Placement API is used to select a set of managed clusters in one or multiple
ManagedClusterSets so that the user’s workloads can be deployed to these clusters.
Bind ManagedClusterSet to a namespace
Before creating a
Placement, you need to create a
ManagedClusterSetBinding in a namespace to bind to a
ManagedClusterSet. Then you can create a
Placement in the same namespace to select the clusters in this
ManagedClusterSet. Assume a
ManagedClusterSet is created on the hub cluster as seen in the following examples.
apiVersion: cluster.open-cluster-management.io/v1beta1 kind: ManagedClusterSet metadata: name: prod
You can create a
ManagedClusterSetBinding as follows to bind the
ManagedClusterSet to the default namespace.
apiVersion: cluster.open-cluster-management.io/v1beta1 kind: ManagedClusterSetBinding metadata: name: prod namespace: default spec: clusterSet: prod
You must have the
create permission on resource
managedclusterset/bind to bind the
ManagedClusterSet to a namespace.
Select clusters in ManagedClusterSet
ManagedClusterSetBinding is created in a namespace, you can create a placement in the namespace to define what clusters should be selected in the bound
You can specify
prioritizers to filter and score clusters.
predicates section, you can select clusters by labels or
clusterClaims. For instance, you can select 3 clusters with labels
purpose=test and clusterClaim
platform.open-cluster-management.io=aws as seen in the following examples.
apiVersion: cluster.open-cluster-management.io/v1alpha1 kind: Placement metadata: name: placement1 namespace: default spec: numberOfClusters: 3 clusterSets: - prod predicates: - requiredClusterSelector: labelSelector: matchLabels: purpose: test claimSelector: matchExpressions: - key: platform.open-cluster-management.io operator: In values: - aws
prioritizerPolicy section, you can define the policy of prioritizers. For instance, you can select 2 clusters with the largest memory available and pin the placementdecisions as seen in the following examples.
apiVersion: cluster.open-cluster-management.io/v1alpha1 kind: Placement metadata: name: placement1 namespace: default spec: numberOfClusters: 2 prioritizerPolicy: mode: Exact configurations: - name: ResourceAllocatableMemory - name: Steady weight: 3
""is Additive by default.
Additivemode, any prioritizer not explicitly enumerated is enabled in its default Configurations, in which Steady and Balance prioritizers have the weight of 1 while other prioritizers have the weight of 0. Additive doesn’t require configuring all prioritizers. The default Configurations may change in the future, and additional prioritization will happen.
Exactmode, any prioritizer not explicitly enumerated is weighted as zero. Exact requires knowing the full set of prioritizers you want, but avoids behavior changes between releases.
configurationsrepresents the configuration of prioritizers.
nameis the name of a prioritizer. Below are the valid names:
- Balance: balance the decisions among the clusters.
- Steady: ensure the existing decision is stabilized.
- ResourceAllocatableCPU & ResourceAllocatableMemory: sort clusters based on the allocatable.
weightdefines the weight of prioritizer. The value must be ranged in [0,10]. Each prioritizer will calculate an integer score of a cluster in the range of [-100, 100]. The final score of a cluster will be sum(weight * prioritizer_score). A higher weight indicates that the prioritizer weights more in the cluster selection, while 0 weight indicate thats the prioritizer is disabled.
A slice of
PlacementDecision will be created by placement controller in the same namespace, each with a label of
PlacementDecision contains the results of the cluster selection as seen in the following examples.
apiVersion: cluster.open-cluster-management.io/v1alpha1 kind: PlacementDecision metadata: labels: cluster.open-cluster-management.io/placement: placement1 name: placement1-decision-1 namespace: default spec: decisions: - clusterName: cluster1 - clusterName: cluster2 - clusterName: cluster3
PlacementDecision can be consumed by another operand to decide how the workload should be placed in multiple clusters.
In addition to selecting cluster by predicates, we are still working on other advanced features including