Configure multidimensional Pod autoscaling

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.

Prerequisites

Using multidimensional Pod autoscaling

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

Creating a 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

Creating a MultidimPodAutoscaler

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

Viewing a MultidimPodAutoscaler

View all MultidimPodAutoscaler objects by using the kubectl get command:

kubectl get mpa

Deleting a MultidimPodAutoscaler

Delete a MultidimPodAutoscaler object by using the kubectl delete command:

kubectl delete -f php-apache-autoscaler.yaml

API reference

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

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.

Field Type Description
metadata ObjectMeta Standard object metadata.
spec MultidimPodAutoscalerSpec The desired behavior of the multidimensional Pod autoscaler.
status MultidimPodAutoscalerStatus The most recently observed status of the multidimensional Pod autoscaler.

MultidimPodAutoscalerSpec

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

Field Type Description
ScaleTargetRef autoscaling.CrossVersionObjectReference A reference that points to a target resource to scale (with the Scale subresource).
Goals *MultidimGoals Goals that the multidimensional Pod autoscaler tries to achieve and maintain.
Constraints *MultidimConstraints Describes the constraints for autoscaling. Constraints outweigh goals. If constraints block some goal, then the goal will not be reached. For example, reaching the maximum replica count prevents further scale up even if the replicas might need to scale.
Policy *MultidimPolicy Policy allows you to specify how the recommendations are applied.

MultidimGoals

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

Field Type Description
Metrics []MetricSpec Contains the list of metrics along with the desired value. Multidimensional Pod autoscaler tries to stay close to the desired values.

MultidimConstraints

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

Field Type Description
Global *GlobalConstraints Constraints that apply to the autoscaled application as a whole.
Pod *PodConstraints Constraints that apply to a single Pods from the targeted application.
ContainerControlledResources []ResourceName Container Resources that should be controlled by the autoscaler. memory is the only supported value.
Container []ContainerConstraints Constraints that apply to Pods' containers.

ResourceConstraints

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

Field Type Description
MinAllowed ResourceList Minimum amount of resources that you can assign. If not provided, 0 is used.
MaxAllowed ResourceList Maximum amount of resources that you can assign. If not provided, there are no limits on the maximum amount of resources.

GlobalConstraints

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

Field Type Description
MinReplicas *Int32 Minimum amount of replicas that the application can have. If not provided, 1 is used.
MaxReplicas *Int32 Maximum amount of replicas that the application can have. If not provided, there are no limits on the maximum amount of replicas.
Requests *ResourceConstraints Minimum and maximum amount of resources that an application can request, summed across all pods.

PodConstraints

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

Field Type Description
Requests *ResourceConstraints Minimum and maximum amount of resources that a single Pod can request, summed across all containers that belong to the Pod.

ContainerConstraints

ContainerConstraints are constraints that apply to Pods' containers.

Fields Type Description
Name String Name of the container for which the constraints are specified. You can also use * to specify constraints for all containers in a Pod.
Requests *ResourceConstraints Minimum and maximum amount of resources that the specified container can request.

UpdateMode

Use UpdateMode to control how the calculated recommendations are applied.

Fields Type Description
AutoUpdates UpdateMode = "Auto" AutoUpdates means that all autoscaler recommendations can be applied at any time.
AutoUpdates UpdateMode = "Off" AutoUpdates means that autoscaler recommendations are not applied at all.

MultidimPolicy

Fields Type Description
Update UpdateMode Defines how the recommendations should be applied. An empty value fails validation.

MultidimPodAutoscalerStatus

MultidimPodAutoscalerStatus describes the runtime state of the autoscaler.

Fields Type Description
ObservedGeneration *Int64 The most recently generation observed by this autoscaler.
RecommendedPodResources *RecommendedPodResources The most recently computed amount of resources recommended by the autoscaler for the controlled Pods.
CurrentReplicas Int32 CurrentReplicas is the current number of replicas of Pods managed by this autoscaler, as last seen by the autoscaler.
DesiredReplicas Int32 DesiredReplicas is the desired number of replicas of Pods managed by this autoscaler, as last calculated by the autoscaler.
CurrentMetrics []autoscaling.MetricStatus The last read state of the metrics used by this autoscaler.
Conditions []metav1.Condition Conditions is the set of conditions required for this autoscaler to scale its target, and indicates whether or not those conditions are met.

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