Dieser Inhalt ist in der von Ihnen ausgewählten Sprache nicht verfügbar.
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:
-
Workload partitioning pins the OpenShift Container Platform infrastructure pods to a defined
cpuset
configuration. -
The Performance Addon Operator performance profile pins the systemd services to a defined
cpuset
configuration. -
This
cpuset
configuration 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.conf
andkubelet.conf
in 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: root
When configured in the cluster host, the contents of
/etc/crio/crio.conf.d/01-workload-partitioning
should 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
cpuset
value varies based on the installation.
If Hyper-Threading is enabled, specify both threads for each core. The
cpuset
value must match the reserved CPUs that you define in thespec.cpu.reserved
field in the performance profile.When configured in the cluster, the contents of
/etc/kubernetes/openshift-workload-pinning
should look like this:{ "management": { "cpuset": "0-1,52-53" 1 } }
- 1
- The
cpuset
must match thecpuset
value in/etc/crio/crio.conf.d/01-workload-partitioning
.