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Chapter 18. Workload partitioning on single-node OpenShift
In resource-constrained environments, such as single-node OpenShift deployments, it is advantageous to reserve most of the CPU resources for your own workloads and configure OpenShift Container Platform to run on a fixed number of CPUs within the host. In these environments, management workloads, including the control plane, need to be configured to use fewer resources than they might by default in normal clusters. You can isolate the OpenShift Container Platform services, cluster management workloads, and infrastructure pods to run on a reserved set of CPUs.
When you use workload partitioning, the CPU resources used by OpenShift Container Platform for cluster management are isolated to a partitioned set of CPU resources on a single-node cluster. This partitioning isolates cluster management functions to the defined number of CPUs. All cluster management functions operate solely on that cpuset configuration.
The minimum number of reserved CPUs required for the management partition for a single-node cluster is four CPU Hyper threads (HTs). The set of pods that make up the baseline OpenShift Container Platform installation and a set of typical add-on Operators are annotated for inclusion in the management workload partition. These pods operate normally within the minimum size cpuset configuration. Inclusion of Operators or workloads outside of the set of accepted management pods requires additional CPU HTs to be added to that partition.
Workload partitioning isolates the user workloads away from the platform workloads using the normal scheduling capabilities of Kubernetes to manage the number of pods that can be placed onto those cores, and avoids mixing cluster management workloads and user workloads.
When using workload partitioning, you must install the Performance Addon Operator and apply the performance profile:
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Workload partitioning pins the OpenShift Container Platform infrastructure pods to a defined
cpusetconfiguration. -
The Performance Addon Operator performance profile pins the systemd services to a defined
cpusetconfiguration. -
This
cpusetconfiguration must match.
Workload partitioning introduces a new extended resource of <workload-type>.workload.openshift.io/cores for each defined CPU pool, or workload-type. Kubelet advertises these new resources and CPU requests by pods allocated to the pool are accounted for within the corresponding resource rather than the typical cpu resource. When workload partitioning is enabled, the <workload-type>.workload.openshift.io/cores resource allows access to the CPU capacity of the host, not just the default CPU pool.
18.1. Maximizing CPU allocation with workload partitioning
During single-node OpenShift cluster installation, you must enable workload partitioning. This limits the cores allowed to run platform services, maximizing the CPU core for application payloads.
You can enable workload partitioning only during cluster installation. You cannot disable workload partitioning post-installation. However, you can reconfigure workload partitioning by updating the cpu value that you define in the performance profile, and in the related cpuset value in the MachineConfig custom resource (CR).
The base64-encoded CR that enables workload partitioning contains the CPU set that the management workloads are constrained to. Encode host-specific values for
crio.confandkubelet.confin base64. This content must be adjusted to match the CPU set that is specified in the cluster performance profile and must be accurate for the number of cores in the cluster host.apiVersion: machineconfiguration.openshift.io/v1 kind: MachineConfig metadata: labels: machineconfiguration.openshift.io/role: master name: 02-master-workload-partitioning spec: config: ignition: version: 3.2.0 storage: files: - contents: source: data:text/plain;charset=utf-8;base64,W2NyaW8ucnVudGltZS53b3JrbG9hZHMubWFuYWdlbWVudF0KYWN0aXZhdGlvbl9hbm5vdGF0aW9uID0gInRhcmdldC53b3JrbG9hZC5vcGVuc2hpZnQuaW8vbWFuYWdlbWVudCIKYW5ub3RhdGlvbl9wcmVmaXggPSAicmVzb3VyY2VzLndvcmtsb2FkLm9wZW5zaGlmdC5pbyIKcmVzb3VyY2VzID0geyAiY3B1c2hhcmVzIiA9IDAsICJjcHVzZXQiID0gIjAtMSw1Mi01MyIgfQo= mode: 420 overwrite: true path: /etc/crio/crio.conf.d/01-workload-partitioning user: name: root - contents: source: data:text/plain;charset=utf-8;base64,ewogICJtYW5hZ2VtZW50IjogewogICAgImNwdXNldCI6ICIwLTEsNTItNTMiCiAgfQp9Cg== mode: 420 overwrite: true path: /etc/kubernetes/openshift-workload-pinning user: name: rootWhen configured in the cluster host, the contents of
/etc/crio/crio.conf.d/01-workload-partitioningshould look like this:[crio.runtime.workloads.management] activation_annotation = "target.workload.openshift.io/management" annotation_prefix = "resources.workload.openshift.io" [crio.runtime.workloads.management.resources] cpushares = 0 cpuset = "0-1, 52-53" 1- 1
- The
cpusetvalue varies based on the installation.
If Hyper-Threading is enabled, specify both threads for each core. The
cpusetvalue must match the reserved CPUs that you define in thespec.cpu.reservedfield in the performance profile.When configured in the cluster, the contents of
/etc/kubernetes/openshift-workload-pinningshould look like this:{ "management": { "cpuset": "0-1,52-53" 1 } }- 1
- The
cpusetmust match thecpusetvalue in/etc/crio/crio.conf.d/01-workload-partitioning.
