Chapter 17. Workload partitioning in single-node OpenShift

In resource-constrained environments, such as single-node OpenShift deployments, 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 in single-node OpenShift 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 is an overview of the configurations required for workload partitioning:

Workload partitioning introduces an extended <workload-type>.workload.openshift.io/cores resource for each defined CPU pool, or workload type. Kubelet advertises the resources and CPU requests by pods allocated to the pool within the corresponding 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.