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版本:v1.9

FederatedHPA scales with custom 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.

FederatedHPA not only supports resource metrics such as CPU and memory, but also supports custom metrics which may expand the use cases of FederatedHPA.

This document walks you through an example of enabling FederatedHPA to automatically manage scale for a cross-cluster app with custom metrics.

The walkthrough example will do as follows:

federatedhpa-custom-metrics-demo

  • One sample-deployment's pod exists in member1 cluster.
  • The service is deployed in member1 and member2 cluster.
  • Request the multi-cluster service and trigger an increase in the pod's custom metrics(http_requests_total).
  • The replicas will be scaled up in member1 and member2 cluster.

Prerequisites

Karmada has been installed

You 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

You 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, you 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

prometheus and prometheus-adapter have been installed in member clusters

You need to install prometheus and prometheus-adapter for member clusters to provider the custom metrics. You can install it by running the following in member clusters:

git clone https://github.com/prometheus-operator/kube-prometheus.git
cd kube-prometheus
kubectl apply --server-side -f manifests/setup
kubectl wait \
--for condition=Established \
--all CustomResourceDefinition \
--namespace=monitoring
kubectl apply -f manifests/

You can verify the installation by the following command:

kubectl --kubeconfig=/root/.kube/members.config --context=member1 get po -nmonitoring
NAME READY STATUS RESTARTS AGE
alertmanager-main-0 2/2 Running 0 30h
alertmanager-main-1 2/2 Running 0 30h
alertmanager-main-2 2/2 Running 0 30h
blackbox-exporter-6bc47b9578-zcbb7 3/3 Running 0 30h
grafana-6b68cd6b-vmw74 1/1 Running 0 30h
kube-state-metrics-597db7f85d-2hpfs 3/3 Running 0 30h
node-exporter-q8hdx 2/2 Running 0 30h
prometheus-adapter-57d9587488-86ckj 1/1 Running 0 29h
prometheus-adapter-57d9587488-zrt29 1/1 Running 0 29h
prometheus-k8s-0 2/2 Running 0 30h
prometheus-k8s-1 2/2 Running 0 30h
prometheus-operator-7d4b94944f-kkwkk 2/2 Running 0 30h

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

You 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, you can run following command to deploy karmada-metrics-adapter:

hack/deploy-metrics-adapter.sh $HOME/.kube/karmada.config karmada-host $HOME/.kube/karmada.config karmada-apiserver

Deploy workload in member1 and member2 cluster

You need to deploy a sample deployment(1 replica) and service in member1 and member2.

apiVersion: apps/v1
kind: Deployment
metadata:
name: sample-app
labels:
app: sample-app
spec:
replicas: 1
selector:
matchLabels:
app: sample-app
template:
metadata:
labels:
app: sample-app
spec:
containers:
- image: luxas/autoscale-demo:v0.1.2
name: metrics-provider
ports:
- name: http
containerPort: 8080
---
apiVersion: v1
kind: Service
metadata:
labels:
app: sample-app
name: sample-app
spec:
ports:
- name: http
port: 80
protocol: TCP
targetPort: 8080
selector:
app: sample-app
type: ClusterIP
---
apiVersion: policy.karmada.io/v1alpha1
kind: PropagationPolicy
metadata:
name: app-propagation
spec:
resourceSelectors:
- apiVersion: apps/v1
kind: Deployment
name: sample-app
- apiVersion: v1
kind: Service
name: sample-app
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
sample-app-9b7d8c9f5-xrnfx member1 1/1 Running 0 111s
$ karmadactl get svc
NAME CLUSTER TYPE CLUSTER-IP EXTERNAL-IP PORT(S) AGE ADOPTION
sample-app member1 ClusterIP 10.11.29.250 <none> 80/TCP 3m53s Y

Monitor your application in member1 and member2 cluster

In order to monitor your application, you'll need to set up a ServiceMonitor pointing at the application. Assuming you've set up your Prometheus instance to use ServiceMonitors with the app: sample-app label, create a ServiceMonitor to monitor the app's metrics via the service:

apiVersion: monitoring.coreos.com/v1
kind: ServiceMonitor
metadata:
name: sample-app
labels:
app: sample-app
spec:
selector:
matchLabels:
app: sample-app
endpoints:
- port: http
kubectl create -f sample-app.monitor.yaml

Now, you should see your metrics (http_requests_total) appear in your Prometheus instance. Look them up via the dashboard, and make sure they have the namespace and pod labels. If not, check the labels on the service monitor match the ones on the Prometheus CRD.

Launch you adapter in member1 and member2 cluster

After you deploy prometheus-adapter, you need to update to the adapter config which is necessary in order to expose custom metrics.

apiVersion: v1
kind: ConfigMap
metadata:
name: adapter-config
namespace: monitoring
data:
config.yaml: |-
"rules":
- "seriesQuery": |
{namespace!="",__name__!~"^container_.*"}
"resources":
"template": "<<.Resource>>"
"name":
"matches": "^(.*)_total"
"as": ""
"metricsQuery": |
sum by (<<.GroupBy>>) (
irate (
<<.Series>>{<<.LabelMatchers>>}[1m]
)
)
$ kubectl apply -f prom-adapter.config.yaml
# Restart prom-adapter pods
$ kubectl rollout restart deployment prometheus-adapter -n monitoring

Registry metrics API in member1 and member2 cluster

You also need to register the custom metrics API with the API aggregator (part of the main Kubernetes API server). For that you need to create an APIService resource.

apiVersion: apiregistration.k8s.io/v1
kind: APIService
metadata:
name: v1beta2.custom.metrics.k8s.io
spec:
group: custom.metrics.k8s.io
groupPriorityMinimum: 100
insecureSkipTLSVerify: true
service:
name: prometheus-adapter
namespace: monitoring
version: v1beta2
versionPriority: 100
$ kubectl create -f api-service.yaml

The API is registered as custom.metrics.k8s.io/v1beta2, and you can use the following command to verify:

$ kubectl get --raw "/apis/custom.metrics.k8s.io/v1beta2/namespaces/default/pods/*/http_requests?selector=app%3Dsample-app"

The output is similar to:

{"kind":"MetricValueList","apiVersion":"custom.metrics.k8s.io/v1beta2","metadata":{},"items":[{"describedObject":{"kind":"Pod","namespace":"default","name":"sample-app-9b7d8c9f5-9lw6b","apiVersion":"/v1"},"metric":{"name":"http_requests","selector":null},"timestamp":"2023-06-14T09:09:54Z","value":"66m"}]}

If karmada-metrics-adapter is installed successfully, you can also verify it with the above command in Karmada control plane.

Deploy FederatedHPA in Karmada control plane

Then let's deploy FederatedHPA in Karmada control plane.

apiVersion: autoscaling.karmada.io/v1alpha1
kind: FederatedHPA
metadata:
name: sample-app
spec:
scaleTargetRef:
apiVersion: apps/v1
kind: Deployment
name: sample-app
minReplicas: 1
maxReplicas: 10
behavior:
scaleDown:
stabilizationWindowSeconds: 10
scaleUp:
stabilizationWindowSeconds: 10
metrics:
- type: Pods
pods:
metric:
name: http_requests
target:
averageValue: 700m
type: Value

After deploying, you can check the FederatedHPA:

NAME          REFERENCE-KIND   REFERENCE-NAME   MINPODS   MAXPODS   REPLICAS   AGE
sample-app Deployment sample-app 1 10 1 15d

Export service to member1 cluster

As mentioned before, you 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: sample-app
    ---
    apiVersion: policy.karmada.io/v1alpha1
    kind: PropagationPolicy
    metadata:
    name: serve-export-policy
    spec:
    resourceSelectors:
    - apiVersion: multicluster.x-k8s.io/v1alpha1
    kind: ServiceExport
    name: sample-app
    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: sample-app
    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: sample-app
    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-sample-app 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 you can 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
    sample-app-9b7d8c9f5-xrnfx member1 1/1 Running 0 111s
  • Check multi-cluster service ip.

    $ karmadactl get svc
    NAME CLUSTER TYPE CLUSTER-IP EXTERNAL-IP PORT(S) AGE ADOPTION
    derived-sample-app 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/metrics
  • Wait 15s, the replicas will be scaled up, then you can check the pod distribution again.

    $ karmadactl get po -l app=sample-app
    NAME CLUSTER READY STATUS RESTARTS AGE
    sample-app-9b7d8c9f5-454vz member2 1/1 Running 0 84s
    sample-app-9b7d8c9f5-7fjhn member2 1/1 Running 0 69s
    sample-app-9b7d8c9f5-ddf4s member2 1/1 Running 0 69s
    sample-app-9b7d8c9f5-mxqmh member2 1/1 Running 0 84s
    sample-app-9b7d8c9f5-qbc2j member2 1/1 Running 0 69s
    sample-app-9b7d8c9f5-2tgxt member1 1/1 Running 0 69s
    sample-app-9b7d8c9f5-66n9s member1 1/1 Running 0 69s
    sample-app-9b7d8c9f5-fbzps member1 1/1 Running 0 84s
    sample-app-9b7d8c9f5-ldmhz member1 1/1 Running 0 84s
    sample-app-9b7d8c9f5-xrnfx member1 1/1 Running 0 87m

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=sample-app
NAME CLUSTER READY STATUS RESTARTS AGE
sample-app-9b7d8c9f5-xrnfx member1 1/1 Running 0 91m