1. Documentation
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Workload partitioning

To prevent platform processes from interrupting your applications, configure workload partitioning. This isolates OKD services and infrastructure pods to a reserved set of CPUs, ensuring that the remaining compute resources are available exclusively for your customer workloads.

The minimum number of reserved CPUs required for the cluster management is four CPU Hyper-Threads (HTs).

In the context of enabling workload partitioning and managing CPU resources effectively, the cluster might not permit incorrectly configured nodes to join the cluster through a node admission webhook. When the workload partitioning feature is enabled, the machine config pools for control plane nodes and compute nodes get supplied with configurations for nodes to use. Adding new nodes to these pools ensures the pools correctly get configured before joining the cluster.

Currently, nodes must have uniform configurations per machine config pool to ensure that correct CPU affinity is set across all nodes within that pool. After admission, nodes within the cluster identify themselves as supporting a new resource type called management.workload.openshift.io/cores and accurately report their CPU capacity. Workload partitioning can be enabled during cluster installation only by adding the additional field cpuPartitioningMode to the install-config.yaml file.

When workload partitioning is enabled, the management.workload.openshift.io/cores resource allows the scheduler to correctly assign pods based on the cpushares capacity of the host, not just the default cpuset. This ensures more precise allocation of resources for workload partitioning scenarios.

Workload partitioning ensures that CPU requests and limits specified in the pod’s configuration are respected. In OKD 4.16 or later, accurate CPU usage limits are set for platform pods through CPU partitioning. As workload partitioning uses the custom resource type of management.workload.openshift.io/cores, the values for requests and limits are the same due to a requirement by Kubernetes for extended resources. However, the annotations modified by workload partitioning correctly reflect the desired limits.

Extended resources cannot be overcommitted, so request and limit must be equal if both are present in a container spec.

Enabling workload partitioning

To partition cluster management pods into a specified CPU affinity, enable workload partitioning. This configuration ensures that management pods operate within the reserved CPU limits defined in your Performance Profile, preventing them from consuming resources intended for customer workloads.

Consider additional post-installation Operators that use workload partitioning when calculating how many reserved CPU cores to set aside for the platform.

Workload partitioning isolates user workloads from platform workloads using standard Kubernetes scheduling capabilities.

You can enable workload partitioning only during cluster installation. You cannot disable workload partitioning post-installation. However, you can change the CPU configuration for reserved and isolated CPUs post-installation.

The procedure demonstrates enabling workload partitioning cluster-wide.

Procedure

  • In the install-config.yaml file, add the additional field cpuPartitioningMode and set it to AllNodes.

    apiVersion: v1
baseDomain: devcluster.openshift.com
cpuPartitioningMode: AllNodes
compute:
  - architecture: amd64
    hyperthreading: Enabled
    name: worker
    platform: {}
    replicas: 3
controlPlane:
  architecture: amd64
  hyperthreading: Enabled
  name: master
  platform: {}
  replicas: 3

    • cpuPartitioningMode: Specifies the cluster to set up for CPU partitioning at install time. The default value is None, which ensures that no CPU partitioning is enabled at install time.

Performance profiles and workload partitioning

To enable workload partitioning, apply a performance profile. This configuration specifies the isolated and reserved CPUs, ensuring that customer workloads run on dedicated cores without interruption from platform processes.

An appropriately configured performance profile specifies the isolated and reserved CPUs. Create a performance profile by using the Performance Profile Creator (PPC) tool.

Sample performance profile configuration
apiVersion: performance.openshift.io/v2
kind: PerformanceProfile
metadata:
  # if you change this name make sure the 'include' line in TunedPerformancePatch.yaml
  # matches this name: include=openshift-node-performance-${PerformanceProfile.metadata.name}
  # Also in file 'validatorCRs/informDuValidator.yaml': 
  # name: 50-performance-${PerformanceProfile.metadata.name}
  name: openshift-node-performance-profile
  annotations:
    ran.openshift.io/reference-configuration: "ran-du.redhat.com"
spec:
  additionalKernelArgs:
    - "rcupdate.rcu_normal_after_boot=0"
    - "efi=runtime"
    - "vfio_pci.enable_sriov=1"
    - "vfio_pci.disable_idle_d3=1"
    - "module_blacklist=irdma"
  cpu:
    isolated: $isolated
    reserved: $reserved
  hugepages:
    defaultHugepagesSize: $defaultHugepagesSize
    pages:
      - size: $size
        count: $count
        node: $node
  machineConfigPoolSelector:
    pools.operator.machineconfiguration.openshift.io/$mcp: ""
  nodeSelector:
    node-role.kubernetes.io/$mcp: ''
  numa:
    topologyPolicy: "restricted"
  # To use the standard (non-realtime) kernel, set enabled to false
  realTimeKernel:
    enabled: true
  workloadHints:
    # WorkloadHints defines the set of upper level flags for different type of workloads.
    # See https://github.com/openshift/cluster-node-tuning-operator/blob/master/docs/performanceprofile/performance_profile.md#workloadhints
    # for detailed descriptions of each item.
    # The configuration below is set for a low latency, performance mode.
    realTime: true
    highPowerConsumption: false
    perPodPowerManagement: false
Table 1. PerformanceProfile CR options for single-node OpenShift clusters
PerformanceProfile CR field Description

metadata.name

Ensure that name matches the following fields set in related GitOps ZTP custom resources (CRs):

  • include=openshift-node-performance-${PerformanceProfile.metadata.name} in TunedPerformancePatch.yaml

  • name: 50-performance-${PerformanceProfile.metadata.name} in validatorCRs/informDuValidator.yaml

spec.additionalKernelArgs

"efi=runtime" Configures UEFI secure boot for the cluster host.

spec.cpu.isolated

Set the isolated CPUs. Ensure all of the Hyper-Threading pairs match.

The reserved and isolated CPU pools must not overlap and together must span all available cores. CPU cores that are not accounted for cause an undefined behaviour in the system.

spec.cpu.reserved

Set the reserved CPUs. When workload partitioning is enabled, system processes, kernel threads, and system container threads are restricted to these CPUs. All CPUs that are not isolated should be reserved.

spec.hugepages.pages

  • Set the number of huge pages (count)

  • Set the huge pages size (size).

  • Set node to the NUMA node where the hugepages are allocated (node)

spec.realTimeKernel

Set enabled to true to use the realtime kernel.

spec.workloadHints

Use workloadHints to define the set of top level flags for different type of workloads. The example configuration configures the cluster for low latency and high performance.
Additional resources