Workload partitioning is a Technology Preview feature only. Technology Preview features are not supported with Red Hat production service level agreements (SLAs) and might not be functionally complete. Red Hat does not recommend using them in production. These features provide early access to upcoming product features, enabling customers to test functionality and provide feedback during the development process.

For more information about the support scope of Red Hat Technology Preview features, see Technology Preview Features Support Scope.

In resource-constrained environments, you can use workload partitioning to isolate OpenShift Container Platform services, cluster management workloads, and infrastructure pods to run on a reserved set of CPUs.

The minimum number of reserved CPUs required for the cluster management is four CPU Hyper-Threads (HTs). With workload partitioning, you annotate the set of cluster management pods and a set of typical add-on Operators for inclusion in the cluster management workload partition. These pods operate normally within the minimum size CPU configuration. Additional Operators or workloads outside of the set of minimum cluster management pods require additional CPUs to be added to the workload partition.

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

The following changes are required for workload partitioning:

  1. In the install-config.yaml file, add the additional field: cpuPartitioningMode.

    apiVersion: v1
    baseDomain: devcluster.openshift.com
    cpuPartitioningMode: AllNodes (1)
      - architecture: amd64
        hyperthreading: Enabled
        name: worker
        platform: {}
        replicas: 3
      architecture: amd64
      hyperthreading: Enabled
      name: master
      platform: {}
      replicas: 3
    1 Sets up a cluster for CPU partitioning at install time. The default value is None.

    Workload partitioning can only be enabled during cluster installation. You cannot disable workload partitioning postinstallation.

  2. In the performance profile, specify the isolated and reserved CPUs.

    Recommended performance profile configuration
    apiVersion: performance.openshift.io/v2
    kind: PerformanceProfile
      name: openshift-node-performance-profile
      - "rcupdate.rcu_normal_after_boot=0"
      - "efi=runtime"
      - "module_blacklist=irdma"
        isolated: 2-51,54-103
        reserved: 0-1,52-53
        defaultHugepagesSize: 1G
          - count: 32
            size: 1G
            node: 0
        pools.operator.machineconfiguration.openshift.io/master: ""
        node-role.kubernetes.io/master: ''
        topologyPolicy: "restricted"
        enabled: true
        realTime: true
        highPowerConsumption: false
        perPodPowerManagement: false
    Table 1. PerformanceProfile CR options for single-node OpenShift clusters
    PerformanceProfile CR field Description


    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


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


    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.


    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.


    • 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)


    Set enabled to true to use the realtime kernel.


    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.

Workload partitioning introduces an extended management.workload.openshift.io/cores resource type for platform pods. kubelet advertises the resources and CPU requests by pods allocated to the pool within the corresponding resource. 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.

Additional resources
  • For the recommended workload partitioning configuration for single-node OpenShift clusters, see Workload partitioning.