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Configure multidimensional Pod autoscaling | Google Kubernetes Engine (GKE)

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This feature is subject to the "Pre-GA Offerings Terms" in the General Service Terms section of the Service Specific Terms. Pre-GA features are available "as is" and might have limited support. For more information, see the launch stage descriptions.

Multidimensional Pod autoscaling frees you from choosing a single way to scale your clusters. With multidimensional Pod autoscaling, you can use horizontal scaling based on CPU and vertical scaling based on memory at the same time.

A MultidimPodAutoscaler object modifies memory requests and adds replicas so that the average CPU utilization of each replica matches your target utilization.

• GKE cluster version 1.19.4-gke.1700 or later.
• For Standard clusters, enable vertical Pod autoscaling in your cluster. Vertical Pod autoscaling is already enabled in Autopilot clusters.

This example shows you how to create a Deployment and a MultidimPodAutoscaler object to autoscale your Deployment.

Before you can create a MultidimPodAutoscaler, you must create the workload it monitors. The following file, php-apache.yaml, specifies a value for the CPU requests:

apiVersion: apps/v1
kind: Deployment
metadata:
  name: php-apache
spec:
  selector:
    matchLabels:
      run: php-apache
  replicas: 1
  template:
    metadata:
      labels:
        run: php-apache
    spec:
      containers:
      - name: php-apache
        image: us-docker.pkg.dev/google-samples/containers/gke/hello-app:1.0
        ports:
        - containerPort: 80
        resources:
        # Since MPA does not specify CPU requests, you must specify a request in
        # the Deployment
          limits:
            cpu: 500m
          requests:
            cpu: 200m

To create the Deployment, apply the php-apache.yaml manifest:

kubectl apply -f php-apache.yaml

Once you have created the Deployment, you can create a MultidimPodAutoscaler object. The following MultidimPodAutoscaler manifest automatically adjusts the number of replicas and memory requests based on the values you specify.

For more information on the fields in this example, see the API reference section.

apiVersion: autoscaling.gke.io/v1beta1
kind: MultidimPodAutoscaler
metadata:
  name: php-apache-autoscaler
spec:
  scaleTargetRef:
    apiVersion: apps/v1
    kind: Deployment
    name: php-apache
  goals:
    metrics:
    - type: Resource
      resource:
      # Define the target CPU utilization request here
        name: cpu
        target:
          type: Utilization
          averageUtilization: 60
  constraints:
    global:
      minReplicas: 1
      maxReplicas: 5
    containerControlledResources: [ memory ]
    container:
    - name: '*'
    # Define boundaries for the memory request here
      requests:
        minAllowed:
          memory: 1Gi
        maxAllowed:
          memory: 2Gi
  policy:
    updateMode: Auto

To create the MultidimPodAutoscaler, apply the php-apache-autoscaler.yaml manifest:

kubectl apply -f php-apache-autoscaler.yaml

View all MultidimPodAutoscaler objects by using the kubectl get command:

kubectl get mpa

Delete a MultidimPodAutoscaler object by using the kubectl delete command:

kubectl delete -f php-apache-autoscaler.yaml

The following sections provide information on the possible fields you can add to your MultidimPodAutoscaler object.

All fields are for apiVersion v1beta1 autoscaling.gke.io.

MultidimPodAutoscaler is the configuration for a multidimensional Pod autoscaler, which automatically manages Pod resources and their count based on historical and real-time resource utilization.

MultidimPodAutoscalerSpec is the specification that defines the behavior of the autoscaler.

MultidimGoals are goals that the multidimensional Pod autoscaler tries to achieve.

MultidimConstraints describe the constraints for autoscaling. Constraints take precedence over goals.

ResourceConstraints define the minimum and maximum amount of resources that you can assign to a container, Pod, or application.

GlobalConstraints define the constraints which apply to the application altogether. These constraints include the number of replicas or the total amount of resources.

PodConstraints define the minimum and maximum amount of resources that a single Pod can request, summed across all containers that belong to the Pod.

ContainerConstraints are constraints that apply to Pods' containers.

Use UpdateMode to control how the calculated recommendations are applied.

MultidimPodAutoscalerStatus describes the runtime state of the autoscaler.

• Learn more about configuring horizontal Pod autoscaling.
• Learn more about configuring vertical Pod autoscaling.

Except as otherwise noted, the content of this page is licensed under the Creative Commons Attribution 4.0 License, and code samples are licensed under the Apache 2.0 License. For details, see the Google Developers Site Policies. Java is a registered trademark of Oracle and/or its affiliates.

Last updated 2025-08-12 UTC.

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