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Version: v1.8

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

This document walk you through an example of enabling FederatedHPA to automatically manage scale for a cross-cluster deployed nginx.

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 cluster.
  • Request the multi-cluster service and trigger the pod's CPU usage increases.
  • The replicas will be scaled up in member1 and member2 cluster.

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: In order to prevent routing conflicts, Pod and Service CIDRs of clusters need 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 workload in member1 and member2 cluster

We need to deploy 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 distribution 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 cluster, so let create this mult-cluster service.

  • Create a ServiceExport object on Karmada Control Plane, and then create a PropagationPolicy to propagate the ServiceExport object to member1 and member2 cluster.
    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 cluster.
    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 distribution 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 with hey to increase the nginx pods' CPU usage.

    $ 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 distribution 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