Chapter 7. Applying autoscaling to an OpenShift Container Platform cluster

The cluster autoscaler adjusts the size of an OpenShift Container Platform cluster to meet its current deployment needs. It uses declarative, Kubernetes-style arguments to provide infrastructure management that does not rely on objects of a specific cloud provider. The cluster autoscaler has a cluster scope, and is not associated with a particular namespace.

The cluster autoscaler increases the size of the cluster when there are pods that fail to schedule on any of the current worker nodes due to insufficient resources or when another node is necessary to meet deployment needs. The cluster autoscaler does not increase the cluster resources beyond the limits that you specify.

The cluster autoscaler computes the total memory, CPU, and GPU on all nodes the cluster, even though it does not manage the control plane nodes. These values are not single-machine oriented. They are an aggregation of all the resources in the entire cluster. For example, if you set the maximum memory resource limit, the cluster autoscaler includes all the nodes in the cluster when calculating the current memory usage. That calculation is then used to determine if the cluster autoscaler has the capacity to add more worker resources.

Automatic node removal

Every 10 seconds, the cluster autoscaler checks which nodes are unnecessary in the cluster and removes them. The cluster autoscaler considers a node for removal if the following conditions apply:

If the following types of pods are present on a node, the cluster autoscaler will not remove the node:

For example, you set the maximum CPU limit to 64 cores and configure the cluster autoscaler to only create machines that have 8 cores each. If your cluster starts with 30 cores, the cluster autoscaler can add up to 4 more nodes with 32 cores, for a total of 62.

Limitations

If you configure the cluster autoscaler, additional usage restrictions apply:

Interaction with other scheduling features

The horizontal pod autoscaler (HPA) and the cluster autoscaler modify cluster resources in different ways. The HPA changes the deployment’s or replica set’s number of replicas based on the current CPU load. If the load increases, the HPA creates new replicas, regardless of the amount of resources available to the cluster. If there are not enough resources, the cluster autoscaler adds resources so that the HPA-created pods can run. If the load decreases, the HPA stops some replicas. If this action causes some nodes to be underutilized or completely empty, the cluster autoscaler deletes the unnecessary nodes.

The cluster autoscaler takes pod priorities into account. The Pod Priority and Preemption feature enables scheduling pods based on priorities if the cluster does not have enough resources, but the cluster autoscaler ensures that the cluster has resources to run all pods. To honor the intention of both features, the cluster autoscaler includes a priority cutoff function. You can use this cutoff to schedule "best-effort" pods, which do not cause the cluster autoscaler to increase resources but instead run only when spare resources are available.

Pods with priority lower than the cutoff value do not cause the cluster to scale up or prevent the cluster from scaling down. No new nodes are added to run the pods, and nodes running these pods might be deleted to free resources.

apiVersion: "autoscaling.openshift.io/v1"
kind: "ClusterAutoscaler"
metadata:
  name: "default"
spec:
  podPriorityThreshold: -10 1
  resourceLimits:
    maxNodesTotal: 24 2
    cores:
      min: 8 3
      max: 128 4
    memory:
      min: 4 5
      max: 256 6
    gpus:
    - type: <gpu_type> 7
      min: 0 8
      max: 16 9
  logVerbosity: 4 10
  scaleDown: 11
    enabled: true 12
    delayAfterAdd: 10m 13
    delayAfterDelete: 5m 14
    delayAfterFailure: 30s 15
    unneededTime: 5m 16
    utilizationThreshold: "0.4" 17
  expanders: ["Random"] 18

The machine autoscaler adjusts the number of Machines in the compute machine sets that you deploy in an OpenShift Container Platform cluster. You can scale both the default worker compute machine set and any other compute machine sets that you create. The machine autoscaler makes more Machines when the cluster runs out of resources to support more deployments. Any changes to the values in MachineAutoscaler resources, such as the minimum or maximum number of instances, are immediately applied to the compute machine set they target.

apiVersion: "autoscaling.openshift.io/v1beta1"
kind: "MachineAutoscaler"
metadata:
  name: "worker-us-east-1a" 1
  namespace: "openshift-machine-api"
spec:
  minReplicas: 1 2
  maxReplicas: 12 3
  scaleTargetRef: 4
    apiVersion: machine.openshift.io/v1beta1
    kind: MachineSet 5
    name: worker-us-east-1a 6

You can disable an individual machine autoscaler in your cluster or disable autoscaling on the cluster entirely.