Version: Next

Autoscaling across clusters with resource metrics

In Karmada, a FederatedHPA scales up/down the workload's replicas across multiple clusters, with the aim of automatically scaling the workload to match the demand.

When the load is increased, FederatedHPA scales up the replicas of the workload (Deployment, StatefulSet, or other similar resource) if the number of Pods is under the configured maximum. When the load is decreased, FederatedHPA scales down the replicas of the workload if the number of Pods is above the configured minimum.

This document walks you through an example of enabling FederatedHPA to automatically manage scaling for a cross-cluster nginx Deployment.

The walkthrough example will do as follows:
federatedhpa-demo

One Deployment's Pod exists in member1 cluster.
The Service is deployed in member1 and member2 clusters.
Request to use the multi-cluster Service to increase Pod's CPU utilization.
The replicas will be scaled up in member1 and member2 clusters.

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 Network

Ensure that at least two clusters have been added to Karmada, and the container networks between member clusters are connected.

If you use the hack/local-up-karmada.sh script to deploy Karmada, Karmada will have three member clusters, and the container networks of the member1 and member2 will be connected.
You can use Submariner or other related open source projects to connect networks between member clusters.

Note: To prevent routing conflicts, the Pod and Service CIDRs of clusters must be non-overlapping.

The ServiceExport and ServiceImport CRDs have been installed

We need to install ServiceExport and ServiceImport in the member clusters to enable multi-cluster Service.

After ServiceExport and ServiceImport have been installed on the Karmada Control Plane, we can create ClusterPropagationPolicy to propagate those two CRDs to the member clusters.

# propagate ServiceExport CRD
apiVersion: policy.karmada.io/v1alpha1
kind: ClusterPropagationPolicy
metadata:
  name: serviceexport-policy
spec:
  resourceSelectors:
    - apiVersion: apiextensions.k8s.io/v1
      kind: CustomResourceDefinition
      name: serviceexports.multicluster.x-k8s.io
  placement:
    clusterAffinity:
      clusterNames:
        - member1
        - member2
---        
# propagate ServiceImport CRD
apiVersion: policy.karmada.io/v1alpha1
kind: ClusterPropagationPolicy
metadata:
  name: serviceimport-policy
spec:
  resourceSelectors:
    - apiVersion: apiextensions.k8s.io/v1
      kind: CustomResourceDefinition
      name: serviceimports.multicluster.x-k8s.io
  placement:
    clusterAffinity:
      clusterNames:
        - member1
        - member2

metrics-server has been installed in member clusters

We need to install metrics-server for member clusters to provider the metrics API, install it by running:

hack/deploy-k8s-metrics-server.sh ${member_cluster_kubeconfig} ${member_cluster_context_name}

If you use the hack/local-up-karmada.sh script to deploy Karmada, you can run following command to deploy metrics-server in all three member clusters:

hack/deploy-k8s-metrics-server.sh $HOME/.kube/members.config member1
hack/deploy-k8s-metrics-server.sh $HOME/.kube/members.config member2
hack/deploy-k8s-metrics-server.sh $HOME/.kube/members.config member3

karmada-metrics-adapter has been installed in Karmada control plane

We need to install karmada-metrics-adapter in Karmada control plane to provide the metrics API, install it by running:

hack/deploy-metrics-adapter.sh ${host_cluster_kubeconfig} ${host_cluster_context} ${karmada_apiserver_kubeconfig} ${karmada_apiserver_context_name}

If you use the hack/local-up-karmada.sh script to deploy Karmada, karmada-metrics-adapter will be installed by default.

Deploy workloads in `member1` and `member2` clusters

We need to deploy the Deployment (1 replica) and Service in member1 and member2.

apiVersion: apps/v1
kind: Deployment
metadata:
  name: nginx
  labels:
    app: nginx
spec:
  replicas: 1
  selector:
    matchLabels:
      app: nginx
  template:
    metadata:
      labels:
        app: nginx
    spec:
      containers:
      - image: nginx
        name: nginx
        resources:
          requests:
            cpu: 25m
            memory: 64Mi
          limits:
            cpu: 25m
            memory: 64Mi
---
apiVersion: v1
kind: Service
metadata:
  name: nginx-service
spec:
  ports:
  - port: 80
    targetPort: 80
  selector:
    app: nginx
---
apiVersion: policy.karmada.io/v1alpha1
kind: PropagationPolicy
metadata:
  name: nginx-propagation
spec:
  resourceSelectors:
    - apiVersion: apps/v1
      kind: Deployment
      name: nginx
    - apiVersion: v1
      kind: Service
      name: nginx-service
  placement:
    clusterAffinity:
      clusterNames:
        - member1
        - member2
    replicaScheduling:
      replicaDivisionPreference: Weighted
      replicaSchedulingType: Divided
      weightPreference:
        staticWeightList:
          - targetCluster:
              clusterNames:
                - member1
            weight: 1
          - targetCluster:
              clusterNames:
                - member2
            weight: 1

After deploying, you can check the propagation of the Pods and Service:

$ karmadactl get pods
NAME                     CLUSTER   READY   STATUS    RESTARTS   AGE
nginx-777bc7b6d7-mbdn8   member1   1/1     Running   0          9h
$ karmadactl get svc
NAME                    CLUSTER   TYPE        CLUSTER-IP      EXTERNAL-IP   PORT(S)   AGE   ADOPTION
nginx-service           member1   ClusterIP   10.11.216.215   <none>        80/TCP    9h    Y
nginx-service           member2   ClusterIP   10.13.46.61     <none>        80/TCP    9h    Y

Deploy FederatedHPA in Karmada control plane

Then let's deploy FederatedHPA in Karmada control plane.

apiVersion: autoscaling.karmada.io/v1alpha1
kind: FederatedHPA
metadata:
  name: nginx
spec:
  scaleTargetRef:
    apiVersion: apps/v1
    kind: Deployment
    name: nginx
  minReplicas: 1
  maxReplicas: 10
  behavior:
    scaleDown:
      stabilizationWindowSeconds: 10
    scaleUp:
      stabilizationWindowSeconds: 10
  metrics:
    - type: Resource
      resource:
        name: cpu
        target:
          type: Utilization
          averageUtilization: 10

After deploying, you can check the FederatedHPA:

$ kubectl --kubeconfig $HOME/.kube/karmada.config --context karmada-apiserver get fhpa
NAME    REFERENCE-KIND   REFERENCE-NAME   MINPODS   MAXPODS   REPLICAS   AGE
nginx   Deployment       nginx            1         10        1          9h

Export Service to `member1` cluster

As mentioned before, we need a multi-cluster Service to route the requests to the Pods in member1 and member2 clusters, so let create this multi-cluster Service.

Create a ServiceExport object on Karmada Control Plane, and then create a PropagationPolicy to propagate the ServiceExport object to member1 and member2 clusters.

apiVersion: multicluster.x-k8s.io/v1alpha1
kind: ServiceExport
metadata:
  name: nginx-service
---
apiVersion: policy.karmada.io/v1alpha1
kind: PropagationPolicy
metadata:
  name: serve-export-policy
spec:
  resourceSelectors:
    - apiVersion: multicluster.x-k8s.io/v1alpha1
      kind: ServiceExport
      name: nginx-service
  placement:
    clusterAffinity:
      clusterNames:
        - member1
        - member2

Create a ServiceImport object on Karmada Control Plane, and then create a PropagationPolicy to propagate the ServiceImport object to member1 clusters.

apiVersion: multicluster.x-k8s.io/v1alpha1
kind: ServiceImport
metadata:
  name: nginx-service
spec:
  type: ClusterSetIP
  ports:
  - port: 80
    protocol: TCP
---
apiVersion: policy.karmada.io/v1alpha1
kind: PropagationPolicy
metadata:
  name: serve-import-policy
spec:
  resourceSelectors:
    - apiVersion: multicluster.x-k8s.io/v1alpha1
      kind: ServiceImport
      name: nginx-service
  placement:
    clusterAffinity:
      clusterNames:
        - member1

After deploying, you can check the multi-cluster Service:

$ karmadactl get svc
NAME                    CLUSTER   TYPE        CLUSTER-IP      EXTERNAL-IP   PORT(S)   AGE   ADOPTION
derived-nginx-service   member1   ClusterIP   10.11.59.213    <none>        80/TCP    9h    Y

Install hey http load testing tool in member1 cluster

In order to do http requests, here we use hey.

Download hey and copy it to kind cluster container.

wget https://hey-release.s3.us-east-2.amazonaws.com/hey_linux_amd64
chmod +x hey_linux_amd64
docker cp hey_linux_amd64 member1-control-plane:/usr/local/bin/hey

Test scaling up

Check the Pod propagation firstly.

$ karmadactl get pods
NAME                     CLUSTER   READY   STATUS      RESTARTS   AGE
nginx-777bc7b6d7-mbdn8   member1   1/1     Running     0          61m

Check multi-cluster Service IP.

$ karmadactl get svc
NAME                    CLUSTER   TYPE        CLUSTER-IP        EXTERNAL-IP   PORT(S)   AGE   ADOPTION
derived-nginx-service   member1   ClusterIP   10.11.59.213      <none>        80/TCP    20m   Y

Request multi-cluster Service to increase the nginx Pods' CPU usage using hey.
```
docker exec member1-control-plane hey -c 1000 -z 1m http://10.11.59.213
```

Wait 15s, the replicas will be scaled up, then you can check the Pod propagation again.

$ karmadactl get pods -l app=nginx
NAME                     CLUSTER   READY   STATUS      RESTARTS   AGE
nginx-777bc7b6d7-c2cfv   member1   1/1     Running     0          22s
nginx-777bc7b6d7-mbdn8   member1   1/1     Running     0          62m
nginx-777bc7b6d7-pk2s4   member1   1/1     Running     0          37s
nginx-777bc7b6d7-tbb4k   member1   1/1     Running     0          37s
nginx-777bc7b6d7-znlj9   member1   1/1     Running     0          22s
nginx-777bc7b6d7-6n7d9   member2   1/1     Running     0          22s
nginx-777bc7b6d7-dfbnw   member2   1/1     Running     0          22s
nginx-777bc7b6d7-fsdg2   member2   1/1     Running     0          37s
nginx-777bc7b6d7-kddhn   member2   1/1     Running     0          22s
nginx-777bc7b6d7-lwn52   member2   1/1     Running     0          37s

Test scaling down

After 1 minute, the load testing tool will be stopped, then you can see the workload is scaled down across clusters.

$ karmadactl get pods -l app=nginx
NAME                     CLUSTER   READY   STATUS    RESTARTS   AGE
nginx-777bc7b6d7-mbdn8   member1   1/1     Running   0          64m

Prerequisites​

Karmada has been installed​

Member Cluster Network​

The ServiceExport and ServiceImport CRDs have been installed​

metrics-server has been installed in member clusters​

karmada-metrics-adapter has been installed in Karmada control plane​

Deploy workloads in member1 and member2 clusters​

Deploy FederatedHPA in Karmada control plane​

Export Service to member1 cluster​

Install hey http load testing tool in member1 cluster​

Test scaling up​

Test scaling down​