining reasonable CPU and memory balance.** - -:::note -Adjust resources, resource weights, utilization thresholds, and plugin parameters to match your VM SKUs, workload patterns, and cluster topology. -::: - -```yaml -apiVersion: aks.azure.com/v1alpha1 -kind: SchedulerConfiguration -metadata: - name: upstream -spec: - rawConfig: | - apiVersion: kubescheduler.config.k8s.io/v1 - kind: KubeSchedulerConfiguration - profiles: - - schedulerName: gpu-node-binpacking-scheduler - plugins: - multiPoint: - enabled: - - name: NodeResourcesFit - - name: NodeResourcesBalancedAllocation - disabled: - - name: PodTopologySpread - pluginConfig: - - name: NodeResourcesFit - args: - apiVersion: kubescheduler.config.k8s.io/v1 - kind: NodeResourcesFitArgs - scoringStrategy: - type: MostAllocated - resources: - - name: nvidia.com/gpu - weight: 8 - - name: cpu - weight: 1 - - name: NodeResourcesBalancedAllocation - args: - apiVersion: kubescheduler.config.k8s.io/v1 - kind: NodeResourcesBalancedAllocationArgs - resources: - - name: cpu - weight: 1 - - name: memory - weight: 1 -``` - -### FAQ - -1. Which bin-packing strategy does AKS recommend to increase node utilization? - - AKS recommends using the scoring strategy `RequestedToCapacityRatio` because it provides a more granular scoring approach allowing users to define an ideal utilization curve for their respective nodes. For example, this bin packing strategy allows users to configure a target utilization of 85%. - -2. How do Configurable Scheduler Profiles interact with autoscalers such as Node Auto Provisioning (NAP), Cluster Autoscaler (CA), and Vertical Pod Autoscaler (VPA)? - - These components are complementary to each other. Configurable Scheduler Profiles influence how pods are placed on nodes, while autoscalers make scaling decisions based on resource utilization and pending pods. Look out for a incoming blog that details how scheduling constraints effect Node Auto Provisoing. - - - **Node Auto Provisioning (NAP)** is triggered when pods are unschedulable. If a suitable node already exists, that pod will be scheduled with the defined Configurable Scheduler Profile. If no suitable node exists, NAP provisions new capacity and schedules the pod. - - **Cluster Autoscaler (CA)** manages node scale-up and scale-down. On scale-up, CA is triggered when there aren't any suitable nodes available for the pending pod. Using Configurable Scheduler Profiles ensures nodes are only scaled when provisioned resources are no longer suitable. On scale-down, CA is triggered when nodes fall below utilization thresholds, the default is 50%. As active nodes are packed more efficiently, underutilized nodes become easier candidates for removal. - - **Vertical Pod Autoscaler (VPA)** optimizes resource utilization patterns in pods. As pods are recreated with updated CPU and memory requests, they are scheduled using the configured scheduler profile, allowing placement decisions to reflect the new resource requirements. - -3. What if a resource, like `memory`, is omitted in the `scoringStrategy`? - - If a resource is omitted in the `scoringStrategy`, then that resource will not be considered in the filter or scoring cycles of the defined Configurable Scheduler Profile. If that resource should be considered, but with a reduced influence on the final score, it can be included with reduced weight. - -4. Can multiple Configurable Scheduler Profiles be used for different workloads on the same cluster? - - Yes, multiple scheduling profiles can coexist in a single cluster. This allows different placement strategies (for example, cost‑optimized vs. latency‑sensitive workloads) to run side‑by‑side. Visit the documentation for a [multiple scheduler profiles example][configure-multi-config]. - - - Multiple profiles can be defined centrally in a single scheduler configuration. - - Individual workloads select a profile via `schedulerName` in the pod spec. - -5. How do I monitor whether my scheduler profile is improving utilization? - - Monitor these signals to confirm that the scheduler is behaving correctly. Over time, you should see higher average node utilization, reduced variance between nodes, and fewer lightly utilized nodes. - - - Track node‑level utilization metrics, including CPU and memory utilization per node and distribution of pods across nodes, using Azure Monitor Container Insights, the AKS [node viewer tool][aks-node-viewer], or `kubectl top nodes` for quick validation. - - Review autoscaler outcomes, looking for fewer scale‑ups during normal load and more decisive scale‑downs after demand drops. - - Measure cost metrics, such as reduced idle costs when you use the [Cost Analysis add-on][aks-cost-analysis-add-on]. - -## Next steps: Optimize Azure resources and test Configurable Scheduler Profiles on AKS - -With Configurable Scheduler Profiles, teams gain fine-grained control over pod placement strategies like bin-packing, topology distribution, and resource-based scoring that directly addresses challenges related to application resilience and resource utilization for their AKS clusters. By leveraging these scheduling plugins, AKS users can ensure their workloads make full use of available GPU capacity, reduce idle costs, and avoid costly overprovisioning. This not only improves return on investment (ROI) but also accelerates development by allowing more jobs to run concurrently and reliably. - -- For additional guidance and best practices, see [kube-scheduler best practices][best-practices-advanced-scheduler] -- Increase node utilization using [Configurable Scheduler Profiles][node-bin-packing-configurations] -- If additional capabilities or ML frameworks are needed to schedule and queue batch workloads, you can [install and configure Kueue on AKS][kueue-overview] to ensure efficient, policy-driven scheduling in AKS clusters. - -[concepts-scheduler-configuration]: https://learn.microsoft.com/azure/aks/concepts-scheduler-configuration -[kueue-overview]: https://learn.microsoft.com/azure/aks/kueue-overview -[best-practices-advanced-scheduler]: https://learn.microsoft.com/azure/aks/operator-best-practices-advanced-scheduler -[scheduling-framework-interfaces]: https://kubernetes.io/docs/concepts/scheduling-eviction/scheduling-framework/#interfaces -[supported-in-tree-scheduling-plugins]: https://learn.microsoft.com/azure/aks/concepts-scheduler-configuration#supported-in-tree-scheduling-plugins -[node-bin-packing-configurations]: https://learn.microsoft.com/azure/aks/configure-node-binpack-scheduler?tabs=new-cluster -[datadog-state-of-containers]: https://www.datadoghq.com/state-of-containers-and-serverless/ -[configure-requested-to-capacity]: https://learn.microsoft.com/azure/aks/configure-node-binpack-scheduler?tabs=new-cluster#configure-node-bin-packing-with-requestedtocapacity-plugin -[configure-most-allocated]: https://learn.microsoft.com/azure/aks/configure-node-binpack-scheduler?tabs=new-cluster#configure-node-bin-packing-with-mostallocated-plugin -[configure-multi-config]: https://learn.microsoft.com/azure/aks/configure-aks-scheduler?tabs=new-cluster#configure-multiple-scheduler-profiles -[aks-cost-analysis-add-on]: https://learn.microsoft.com/azure/aks/cost-analysis -[wb-gpu-utilization]: https://wandb.ai/wandb_fc/articles/reports/Monitor-Improve-GPU-Usage-for-Model-Training--Vmlldzo1NDQzNjM3 -[deep-dive-scheduler-framework]: https://helayoty.org/blog/inside-kube-scheduler-the-plugin -[aks-node-viewer]: https://github.com/Azure/aks-node-viewer From 84a67267f3381560904b009dc5814e75bc09f769 Mon Sep 17 00:00:00 2001 From: colinmixonn <109253437+colinmixonn@users.noreply.github.com> Date: Wed, 1 Apr 2026 17:15:09 -0700 Subject: [PATCH 149/164] Update publication date for AKS Configurable Scheduler Profiles --- .../2026-04-06-aks-config-scheduler-profiles-preview/index.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/website/blog/2026-04-06-aks-config-scheduler-profiles-preview/index.md b/website/blog/2026-04-06-aks-config-scheduler-profiles-preview/index.md index 31b6a299a..bffd858fa 100644 --- a/website/blog/2026-04-06-aks-config-scheduler-profiles-preview/index.md +++ b/website/blog/2026-04-06-aks-config-scheduler-profiles-preview/index.md @@ -1,7 +1,7 @@ --- title: "AKS Configurable Scheduler Profiles (preview)" description: "Improve GPU and CPU utilization, align pod placement to critical workloads, and reduce node costs at scale with Configurable Scheduler Profiles on AKS." -date: 2026-03-31 +date: 2026-04-06 authors: [colin-mixon] tags: [ai, performance, scheduler, best-practices, cost] --- From 79c481deee4603464978101749fca8e0cbcc0e53 Mon Sep 17 00:00:00 2001 From: colinmixonn <109253437+colinmixonn@users.noreply.github.com> Date: Wed, 1 Apr 2026 17:53:26 -0700 Subject: [PATCH 150/164] Apply suggestions from code review Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> --- .../index.md | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/website/blog/2026-04-06-aks-config-scheduler-profiles-preview/index.md b/website/blog/2026-04-06-aks-config-scheduler-profiles-preview/index.md index bffd858fa..119d5ad43 100644 --- a/website/blog/2026-04-06-aks-config-scheduler-profiles-preview/index.md +++ b/website/blog/2026-04-06-aks-config-scheduler-profiles-preview/index.md @@ -63,9 +63,9 @@ Configurable Scheduler Profiles configured for bin-packing show a visible consol ![Table showing increased node utilization with the node bin packing scheduler profiles versus the pod distribution using the default scheduler](./default-config-scheduler-comparison.png) -While this experiment uses intentionally simple, CPU-bound containers to isolate scheduling behavior, the placement patterns observed here can be applied to GPU-bound workloads where capacity constraints elevate the need for utilization improvements. In this constrained GPU scenario the bin‑packing scheduler plays a critical role in maximizing usable capacity of 4 GPU's spread across 2 nodes. By consolidating single‑GPU workloads onto the same node, the scheduler avoids idle GPUs and enables all four GPUs to be actively used before new nodes are required. +While this experiment uses intentionally simple, CPU-bound containers to isolate scheduling behavior, the placement patterns observed here can be applied to GPU-bound workloads where capacity constraints elevate the need for utilization improvements. In this constrained GPU scenario the bin‑packing scheduler plays a critical role in maximizing usable capacity of 4 GPUs spread across 2 nodes. By consolidating single‑GPU workloads onto the same node, the scheduler avoids idle GPUs and enables all four GPUs to be actively used before new nodes are required. -![Table showing increased node utilization with the node bin packing scheduler profiles versus the pod distribution using the default scheduler for gpu's across multiple scheduling iterations](./default-config-scheduler-comparison-gpu.png) +![Table showing increased node utilization with the node bin packing scheduler profiles versus the pod distribution using the default scheduler for GPUs across multiple scheduling iterations](./default-config-scheduler-comparison-gpu.png) This change in distribution shape enables downstream efficiencies: improved control for platform engineers, efficient resource usage, and cost optimization that are difficult to achieve when pods are evenly spread. **The key takeaway is that each profile expresses a distinct scheduling intent. The next two sections detail how the scoring strategies, MostAllocated and RequestedToCapacityRatio achieve these outcomes.** @@ -134,7 +134,7 @@ spec: ### Increase AKS GPU utilization -`MostAllocated` scores nodes based on its current resource utilization, favoring nodes that are already heavily used. `RequestedToCapacityRatio`, on the other hand, scores nodes based on both the resource requests and the remaining node capacity, making `MostAllocated` more suitable for an aggressive cost-optimization scheduling strategy since it inetionally ignores capacity. When paired with MostAllocated, `NodeResourcesBalancedAllocation` complements the behavior because it encourages pod placement on nodes with user-defined proportional utilization, helping reduce bottlenecks caused by asymmetric resource pressure. Lastly, it is critical to note that `PodTopologySpread` is disabled in this profile because bin-packing and zone-spreading are opposing goals. Enabling both can result in spreading over consolidation, weakening the intended packing behavior. +`MostAllocated` scores nodes based on its current resource utilization, favoring nodes that are already heavily used. `RequestedToCapacityRatio`, on the other hand, scores nodes based on both the resource requests and the remaining node capacity, making `MostAllocated` more suitable for an aggressive cost-optimization scheduling strategy since it intentionally ignores capacity. When paired with MostAllocated, `NodeResourcesBalancedAllocation` complements the behavior because it encourages pod placement on nodes with user-defined proportional utilization, helping reduce bottlenecks caused by asymmetric resource pressure. Lastly, it is critical to note that `PodTopologySpread` is disabled in this profile because bin-packing and zone-spreading are opposing goals. Enabling both can result in spreading over consolidation, weakening the intended packing behavior. When combined, these plugins favor GPU‑bound nodes with balanced CPU and memory usage over nodes with large amounts of unused memory or fragmented resources. This results in more efficient GPU placement and fewer partially utilized nodes. [Configure node bin-packing][configure-most-allocated] using the MostAllocated strategy to improve utilization and reduce infrastructure costs. @@ -193,7 +193,7 @@ spec: 2. How do Configurable Scheduler Profiles interact with autoscalers such as Node Auto Provisioning (NAP), Cluster Autoscaler (CA), and Vertical Pod Autoscaler (VPA)? - These components are complementary to each other. Configurable Scheduler Profiles influence how pods are placed on nodes, while autoscalers make scaling decisions based on resource utilization and pending pods. Look out for a incoming blog that details how scheduling constraints effect Node Auto Provisoing. + These components are complementary to each other. Configurable Scheduler Profiles influence how pods are placed on nodes, while autoscalers make scaling decisions based on resource utilization and pending pods. Look out for an upcoming blog that details how scheduling constraints affect Node Auto Provisioning. - **Node Auto Provisioning (NAP)** is triggered when pods are unschedulable. If a suitable node already exists, that pod will be scheduled with the defined Configurable Scheduler Profile. If no suitable node exists, NAP provisions new capacity and schedules the pod. - **Cluster Autoscaler (CA)** manages node scale-up and scale-down. On scale-up, CA is triggered when there aren't any suitable nodes available for the pending pod. Using Configurable Scheduler Profiles ensures nodes are only scaled when provisioned resources are no longer suitable. On scale-down, CA is triggered when nodes fall below utilization thresholds, the default is 50%. As active nodes are packed more efficiently, underutilized nodes become easier candidates for removal. From 18ba6fbaa577fe1771cfd2cf0f7248698f62f70f Mon Sep 17 00:00:00 2001 From: colinmixonn <109253437+colinmixonn@users.noreply.github.com> Date: Wed, 1 Apr 2026 17:58:39 -0700 Subject: [PATCH 151/164] Apply suggestions from code review Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> --- .../2026-04-06-aks-config-scheduler-profiles-preview/index.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/website/blog/2026-04-06-aks-config-scheduler-profiles-preview/index.md b/website/blog/2026-04-06-aks-config-scheduler-profiles-preview/index.md index 119d5ad43..985382566 100644 --- a/website/blog/2026-04-06-aks-config-scheduler-profiles-preview/index.md +++ b/website/blog/2026-04-06-aks-config-scheduler-profiles-preview/index.md @@ -39,7 +39,7 @@ Once a node is selected, the binding cycle can process multiple pods in parallel ![Diagram of the kube-scheduler workflow showing pods entering the scheduling cycle and binding cycle to assign a pod on a node](./kube-scheduler-scheduling-phases-diagram.png) -For a deep dive on the Kubernetes Scheduler visit technical blog from SIG Scheduling contributor and AKS Upstream Engineer, Heba Elayoty, [Deep Dive into the Kubernetes Scheduler Framework][deep-dive-scheduler-framework]. +For a deep dive on the Kubernetes scheduler, visit the technical blog post from SIG Scheduling contributor and AKS upstream engineer Heba Elayoty, [Deep Dive into the Kubernetes Scheduler Framework][deep-dive-scheduler-framework]. ### Limitations of the default Kubernetes scheduler From 840c9fef561787b0e5f6874d678e13926003d53c Mon Sep 17 00:00:00 2001 From: colinmixonn <109253437+colinmixonn@users.noreply.github.com> Date: Thu, 2 Apr 2026 10:31:15 -0700 Subject: [PATCH 152/164] Fix formatting in AKS scheduler profiles documentation --- .../2026-04-06-aks-config-scheduler-profiles-preview/index.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/website/blog/2026-04-06-aks-config-scheduler-profiles-preview/index.md b/website/blog/2026-04-06-aks-config-scheduler-profiles-preview/index.md index 985382566..4544d1b3c 100644 --- a/website/blog/2026-04-06-aks-config-scheduler-profiles-preview/index.md +++ b/website/blog/2026-04-06-aks-config-scheduler-profiles-preview/index.md @@ -73,7 +73,7 @@ This change in distribution shape enables downstream efficiencies: improved cont |---|---|---|---| | Default scheduler | NodeResourcesFit: LeastAllocated | Balance and hotspot reduction | No tuning | | Configurable Scheduler Profile | NodeResourcesFit: MostAllocated | Maximize consolidation / bin‑packing | Maximum node utilization, highest cost reduction potential | -| Configurable Scheduler Profile | NodeResourcesFit: RequestedToCapacityRatio | Targeted utilization with headroom | ✅ **Recommended strategy** Increased utilization with stronger control over consolidation and burst headroom than `MostAllocated` | +| Configurable Scheduler Profile | NodeResourcesFit: RequestedToCapacityRatio | Targeted utilization with headroom | ✅ **Recommended strategy** Increased utilization with stronger control over consolidation and burst headroom than `MostAllocated`| ### Increase AKS CPU utilization From 14f98e061e516a9432dfdd249917cf44fbe77c48 Mon Sep 17 00:00:00 2001 From: colinmixonn <109253437+colinmixonn@users.noreply.github.com> Date: Thu, 2 Apr 2026 10:51:10 -0700 Subject: [PATCH 153/164] Apply suggestions from code review Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> --- .../index.md | 5 ++--- 1 file changed, 2 insertions(+), 3 deletions(-) diff --git a/website/blog/2026-04-06-aks-config-scheduler-profiles-preview/index.md b/website/blog/2026-04-06-aks-config-scheduler-profiles-preview/index.md index 4544d1b3c..3b5ed4c2a 100644 --- a/website/blog/2026-04-06-aks-config-scheduler-profiles-preview/index.md +++ b/website/blog/2026-04-06-aks-config-scheduler-profiles-preview/index.md @@ -12,14 +12,13 @@ In 2025, Datadog found most Kubernetes containers use less than [25% of their re This blog explains how the default Kubernetes scheduler places pods, where the defaults fall short, and how to increase node utilization using Configurable Scheduler Profiles on AKS. + + 1. [How does kube-scheduler work?](#how-does-the-default-kubernetes-scheduler-place-pods) 2. [Use Configurable Scheduler Profiles to increase node utilization and operator control](#increase-node-utilization-and-operator-control) 3. [Increase AKS cluster CPU utilization up to 85% with Configurable Scheduler](#increase-aks-cpu-utilization) 4. [Increase AKS cluster GPU or CPU utilization while balancing memory with Configurable Scheduler](#increase-aks-gpu-utilization) 5. [FAQ: How do Configurable Scheduler Profiles interact with autoscalers?](#faq) - - - ![Diagram showing increased node utilization with the node bin packing scheduler profile](./hero-image.png) ## How does the default Kubernetes scheduler place pods? From 7033113c8b3b12e23b0ba631d408f690911acd9c Mon Sep 17 00:00:00 2001 From: colinmixonn <109253437+colinmixonn@users.noreply.github.com> Date: Thu, 2 Apr 2026 11:00:40 -0700 Subject: [PATCH 154/164] Add image and FAQ section to AKS Config Scheduler post --- .../2026-04-06-aks-config-scheduler-profiles-preview/index.md | 1 + 1 file changed, 1 insertion(+) diff --git a/website/blog/2026-04-06-aks-config-scheduler-profiles-preview/index.md b/website/blog/2026-04-06-aks-config-scheduler-profiles-preview/index.md index 3b5ed4c2a..97adf20e8 100644 --- a/website/blog/2026-04-06-aks-config-scheduler-profiles-preview/index.md +++ b/website/blog/2026-04-06-aks-config-scheduler-profiles-preview/index.md @@ -19,6 +19,7 @@ This blog explains how the default Kubernetes scheduler places pods, where the d 3. [Increase AKS cluster CPU utilization up to 85% with Configurable Scheduler](#increase-aks-cpu-utilization) 4. [Increase AKS cluster GPU or CPU utilization while balancing memory with Configurable Scheduler](#increase-aks-gpu-utilization) 5. [FAQ: How do Configurable Scheduler Profiles interact with autoscalers?](#faq) + ![Diagram showing increased node utilization with the node bin packing scheduler profile](./hero-image.png) ## How does the default Kubernetes scheduler place pods? From a386b158c4d229f1709ce1ae8e0c8f5adbd45e0c Mon Sep 17 00:00:00 2001 From: colinmixonn <109253437+colinmixonn@users.noreply.github.com> Date: Thu, 2 Apr 2026 11:56:28 -0700 Subject: [PATCH 155/164] Apply suggestions from code review Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> --- .../index.md | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/website/blog/2026-04-06-aks-config-scheduler-profiles-preview/index.md b/website/blog/2026-04-06-aks-config-scheduler-profiles-preview/index.md index 97adf20e8..245b4c800 100644 --- a/website/blog/2026-04-06-aks-config-scheduler-profiles-preview/index.md +++ b/website/blog/2026-04-06-aks-config-scheduler-profiles-preview/index.md @@ -69,11 +69,11 @@ While this experiment uses intentionally simple, CPU-bound containers to isolate This change in distribution shape enables downstream efficiencies: improved control for platform engineers, efficient resource usage, and cost optimization that are difficult to achieve when pods are evenly spread. **The key takeaway is that each profile expresses a distinct scheduling intent. The next two sections detail how the scoring strategies, MostAllocated and RequestedToCapacityRatio achieve these outcomes.** -| Scheduler | Scoring Strategy | Scheduling intent | Operator Benefits | -|---|---|---|---| -| Default scheduler | NodeResourcesFit: LeastAllocated | Balance and hotspot reduction | No tuning | -| Configurable Scheduler Profile | NodeResourcesFit: MostAllocated | Maximize consolidation / bin‑packing | Maximum node utilization, highest cost reduction potential | -| Configurable Scheduler Profile | NodeResourcesFit: RequestedToCapacityRatio | Targeted utilization with headroom | ✅ **Recommended strategy** Increased utilization with stronger control over consolidation and burst headroom than `MostAllocated`| +| Scheduler | Scoring strategy | Scheduling intent | Operator benefits | +| ------------------------------ | -------------------------------------- | ------------------------------------ | ------------------------------------------------------------------------------------------------------------------ | +| Default scheduler | NodeResourcesFit: LeastAllocated | Balance and hotspot reduction | No tuning | +| Configurable Scheduler Profile | NodeResourcesFit: MostAllocated | Maximize consolidation / bin‑packing | Maximum node utilization, highest cost reduction potential | +| Configurable Scheduler Profile | NodeResourcesFit: RequestedToCapacityRatio | Targeted utilization with headroom | ✅ **Recommended strategy** Increased utilization with stronger control over consolidation and burst headroom than `MostAllocated` | ### Increase AKS CPU utilization From baae67f8108e806fd620e4d1d503a0ccd13e1b41 Mon Sep 17 00:00:00 2001 From: colinmixonn <109253437+colinmixonn@users.noreply.github.com> Date: Fri, 3 Apr 2026 10:47:08 -0700 Subject: [PATCH 156/164] Apply suggestions from code review Co-authored-by: Ahmed Sabbour <103856+sabbour@users.noreply.github.com> --- .../index.md | 15 +++++++-------- 1 file changed, 7 insertions(+), 8 deletions(-) diff --git a/website/blog/2026-04-06-aks-config-scheduler-profiles-preview/index.md b/website/blog/2026-04-06-aks-config-scheduler-profiles-preview/index.md index 245b4c800..27af2ca31 100644 --- a/website/blog/2026-04-06-aks-config-scheduler-profiles-preview/index.md +++ b/website/blog/2026-04-06-aks-config-scheduler-profiles-preview/index.md @@ -8,9 +8,9 @@ tags: [ai, performance, scheduler, best-practices, cost] In 2025, Datadog found most Kubernetes containers use less than [25% of their requested CPU][datadog-state-of-containers], and in 2023, Weights and Biases found that nearly a third of GPU users [average less than 15% utilization][wb-gpu-utilization]. Underutilized resources materially contribute to increased infrastructure cost and this data signals that most users can greatly improve resource utilization. While there are many factors that impact node utilization, as a core component of the Kubernetes control plane, the kube-scheduler plays a critical role in node utilization. -[Configurable Scheduler Profiles][concepts-scheduler-configuration] on AKS lets customers configure their own scheduling logic: enable specific plugins, adjust plugin priorities, and tune parameter weights. **The result: higher node density, better GPU utilization, and lower infrastructure costs.** +[Configurable Scheduler Profiles][concepts-scheduler-configuration] on Azure Kubernetes Service (AKS) let you configure your own scheduling logic: enable specific plugins, adjust plugin priorities, and tune parameter weights. **The result: higher node density, better GPU utilization, and lower infrastructure costs.** -This blog explains how the default Kubernetes scheduler places pods, where the defaults fall short, and how to increase node utilization using Configurable Scheduler Profiles on AKS. +You'll learn how the default Kubernetes scheduler places pods, where the defaults fall short, and how to increase node utilization with Configurable Scheduler Profiles on AKS. @@ -39,7 +39,7 @@ Once a node is selected, the binding cycle can process multiple pods in parallel ![Diagram of the kube-scheduler workflow showing pods entering the scheduling cycle and binding cycle to assign a pod on a node](./kube-scheduler-scheduling-phases-diagram.png) -For a deep dive on the Kubernetes scheduler, visit the technical blog post from SIG Scheduling contributor and AKS upstream engineer Heba Elayoty, [Deep Dive into the Kubernetes Scheduler Framework][deep-dive-scheduler-framework]. +For more on how the Kubernetes scheduler works, see the technical blog post from SIG Scheduling contributor and AKS upstream engineer Heba Elayoty, [Deep Dive into the Kubernetes Scheduler Framework][deep-dive-scheduler-framework]. ### Limitations of the default Kubernetes scheduler @@ -67,7 +67,7 @@ While this experiment uses intentionally simple, CPU-bound containers to isolate ![Table showing increased node utilization with the node bin packing scheduler profiles versus the pod distribution using the default scheduler for GPUs across multiple scheduling iterations](./default-config-scheduler-comparison-gpu.png) -This change in distribution shape enables downstream efficiencies: improved control for platform engineers, efficient resource usage, and cost optimization that are difficult to achieve when pods are evenly spread. **The key takeaway is that each profile expresses a distinct scheduling intent. The next two sections detail how the scoring strategies, MostAllocated and RequestedToCapacityRatio achieve these outcomes.** +This change in distribution shape enables downstream efficiencies: improved control for platform engineers, efficient resource usage, and cost optimization that are difficult to achieve when pods are evenly spread. **Each profile expresses a distinct scheduling intent. The next two sections detail how the scoring strategies, MostAllocated and RequestedToCapacityRatio achieve these outcomes.** | Scheduler | Scoring strategy | Scheduling intent | Operator benefits | | ------------------------------ | -------------------------------------- | ------------------------------------ | ------------------------------------------------------------------------------------------------------------------ | @@ -77,7 +77,7 @@ This change in distribution shape enables downstream efficiencies: improved cont ### Increase AKS CPU utilization -`RequestedToCapacityRatio` scores nodes based on the ratio of requested resources to total node capacity after the pod is _hypothetically_ placed. This strategy enables more fine-grained bin‑packing by allowing operators to define an ideal utilization curve for their nodes rather than simply preferring the most or least utilized nodes. Lastly, it is critical to note that `PodTopologySpread` is disabled in this profile because bin-packing and zone-spreading are opposing goals and the scheduling logic may prioritize pod spreading. If you need both high utilization _and_ zone resilience, define a new profile to achieve both goals. +`RequestedToCapacityRatio` scores nodes based on the ratio of requested resources to total node capacity after the pod is _hypothetically_ placed. This strategy enables more fine-grained bin‑packing by allowing operators to define an ideal utilization curve for their nodes rather than simply preferring the most or least utilized nodes. `PodTopologySpread` is disabled in this profile because bin-packing and zone-spreading are opposing goals and the scheduling logic may prioritize pod spreading. If you need both high utilization _and_ zone resilience, define a new profile to achieve both goals. By shaping the scoring curve to target a range of 50-85% CPU utilization, operators can increase pod density on provisioned nodes while preserving headroom for bursts, background processes, and system components in CPU-based workloads. [Configure node bin-packing][configure-requested-to-capacity] using the `RequestedToCapacityRatio` strategy to improve utilization and reduce infrastructure costs. @@ -220,12 +220,11 @@ spec: ## Next steps: Optimize Azure resources and test Configurable Scheduler Profiles on AKS -With Configurable Scheduler Profiles, teams gain fine-grained control over pod placement strategies like bin-packing, topology distribution, and resource-based scoring that directly addresses challenges related to application resilience and resource utilization for their AKS clusters. By leveraging these scheduling plugins, AKS users can ensure their workloads make full use of available GPU capacity, reduce idle costs, and avoid costly overprovisioning. This not only improves return on investment (ROI) but also accelerates development by allowing more jobs to run concurrently and reliably. - +Configurable Scheduler Profiles give you direct control over pod placement. With these scheduling plugins, your workloads make full use of available GPU capacity, reduce idle costs, and avoid costly overprovisioning. - For additional guidance and best practices, see [kube-scheduler best practices][best-practices-advanced-scheduler] - Increase node utilization using [Configurable Scheduler Profiles][node-bin-packing-configurations] - If additional capabilities or ML frameworks are needed to schedule and queue batch workloads, you can [install and configure Kueue on AKS][kueue-overview] to ensure efficient, policy-driven scheduling in AKS clusters. - +- To schedule and queue batch workloads, [install and configure Kueue on AKS][kueue-overview] for efficient, policy-driven scheduling. [concepts-scheduler-configuration]: https://learn.microsoft.com/azure/aks/concepts-scheduler-configuration [kueue-overview]: https://learn.microsoft.com/azure/aks/kueue-overview [best-practices-advanced-scheduler]: https://learn.microsoft.com/azure/aks/operator-best-practices-advanced-scheduler From cb7c8ddd5ae233f840b4ee477b836809b5f86425 Mon Sep 17 00:00:00 2001 From: colinmixonn <109253437+colinmixonn@users.noreply.github.com> Date: Fri, 3 Apr 2026 10:48:41 -0700 Subject: [PATCH 157/164] Enhance explanation of AKS GPU scheduling strategies Clarified the explanation of the MostAllocated scheduling strategy and its interaction with other plugins, emphasizing its role in maximizing GPU utilization and reducing idle capacity. --- .../2026-04-06-aks-config-scheduler-profiles-preview/index.md | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/website/blog/2026-04-06-aks-config-scheduler-profiles-preview/index.md b/website/blog/2026-04-06-aks-config-scheduler-profiles-preview/index.md index 27af2ca31..7903a1598 100644 --- a/website/blog/2026-04-06-aks-config-scheduler-profiles-preview/index.md +++ b/website/blog/2026-04-06-aks-config-scheduler-profiles-preview/index.md @@ -134,7 +134,7 @@ spec: ### Increase AKS GPU utilization -`MostAllocated` scores nodes based on its current resource utilization, favoring nodes that are already heavily used. `RequestedToCapacityRatio`, on the other hand, scores nodes based on both the resource requests and the remaining node capacity, making `MostAllocated` more suitable for an aggressive cost-optimization scheduling strategy since it intentionally ignores capacity. When paired with MostAllocated, `NodeResourcesBalancedAllocation` complements the behavior because it encourages pod placement on nodes with user-defined proportional utilization, helping reduce bottlenecks caused by asymmetric resource pressure. Lastly, it is critical to note that `PodTopologySpread` is disabled in this profile because bin-packing and zone-spreading are opposing goals. Enabling both can result in spreading over consolidation, weakening the intended packing behavior. +`MostAllocated` scores nodes based on its current resource utilization, favoring nodes that are already heavily used. `RequestedToCapacityRatio`, on the other hand, scores nodes based on both the resource requests and the remaining node capacity, making `MostAllocated` more suitable for an aggressive cost-optimization scheduling strategy since it intentionally ignores capacity. When paired with MostAllocated, `NodeResourcesBalancedAllocation` complements the behavior because it encourages pod placement on nodes with user-defined proportional utilization, helping reduce bottlenecks caused by asymmetric resource pressure. `PodTopologySpread` is disabled in this profile because bin-packing and zone-spreading are opposing goals. Enabling both can result in spreading over consolidation, weakening the intended packing behavior. When combined, these plugins favor GPU‑bound nodes with balanced CPU and memory usage over nodes with large amounts of unused memory or fragmented resources. This results in more efficient GPU placement and fewer partially utilized nodes. [Configure node bin-packing][configure-most-allocated] using the MostAllocated strategy to improve utilization and reduce infrastructure costs. @@ -225,6 +225,7 @@ Configurable Scheduler Profiles give you direct control over pod placement. With - Increase node utilization using [Configurable Scheduler Profiles][node-bin-packing-configurations] - If additional capabilities or ML frameworks are needed to schedule and queue batch workloads, you can [install and configure Kueue on AKS][kueue-overview] to ensure efficient, policy-driven scheduling in AKS clusters. - To schedule and queue batch workloads, [install and configure Kueue on AKS][kueue-overview] for efficient, policy-driven scheduling. + [concepts-scheduler-configuration]: https://learn.microsoft.com/azure/aks/concepts-scheduler-configuration [kueue-overview]: https://learn.microsoft.com/azure/aks/kueue-overview [best-practices-advanced-scheduler]: https://learn.microsoft.com/azure/aks/operator-best-practices-advanced-scheduler From d7c28fe5f641e4e767fc6af767c397eba2e5e039 Mon Sep 17 00:00:00 2001 From: colinmixonn <109253437+colinmixonn@users.noreply.github.com> Date: Fri, 3 Apr 2026 10:51:14 -0700 Subject: [PATCH 158/164] Enhance explanation of Configurable Scheduler Profiles on AKS Clarified the benefits and functionality of Configurable Scheduler Profiles on AKS, emphasizing the use of a Custom Resource Definition (CRD) and the role of the dedicated controller in maintaining cluster stability. --- .../2026-04-06-aks-config-scheduler-profiles-preview/index.md | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/website/blog/2026-04-06-aks-config-scheduler-profiles-preview/index.md b/website/blog/2026-04-06-aks-config-scheduler-profiles-preview/index.md index 7903a1598..2e5328f24 100644 --- a/website/blog/2026-04-06-aks-config-scheduler-profiles-preview/index.md +++ b/website/blog/2026-04-06-aks-config-scheduler-profiles-preview/index.md @@ -49,7 +49,9 @@ Today, the default scheduler on AKS lacks the flexibility for users to change wh ## Configurable Scheduler Profiles on AKS -[Configurable Scheduler Profiles on AKS][concepts-scheduler-configuration] allow customers to benefit from the extensibility of the [scheduling framework][scheduling-framework-interfaces] while reducing the operational overhead of adopting a second scheduler or defining a custom scheduler. Configurable Scheduler Profiles use a Custom Resource Definition (CRD) that lets users define custom scheduler profiles with their own scheduling logic. A dedicated controller continuously reconciles these user-defined configurations with the underlying kube-scheduler deployment, validating changes and applying them transparently. If a new configuration causes the scheduler to become unhealthy, the controller automatically reverts to the last known good state to ensure cluster stability. +[Configurable Scheduler Profiles on AKS][concepts-scheduler-configuration] let you benefit from the extensibility of the [scheduling framework][scheduling-framework-interfaces] while reducing the operational overhead of adopting a second scheduler or defining a custom scheduler. + +Configurable Scheduler Profiles use a Custom Resource Definition (CRD) that lets you define custom scheduler profiles with their own scheduling logic. A dedicated controller continuously reconciles these user-defined configurations with the underlying kube-scheduler deployment, validating changes and applying them transparently. If a new configuration causes the scheduler to become unhealthy, the controller automatically reverts to the last known good state to ensure cluster stability. ![Architecture diagram showing how Configurable Scheduler Profiles use a CRD and controller to reconcile user-defined profiles with the kube-scheduler deployment](./config-scheduler-profiles.png) From 350f6e5bb97dc42469cb69c4da3b5b32e1e83ead Mon Sep 17 00:00:00 2001 From: colinmixonn <109253437+colinmixonn@users.noreply.github.com> Date: Fri, 3 Apr 2026 11:15:17 -0700 Subject: [PATCH 159/164] Enhance clarity on AKS scheduling and resource utilization Revised content for clarity and improved flow regarding Kubernetes resource utilization and scheduling strategies. --- .../index.md | 24 ++++++++++++------- 1 file changed, 15 insertions(+), 9 deletions(-) diff --git a/website/blog/2026-04-06-aks-config-scheduler-profiles-preview/index.md b/website/blog/2026-04-06-aks-config-scheduler-profiles-preview/index.md index 2e5328f24..957db6f2e 100644 --- a/website/blog/2026-04-06-aks-config-scheduler-profiles-preview/index.md +++ b/website/blog/2026-04-06-aks-config-scheduler-profiles-preview/index.md @@ -6,7 +6,7 @@ authors: [colin-mixon] tags: [ai, performance, scheduler, best-practices, cost] --- -In 2025, Datadog found most Kubernetes containers use less than [25% of their requested CPU][datadog-state-of-containers], and in 2023, Weights and Biases found that nearly a third of GPU users [average less than 15% utilization][wb-gpu-utilization]. Underutilized resources materially contribute to increased infrastructure cost and this data signals that most users can greatly improve resource utilization. While there are many factors that impact node utilization, as a core component of the Kubernetes control plane, the kube-scheduler plays a critical role in node utilization. +Your clusters are likely running well below capacity and underutilized resources materially contribute to increased infrastructure cost. In 2025, Datadog found most Kubernetes containers use less than [25% of their requested CPU][datadog-state-of-containers], and in 2023, Weights and Biases found that nearly a third of GPU users [averaged less than 15% utilization][wb-gpu-utilization]. While there are many factors that impact node utilization, as a core component of the Kubernetes control plane, the kube-scheduler plays a critical role in node utilization. [Configurable Scheduler Profiles][concepts-scheduler-configuration] on Azure Kubernetes Service (AKS) let you configure your own scheduling logic: enable specific plugins, adjust plugin priorities, and tune parameter weights. **The result: higher node density, better GPU utilization, and lower infrastructure costs.** @@ -79,11 +79,14 @@ This change in distribution shape enables downstream efficiencies: improved cont ### Increase AKS CPU utilization -`RequestedToCapacityRatio` scores nodes based on the ratio of requested resources to total node capacity after the pod is _hypothetically_ placed. This strategy enables more fine-grained bin‑packing by allowing operators to define an ideal utilization curve for their nodes rather than simply preferring the most or least utilized nodes. `PodTopologySpread` is disabled in this profile because bin-packing and zone-spreading are opposing goals and the scheduling logic may prioritize pod spreading. If you need both high utilization _and_ zone resilience, define a new profile to achieve both goals. +Using `RequestedToCapacityRatio`, this bin packing profile is configured to favor nodes within a utilization band of 50-85%, avoiding empty nodes, and severely deprioritizing nearly full nodes at 90% utilization or more, to limit oversaturated nodes. [Configure node bin-packing][configure-requested-to-capacity] using the `RequestedToCapacityRatio` strategy to improve utilization and reduce infrastructure costs. -By shaping the scoring curve to target a range of 50-85% CPU utilization, operators can increase pod density on provisioned nodes while preserving headroom for bursts, background processes, and system components in CPU-based workloads. [Configure node bin-packing][configure-requested-to-capacity] using the `RequestedToCapacityRatio` strategy to improve utilization and reduce infrastructure costs. +1. `RequestedToCapacityRatio` scores nodes based on the ratio of requested resources to total node capacity after the pod is _hypothetically_ placed. This strategy enables more fine-grained bin‑packing by allowing operators to define an ideal utilization curve for their nodes rather than simply preferring the most or least utilized nodes. +2. `PodTopologySpread` is disabled in this profile because bin-packing and zone-spreading are opposing goals and the scheduling logic may prioritize pod spreading. If you need both high utilization _and_ zone resilience, define a new profile to achieve both goals. -**This bin packing profile is configured to favor nodes within a utilization band of 50-85%, avoiding empty nodes, and severely deprioritizing nearly full nodes at 90% utilization or more, to limit oversaturated nodes. Given this level of configuration detail, `RequestedToCapacityRatio` is the recommended scoring strategy for node bin‑packing on AKS for production clusters.** +By shaping the scoring curve to target a range of 50-85% CPU utilization, users can increase pod density on provisioned nodes while preserving headroom for bursts, background processes, and system components in CPU-bound workloads. + +**Given this level of configuration detail, `RequestedToCapacityRatio` is the recommended scoring strategy for node bin‑packing on AKS for production clusters.** :::note Scoring strategy can also be used for GPU by changing the target resource. Adjust resources, resource weights, utilization thresholds, and plugin parameters to match your VM SKUs, workload patterns, and cluster topology. @@ -136,9 +139,13 @@ spec: ### Increase AKS GPU utilization -`MostAllocated` scores nodes based on its current resource utilization, favoring nodes that are already heavily used. `RequestedToCapacityRatio`, on the other hand, scores nodes based on both the resource requests and the remaining node capacity, making `MostAllocated` more suitable for an aggressive cost-optimization scheduling strategy since it intentionally ignores capacity. When paired with MostAllocated, `NodeResourcesBalancedAllocation` complements the behavior because it encourages pod placement on nodes with user-defined proportional utilization, helping reduce bottlenecks caused by asymmetric resource pressure. `PodTopologySpread` is disabled in this profile because bin-packing and zone-spreading are opposing goals. Enabling both can result in spreading over consolidation, weakening the intended packing behavior. +When plugins `MostAllocated` and `NodeResourcesBalancedAllocation` are combined, the scheduler favors GPU‑bound nodes with balanced CPU and memory usage over nodes with large amounts of unused memory or fragmented resources. This results in more balanced GPU placement and fewer partially utilized nodes. [Configure node bin-packing][configure-most-allocated] using the MostAllocated strategy to improve utilization and reduce infrastructure costs. + +1. `MostAllocated` scores nodes based on its current resource utilization, favoring nodes that are already heavily used. +2. `RequestedToCapacityRatio`, on the other hand, scores nodes based on both the resource requests and the remaining node capacity, making `MostAllocated` more suitable for an aggressive cost-optimization scheduling strategy since it intentionally ignores capacity. +3. `PodTopologySpread` is disabled in this profile because bin-packing and zone-spreading are opposing goals. Enabling both can result in spreading over consolidation, weakening the intended packing behavior. -When combined, these plugins favor GPU‑bound nodes with balanced CPU and memory usage over nodes with large amounts of unused memory or fragmented resources. This results in more efficient GPU placement and fewer partially utilized nodes. [Configure node bin-packing][configure-most-allocated] using the MostAllocated strategy to improve utilization and reduce infrastructure costs. +When paired with MostAllocated, `NodeResourcesBalancedAllocation` complements the behavior because it encourages pod placement on nodes with user-defined proportional utilization, helping reduce bottlenecks caused by asymmetric resource pressure. **This scheduler configuration maximizes GPU utilization by consolidating smaller jobs onto fewer nodes, reducing idle accelerator capacity while maintaining reasonable CPU and memory balance.** @@ -220,13 +227,12 @@ spec: - Review autoscaler outcomes, looking for fewer scale‑ups during normal load and more decisive scale‑downs after demand drops. - Measure cost metrics, such as reduced idle costs when you use the [Cost Analysis add-on][aks-cost-analysis-add-on]. -## Next steps: Optimize Azure resources and test Configurable Scheduler Profiles on AKS +## Next steps: Optimize resources with Configurable Scheduler Profiles on AKS Configurable Scheduler Profiles give you direct control over pod placement. With these scheduling plugins, your workloads make full use of available GPU capacity, reduce idle costs, and avoid costly overprovisioning. - For additional guidance and best practices, see [kube-scheduler best practices][best-practices-advanced-scheduler] - Increase node utilization using [Configurable Scheduler Profiles][node-bin-packing-configurations] -- If additional capabilities or ML frameworks are needed to schedule and queue batch workloads, you can [install and configure Kueue on AKS][kueue-overview] to ensure efficient, policy-driven scheduling in AKS clusters. -- To schedule and queue batch workloads, [install and configure Kueue on AKS][kueue-overview] for efficient, policy-driven scheduling. +- To schedule and queue batch workloads, [install and configure Kueue on AKS][kueue-overview]. [concepts-scheduler-configuration]: https://learn.microsoft.com/azure/aks/concepts-scheduler-configuration [kueue-overview]: https://learn.microsoft.com/azure/aks/kueue-overview From 8489129e8b445474dfcb688193b51ee061275364 Mon Sep 17 00:00:00 2001 From: colinmixonn <109253437+colinmixonn@users.noreply.github.com> Date: Fri, 3 Apr 2026 11:28:55 -0700 Subject: [PATCH 160/164] Revise AKS Configurable Scheduler Profiles documentation Updated descriptions and explanations for configurable scheduler profiles and GPU utilization strategies in AKS documentation. --- .../index.md | 11 ++++++----- 1 file changed, 6 insertions(+), 5 deletions(-) diff --git a/website/blog/2026-04-06-aks-config-scheduler-profiles-preview/index.md b/website/blog/2026-04-06-aks-config-scheduler-profiles-preview/index.md index 957db6f2e..80dfb2331 100644 --- a/website/blog/2026-04-06-aks-config-scheduler-profiles-preview/index.md +++ b/website/blog/2026-04-06-aks-config-scheduler-profiles-preview/index.md @@ -49,7 +49,7 @@ Today, the default scheduler on AKS lacks the flexibility for users to change wh ## Configurable Scheduler Profiles on AKS -[Configurable Scheduler Profiles on AKS][concepts-scheduler-configuration] let you benefit from the extensibility of the [scheduling framework][scheduling-framework-interfaces] while reducing the operational overhead of adopting a second scheduler or defining a custom scheduler. +[Configurable Scheduler Profiles on AKS][concepts-scheduler-configuration] let you benefit from the extensibility of the [scheduling framework][scheduling-framework-interfaces] while reducing the operational overhead of adopting a second scheduler or defining a custom scheduler. Configurable Scheduler Profiles use a Custom Resource Definition (CRD) that lets you define custom scheduler profiles with their own scheduling logic. A dedicated controller continuously reconciles these user-defined configurations with the underlying kube-scheduler deployment, validating changes and applying them transparently. If a new configuration causes the scheduler to become unhealthy, the controller automatically reverts to the last known good state to ensure cluster stability. @@ -139,13 +139,13 @@ spec: ### Increase AKS GPU utilization -When plugins `MostAllocated` and `NodeResourcesBalancedAllocation` are combined, the scheduler favors GPU‑bound nodes with balanced CPU and memory usage over nodes with large amounts of unused memory or fragmented resources. This results in more balanced GPU placement and fewer partially utilized nodes. [Configure node bin-packing][configure-most-allocated] using the MostAllocated strategy to improve utilization and reduce infrastructure costs. +When `MostAllocated` and `NodeResourcesBalancedAllocation` are combined, the scheduler favors GPU‑bound nodes with balanced CPU and memory usage over nodes with large amounts of unused memory or fragmented resources. This approach reduces fragmented GPU capacity and fewer underutilized secondary resources. [Configure node bin-packing][configure-most-allocated] using the MostAllocated strategy to improve utilization and reduce infrastructure costs. -1. `MostAllocated` scores nodes based on its current resource utilization, favoring nodes that are already heavily used. -2. `RequestedToCapacityRatio`, on the other hand, scores nodes based on both the resource requests and the remaining node capacity, making `MostAllocated` more suitable for an aggressive cost-optimization scheduling strategy since it intentionally ignores capacity. +1. `MostAllocated` scores nodes based on its current resource utilization, favoring nodes that are already heavily used for the specified resources. +2. `RequestedToCapacityRatio`, lets you define a scoring curve so you can explicitly control preferred utilization ranges and scores nodes based resource requests relative to the remaining node capacity. This makes `MostAllocated` more aggressive for consolidation but gives you less explicit control over headroom. 3. `PodTopologySpread` is disabled in this profile because bin-packing and zone-spreading are opposing goals. Enabling both can result in spreading over consolidation, weakening the intended packing behavior. -When paired with MostAllocated, `NodeResourcesBalancedAllocation` complements the behavior because it encourages pod placement on nodes with user-defined proportional utilization, helping reduce bottlenecks caused by asymmetric resource pressure. +`NodeResourcesBalancedAllocation` complements `MostAllocated` because it prefers nodes whose CPU and memory utilization stay proportionally balanced, helping reduce bottlenecks caused by asymmetric resource pressure. **This scheduler configuration maximizes GPU utilization by consolidating smaller jobs onto fewer nodes, reducing idle accelerator capacity while maintaining reasonable CPU and memory balance.** @@ -230,6 +230,7 @@ spec: ## Next steps: Optimize resources with Configurable Scheduler Profiles on AKS Configurable Scheduler Profiles give you direct control over pod placement. With these scheduling plugins, your workloads make full use of available GPU capacity, reduce idle costs, and avoid costly overprovisioning. + - For additional guidance and best practices, see [kube-scheduler best practices][best-practices-advanced-scheduler] - Increase node utilization using [Configurable Scheduler Profiles][node-bin-packing-configurations] - To schedule and queue batch workloads, [install and configure Kueue on AKS][kueue-overview]. From 67a24cbb3c29ab0d7f326bfa6417079120c10673 Mon Sep 17 00:00:00 2001 From: colinmixonn <109253437+colinmixonn@users.noreply.github.com> Date: Fri, 3 Apr 2026 13:07:44 -0700 Subject: [PATCH 161/164] Apply suggestions from code review Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> --- .../2026-04-06-aks-config-scheduler-profiles-preview/index.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/website/blog/2026-04-06-aks-config-scheduler-profiles-preview/index.md b/website/blog/2026-04-06-aks-config-scheduler-profiles-preview/index.md index 80dfb2331..239be1ddc 100644 --- a/website/blog/2026-04-06-aks-config-scheduler-profiles-preview/index.md +++ b/website/blog/2026-04-06-aks-config-scheduler-profiles-preview/index.md @@ -139,10 +139,10 @@ spec: ### Increase AKS GPU utilization -When `MostAllocated` and `NodeResourcesBalancedAllocation` are combined, the scheduler favors GPU‑bound nodes with balanced CPU and memory usage over nodes with large amounts of unused memory or fragmented resources. This approach reduces fragmented GPU capacity and fewer underutilized secondary resources. [Configure node bin-packing][configure-most-allocated] using the MostAllocated strategy to improve utilization and reduce infrastructure costs. +When `MostAllocated` and `NodeResourcesBalancedAllocation` are combined, the scheduler favors GPU‑bound nodes with balanced CPU and memory usage over nodes with large amounts of unused memory or fragmented resources. This approach reduces fragmented GPU capacity and results in fewer underutilized secondary resources. [Configure node bin-packing][configure-most-allocated] using the MostAllocated strategy to improve utilization and reduce infrastructure costs. 1. `MostAllocated` scores nodes based on its current resource utilization, favoring nodes that are already heavily used for the specified resources. -2. `RequestedToCapacityRatio`, lets you define a scoring curve so you can explicitly control preferred utilization ranges and scores nodes based resource requests relative to the remaining node capacity. This makes `MostAllocated` more aggressive for consolidation but gives you less explicit control over headroom. +2. `RequestedToCapacityRatio`, lets you define a scoring curve so you can explicitly control preferred utilization ranges and scores nodes based on resource requests relative to the remaining node capacity. This makes `MostAllocated` more aggressive for consolidation but gives you less explicit control over headroom. 3. `PodTopologySpread` is disabled in this profile because bin-packing and zone-spreading are opposing goals. Enabling both can result in spreading over consolidation, weakening the intended packing behavior. `NodeResourcesBalancedAllocation` complements `MostAllocated` because it prefers nodes whose CPU and memory utilization stay proportionally balanced, helping reduce bottlenecks caused by asymmetric resource pressure. From 1ce29206608a8736249cb923b5e68b169c72067d Mon Sep 17 00:00:00 2001 From: colinmixonn <109253437+colinmixonn@users.noreply.github.com> Date: Fri, 3 Apr 2026 13:13:55 -0700 Subject: [PATCH 162/164] Apply suggestions from code review Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> --- .../2026-04-06-aks-config-scheduler-profiles-preview/index.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/website/blog/2026-04-06-aks-config-scheduler-profiles-preview/index.md b/website/blog/2026-04-06-aks-config-scheduler-profiles-preview/index.md index 239be1ddc..219f98555 100644 --- a/website/blog/2026-04-06-aks-config-scheduler-profiles-preview/index.md +++ b/website/blog/2026-04-06-aks-config-scheduler-profiles-preview/index.md @@ -69,7 +69,7 @@ While this experiment uses intentionally simple, CPU-bound containers to isolate ![Table showing increased node utilization with the node bin packing scheduler profiles versus the pod distribution using the default scheduler for GPUs across multiple scheduling iterations](./default-config-scheduler-comparison-gpu.png) -This change in distribution shape enables downstream efficiencies: improved control for platform engineers, efficient resource usage, and cost optimization that are difficult to achieve when pods are evenly spread. **Each profile expresses a distinct scheduling intent. The next two sections detail how the scoring strategies, MostAllocated and RequestedToCapacityRatio achieve these outcomes.** +This change in distribution shape enables downstream efficiencies: improved control for platform engineers, efficient resource usage, and cost optimization that are difficult to achieve when pods are evenly spread. **Each profile expresses a distinct scheduling intent. The next two sections detail how the scoring strategies, MostAllocated and RequestedToCapacityRatio, achieve these outcomes.** | Scheduler | Scoring strategy | Scheduling intent | Operator benefits | | ------------------------------ | -------------------------------------- | ------------------------------------ | ------------------------------------------------------------------------------------------------------------------ | From 40a5a52700ca9324c0d299b3ce7a2636dfe9080d Mon Sep 17 00:00:00 2001 From: colinmixonn <109253437+colinmixonn@users.noreply.github.com> Date: Mon, 6 Apr 2026 14:52:52 -0700 Subject: [PATCH 163/164] Enhance clarity on AKS Configurable Scheduler Profiles Clarified the limitations of the default Kubernetes scheduler and detailed the functionality of Configurable Scheduler Profiles on AKS. Updated the description of how the scheduling behavior can be adjusted for better resource utilization. --- .../index.md | 13 +++++++++---- 1 file changed, 9 insertions(+), 4 deletions(-) diff --git a/website/blog/2026-04-06-aks-config-scheduler-profiles-preview/index.md b/website/blog/2026-04-06-aks-config-scheduler-profiles-preview/index.md index 219f98555..9cf15a95d 100644 --- a/website/blog/2026-04-06-aks-config-scheduler-profiles-preview/index.md +++ b/website/blog/2026-04-06-aks-config-scheduler-profiles-preview/index.md @@ -45,13 +45,18 @@ For more on how the Kubernetes scheduler works, see the technical blog post from The default scheduler is primarily designed for general-purpose workloads. It prioritizes nodes with the most available resources using the _LeastAllocated_ scoring strategy. This strategy spreads pods across nodes, even when they could safely be packed more densely. While this works well for many services, the default scheduling criteria, and their fixed priority order, are not suitable for workloads that demand optimizing GPU and CPU utilization. In these scenarios, spreading pods across nodes can lead to fragmented resources, underutilized GPUs, and increased infrastructure cost. -Today, the default scheduler on AKS lacks the flexibility for users to change which criteria should be prioritized, or ignored, in the scheduling cycle on a per cluster basis. This rigidity often forces users to either accept suboptimal placement or manage a separate custom scheduler, both of which increase operational complexity. Starting with Kubernetes v1.33, AKS introduces [Configurable Scheduler Profiles][concepts-scheduler-configuration] - an AKS-managed CRD - that exposes the upstream scheduling framework without maintaining a separate scheduler. Now, users can adjust the `NodeResourcesFit` plugin from the default configuration to favor nodes with higher utilization to achieve more efficient bin‑packing and reduce infrastructure cost. +Today, the default scheduler on AKS lacks the flexibility for users to change which criteria should be prioritized, or ignored, in the scheduling cycle because the default scheduler is not accessible to users. This rigidity often forces users to either accept suboptimal placement or manage a separate custom scheduler, both of which increase operational complexity. Starting with Kubernetes v1.33, AKS introduces [Configurable Scheduler Profiles][concepts-scheduler-configuration] - an AKS-managed CRD - that exposes the upstream scheduling framework without maintaining a separate scheduler. Now, users can adjust the `NodeResourcesFit` plugin from the default configuration to favor nodes with higher utilization to achieve more efficient bin‑packing and reduce infrastructure cost. ## Configurable Scheduler Profiles on AKS -[Configurable Scheduler Profiles on AKS][concepts-scheduler-configuration] let you benefit from the extensibility of the [scheduling framework][scheduling-framework-interfaces] while reducing the operational overhead of adopting a second scheduler or defining a custom scheduler. +[Configurable Scheduler Profiles on AKS][concepts-scheduler-configuration] allows users to change the default scheduler behavior using the extensibility of the [scheduling framework][scheduling-framework-interfaces], without the operational overhead of adopting a second scheduler or defining a custom scheduler. You express the intent declaratively, while AKS safely applies and manages the resulting scheduler behavior using a Custom Resource Definition (CRD). -Configurable Scheduler Profiles use a Custom Resource Definition (CRD) that lets you define custom scheduler profiles with their own scheduling logic. A dedicated controller continuously reconciles these user-defined configurations with the underlying kube-scheduler deployment, validating changes and applying them transparently. If a new configuration causes the scheduler to become unhealthy, the controller automatically reverts to the last known good state to ensure cluster stability. +1. AKS begins with the default scheduling configuration. +2. You declare your desired scheduling behavior using a Configurable Scheduler Profile applied as a CRD. +3. The controller overlays the user-defined configuration on top of the base scheduler configuration. +4. The controller produces and maintains a reconciled scheduler ConfigMap representing the combined configuration. +5. The kube-scheduler deployment consumes the controller-managed ConfigMap and applies the updated scheduling behavior. +6. If the scheduler becomes unhealthy, the controller automatically rolls back to the last known good configuration. ![Architecture diagram showing how Configurable Scheduler Profiles use a CRD and controller to reconcile user-defined profiles with the kube-scheduler deployment](./config-scheduler-profiles.png) @@ -69,7 +74,7 @@ While this experiment uses intentionally simple, CPU-bound containers to isolate ![Table showing increased node utilization with the node bin packing scheduler profiles versus the pod distribution using the default scheduler for GPUs across multiple scheduling iterations](./default-config-scheduler-comparison-gpu.png) -This change in distribution shape enables downstream efficiencies: improved control for platform engineers, efficient resource usage, and cost optimization that are difficult to achieve when pods are evenly spread. **Each profile expresses a distinct scheduling intent. The next two sections detail how the scoring strategies, MostAllocated and RequestedToCapacityRatio, achieve these outcomes.** +This change in distribution shape enables downstream efficiencies: improved control for platform engineers, efficient resource usage, and cost optimization that are difficult to achieve when pods are evenly spread. **Configurable Scheduler has many use cases and each profile expresses a distinct scheduling intent. The next two sections detail how the scoring strategies, MostAllocated and RequestedToCapacityRatio, are configured to achieve increased utilization outcomes.** | Scheduler | Scoring strategy | Scheduling intent | Operator benefits | | ------------------------------ | -------------------------------------- | ------------------------------------ | ------------------------------------------------------------------------------------------------------------------ | From 1687b82852fe487ddf981199cea569ac0505216f Mon Sep 17 00:00:00 2001 From: colinmixonn <109253437+colinmixonn@users.noreply.github.com> Date: Wed, 8 Apr 2026 11:45:09 -0700 Subject: [PATCH 164/164] Apply suggestions from code review Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> --- .../2026-04-06-aks-config-scheduler-profiles-preview/index.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/website/blog/2026-04-06-aks-config-scheduler-profiles-preview/index.md b/website/blog/2026-04-06-aks-config-scheduler-profiles-preview/index.md index 9cf15a95d..b32fd8e9f 100644 --- a/website/blog/2026-04-06-aks-config-scheduler-profiles-preview/index.md +++ b/website/blog/2026-04-06-aks-config-scheduler-profiles-preview/index.md @@ -49,7 +49,7 @@ Today, the default scheduler on AKS lacks the flexibility for users to change wh ## Configurable Scheduler Profiles on AKS -[Configurable Scheduler Profiles on AKS][concepts-scheduler-configuration] allows users to change the default scheduler behavior using the extensibility of the [scheduling framework][scheduling-framework-interfaces], without the operational overhead of adopting a second scheduler or defining a custom scheduler. You express the intent declaratively, while AKS safely applies and manages the resulting scheduler behavior using a Custom Resource Definition (CRD). +[Configurable Scheduler Profiles on AKS][concepts-scheduler-configuration] allow users to change the default scheduler behavior using the extensibility of the [scheduling framework][scheduling-framework-interfaces], without the operational overhead of adopting a second scheduler or defining a custom scheduler. You express the intent declaratively, while AKS safely applies and manages the resulting scheduler behavior using a Custom Resource Definition (CRD). 1. AKS begins with the default scheduling configuration. 2. You declare your desired scheduling behavior using a Configurable Scheduler Profile applied as a CRD.