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Descheduler

Users could divide their replicas of a workload into different clusters in terms of available resources of member clusters. However, the scheduler's decisions are influenced by its view of Karmada at that point of time when a new ResourceBinding appears for scheduling. As Karmada multi-clusters are very dynamic and their state changes over time, there may be desire to move already running replicas to some other clusters due to lack of resources for the cluster. This may happen when some nodes of a cluster failed and the cluster does not have enough resource to accommodate their pods or the estimators have some estimation deviation, which is inevitable.

The karmada-descheduler will detect all deployments once in a while, default 2 minutes. In every period, it will find out how many unschedulable replicas a deployment has in target scheduled clusters by calling karmada-scheduler-estimator. Then it will evict them from decreasing spec.clusters and trigger karmada-scheduler to do a 'Scale Schedule' based on the current situation. Note that it will take effect only when the replica scheduling strategy is dynamic divided.

Prerequisites

Karmada has been installed

We can install Karmada by referring to quick-start, or directly run hack/local-up-karmada.sh script which is also used to run our E2E cases.

Member cluster component is ready

Ensure that all member clusters has been joined and their corresponding karmada-scheduler-estimator is installed into karmada-host.

You could check by using the following command:

# check whether the member cluster has been joined
$ kubectl get cluster
NAME       VERSION   MODE   READY   AGE
member1    v1.19.1   Push   True    11m
member2    v1.19.1   Push   True    11m
member3    v1.19.1   Pull   True    5m12s

# check whether the karmada-scheduler-estimator of a member cluster has been working well
$ kubectl --context karmada-host get pod -n karmada-system | grep estimator
karmada-scheduler-estimator-member1-696b54fd56-xt789   1/1     Running   0          77s
karmada-scheduler-estimator-member2-774fb84c5d-md4wt   1/1     Running   0          75s
karmada-scheduler-estimator-member3-5c7d87f4b4-76gv9   1/1     Running   0          72s

If the cluster has not been joined, you could use hack/deploy-agent-and-estimator.sh to deploy both karmada-agent and karmada-scheduler-estimator.
If the cluster has been joined already, you could use hack/deploy-scheduler-estimator.sh to only deploy karmada-scheduler-estimator.

Scheduler option '--enable-scheduler-estimator'

After all member clusters has been joined and estimators are all ready, please specify the option --enable-scheduler-estimator=true to enable scheduler estimator.

# edit the deployment of karmada-scheduler
$ kubectl --context karmada-host edit -n karmada-system deployments.apps karmada-scheduler

And then add the option --enable-scheduler-estimator=true into the command of container karmada-scheduler.

Descheduler has been installed

Ensure that the karmada-descheduler has been installed in to karmada-host.

$ kubectl --context karmada-host get pod -n karmada-system | grep karmada-descheduler
karmada-descheduler-658648d5b-c22qf                    1/1     Running   0          80s

Example

Now let's build a scene where some replicas in a member cluster are not capable to be scheduled due to lack of resources.

First we create a deployment with 3 replicas and divide them into 3 member clusters.

apiVersion: policy.karmada.io/v1alpha1
kind: PropagationPolicy
metadata:
  name: nginx-propagation
spec:
  resourceSelectors:
    - apiVersion: apps/v1
      kind: Deployment
      name: nginx
  placement:
    clusterAffinity:
      clusterNames:
        - member1
        - member2
        - member3
    replicaScheduling:
      replicaDivisionPreference: Weighted
      replicaSchedulingType: Divided
      weightPreference:
        dynamicWeight: AvailableReplicas
---
apiVersion: apps/v1
kind: Deployment
metadata:
  name: nginx
  labels:
    app: nginx
spec:
  replicas: 3
  selector:
    matchLabels:
      app: nginx
  template:
    metadata:
      labels:
        app: nginx
    spec:
      containers:
      - image: nginx
        name: nginx
        resources:
          requests:
            cpu: "2"

It is possible for these 3 replicas to be divided into 3 member clusters averagely, i.e. 1 replica in each cluster. Now we taint all nodes in member1 and evict the replica.

$ kubectl --context member1 cordon member1-control-plane
$ kubectl --context member1 delete pod nginx-68b895fcbd-jgwz6

A new pod will be created and cannot be scheduled by kube-scheduler due to lack of resources.

$ kubectl --context member1 get pod
NAME                     READY   STATUS    RESTARTS   AGE
nginx-68b895fcbd-fccg4   1/1     Pending   0          80s

After about 5 to 7 minutes, the pod in member1 will be evicted and scheduled to other available clusters.

$ kubectl --context member1 get pod
No resources found in default namespace.
# kubectl --context member2 get pod
NAME                     READY   STATUS    RESTARTS   AGE
nginx-68b895fcbd-dgd4x   1/1     Running   0          6m3s
nginx-68b895fcbd-nwgjn   1/1     Running   0          4s

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