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Version: v1.8

Autoscaling Deployment across clusters with CronFederatedHPA

In Karmada, the CronFederatedHPA is responsible for scaling replicas of workloads (such as Deployments) or the minReplicas/maxReplicas of FederatedHPAs. Its purpose is to proactively scale the business in order to handle sudden spikes in load.

This document provides an example of how to enable CronFederatedHPA for a cross-cluster nginx deployment.

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.

Deploy workload in member1 and member2 cluster

We need to deploy deployment(2 replica) in member1 and member2:

apiVersion: apps/v1
kind: Deployment
metadata:
name: nginx
labels:
app: nginx
spec:
replicas: 2
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: policy.karmada.io/v1alpha1
kind: PropagationPolicy
metadata:
name: nginx-propagation
spec:
resourceSelectors:
- apiVersion: apps/v1
kind: Deployment
name: nginx
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 created pods:

$ karmadactl get pods
NAME CLUSTER READY STATUS RESTARTS AGE
nginx-777bc7b6d7-rmmzv member1 1/1 Running 0 104s
nginx-777bc7b6d7-9gf7g member2 1/1 Running 0 104s

Deploy CronFederatedHPA in Karmada control plane

Then let's deploy CronFederatedHPA in Karmada control plane to scale up the Deployment:

apiVersion: autoscaling.karmada.io/v1alpha1
kind: CronFederatedHPA
metadata:
name: nginx-cronfhpa
namespace: default
spec:
scaleTargetRef:
apiVersion: apps/v1
kind: Deployment
name: nginx
rules:
- name: "scale-up"
schedule: "*/1 * * * *"
targetReplicas: 5
suspend: false

The spec.schedule follows the following format:

# ┌───────────── minute (0 - 59)
# │ ┌───────────── hour (0 - 23)
# │ │ ┌───────────── day of the month (1 - 31)
# │ │ │ ┌───────────── month (1 - 12)
# │ │ │ │ ┌───────────── day of the week (0 - 6) (Sunday to Saturday;
# │ │ │ │ │ 7 is also Sunday on some systems)
# │ │ │ │ │ OR sun, mon, tue, wed, thu, fri, sat
# │ │ │ │ │
# * * * * *

The expression */1 * * * * means that the nginx deployment's replicas should be updated to 5 every minute. This ensures that the workload's replicas will be scaled up to 5 in order to handle sudden load peaks.

Testing scaling up

After one minute, the replicas of nginx deployment is scaled to 5 by CronFederatedHPA. Let's now check the number of pods to verify if the scaling has been done as expected:

$ karmadactl get pods
NAME CLUSTER READY STATUS RESTARTS AGE
nginx-777bc7b6d7-8v9b4 member2 1/1 Running 0 18s
nginx-777bc7b6d7-9gf7g member2 1/1 Running 0 8m2s
nginx-777bc7b6d7-5snhz member1 1/1 Running 0 18s
nginx-777bc7b6d7-rmmzv member1 1/1 Running 0 8m2s
nginx-777bc7b6d7-z9kwg member1 1/1 Running 0 18s

By checking the status field of CronFederatedHPA, you can access the scaling history:

$ kubectl --kubeconfig $HOME/.kube/karmada.config --context karmada-apiserver get cronfhpa/nginx-cronfhpa -oyaml
apiVersion: autoscaling.karmada.io/v1alpha1
kind: CronFederatedHPA
metadata:
name: nginx-cronfhpa
namespace: default
spec:
rules:
- failedHistoryLimit: 3
name: scale-up
schedule: '*/1 * * * *'
successfulHistoryLimit: 3
suspend: false
targetReplicas: 5
scaleTargetRef:
apiVersion: apps/v1
kind: Deployment
name: nginx
status:
executionHistories:
- nextExecutionTime: "2023-07-29T03:27:00Z" # The next expected scaling time
ruleName: scale-up
successfulExecutions:
- appliedReplicas: 5 # CronFederatedHPA updates the nginx deployment's replicas to 5
executionTime: "2023-07-29T03:26:00Z" # The actual scaling time
scheduleTime: "2023-07-29T03:26:00Z" # The last expected scaling time

The scaling history includes information about both successful and failed scaling operations.