Vertical Pod autoscaling

This page provides an overview of vertical Pod autoscaling and explains how you can use it to adjust CPU requests and limits and memory requests and limits for containers. This page also provides reference material for the VerticalPodAutoscaler custom resource and related types.

Overview

Vertical Pod autoscaling frees you from having to think about what values to specify for a container's CPU and memory requests. The autoscaler can recommend values for CPU and memory requests and limits, or it can automatically update the values.

Vertical Pod autoscaling provides these benefits:

• Cluster nodes are used efficiently, because Pods use exactly what they need.

• Pods are scheduled onto nodes that have the appropriate resources available.

• You don't have to run time-consuming benchmarking tasks to determine the correct values for CPU and memory requests.

• Maintenance time is reduced, because the autoscaler can adjust CPU and memory requests over time without any action on your part.

Limitations for vertical Pod autoscaling

• Vertical Pod autoscaling supports a maximum of 500 VerticalPodAutoscaler objects per cluster.

• Vertical Pod autoscaling is supported on regional clusters starting from version 1.12.6.

• Do not use vertical Pod autoscaling with Horizontal Pod autoscaling on CPU or memory. However, you can use vertical Pod autoscaling with Horizontal Pod autoscaling on custom and external metrics.

• Vertical Pod autoscaling is not yet ready for use with JVM-based workloads due to limited visibility into actual memory usage of the workload.

• Vertical Pod autoscaling cannot automatically apply recommendations for injected sidecars. If you create a VerticalPodAutoscaler object for a Pod with injected sidecars with an updateMode other than "Off", those sidecars must be opted out using the container resource policy. See ContainerResourcePolicy in the reference section and how to opt out specific containers.

  One of the known issues is using vertical Pod autoscaling alongside Istio. This limitation affects Istio sidecars. To solve the issue, opt out the Istio sidecar by extending your VerticalPodAutoscaler spec definition with:

  resourcePolicy:
    containerPolicies:
    - containerName: istio-proxy
      mode: "Off"
  

Vertical Pod autoscaling in Auto mode

Due to Kubernetes limitations, the only way to modify the resource requests of a running Pod is to recreate the Pod. If you create a VerticalPodAutoscaler with an updateMode of "Auto", the VerticalPodAutoscaler evicts a Pod if it needs to change the Pod's resource requests.

To limit the amount of Pod restarts, use a Pod disruption budget.

To make sure that your cluster can handle the new sizes of your workloads, use Cluster autoscaler and Node auto-provisioning. Vertical Pod autoscaling notifies cluster autoscaler ahead of the update, and the resources needed for the resized workload are provided before recreating it, to minimize the disruption time.

What's next

• Configuring vertical Pod autoscaling
• Assign CPU resources to containers and Pods
• Assign memory resources to containers and Pods
• Scaling an application
• Autoscaling deployments with custom metrics
• Cluster autoscaler

Reference

Note: This is the v1 API reference. We strongly recommend using this version of the API. For instructions on migration and the reference for v1beta1 API, see the migration guide.

VerticalPodAutoscaler v1 autoscaling.k8s.io

Fields
	TypeMeta API group, version, and kind.
metadata	ObjectMeta Standard object metadata.
spec	VerticalPodAutoscalerSpec The desired behavior of the VerticalPodAutoscaler.
status	VerticalPodAutoscalerStatus The most recently observed status of the VerticalPodAutoscaler.

VerticalPodAutoscalerSpec v1 autoscaling.k8s.io

Fields
targetRef	CrossVersionObjectReference Reference to the controller that manages the set of Pods for the autoscaler to control, for example, a Deployment or a StatefulSet. You can point a VerticalPodAutoscaler at any controller that has a Scale subresource. Typically, the VerticalPodAutoscaler retrieves the Pod set from the controller's ScaleStatus. For some well known controllers, for example DaemonSet, the VerticalPodAutoscaler retrieves the Pod set from the controller's spec.
updatePolicy	PodUpdatePolicy Specifies whether recommended updates are applied when a Pod is started and whether recommended updates are applied during the life of a Pod.
resourcePolicy	PodResourcePolicy Specifies policies for how CPU and memory requests are adjusted for individual containers.

VerticalPodAutoscalerList v1 autoscaling.k8s.io

Fields
	TypeMeta API group, version, and kind.
metadata	ObjectMeta Standard object metadata.
items	VerticalPodAutoscaler array A list of VerticalPodAutoscaler objects.

PodUpdatePolicy v1 autoscaling.k8s.io

Fields
updateMode	string Specifies whether recommended updates are applied when a Pod is started and whether recommended updates are applied during the life of a Pod. Possible values are "Off", "Initial", "Recreate", and "Auto".

PodResourcePolicy v1 autoscaling.k8s.io

Fields
containerPolicies	ContainerResourcePolicy array An array of resource policies for individual containers.

ContainerResourcePolicy v1 autoscaling.k8s.io

Fields
containerName	string The name of the container that the policy applies to. If not specified, the policy serves as the default policy.
mode	ContainerScalingMode Specifies whether recommended updates are applied to the container when it is started and whether recommended updates are applied during the life of the container. Possible values are "Off" and "Auto".
minAllowed	ResourceList Specifies the minimum CPU request and memory request allowed for the container.
maxAllowed	ResourceList Specifies the maximum CPU request and memory request allowed for the container.

VerticalPodAutoscalerStatus v1 autoscaling.k8s.io

Fields
recommendation	RecommendedPodResources The most recently recommended CPU and memory requests.
conditions	VerticalPodAutoscalerCondition array Describes the current state of the VerticalPodAutoscaler.

RecommendedPodResources v1 autoscaling.k8s.io

Fields
containerRecommendation	RecommendedContainerResources array An array of resource recommendations for individual containers.

RecommendedContainerResources v1 autoscaling.k8s.io

Fields
containerName	string The name of the container that the recommendation applies to.
target	ResourceList The recommended CPU request and memory request for the container.
lowerBound	ResourceList The minimum recommended CPU request and memory request for the container. This amount is not guaranteed to be sufficient for the application to be stable. Running with smaller CPU and memory requests is likely to have a significant impact on performance or availability.
upperBound	ResourceList The maximum recommended CPU request and memory request for the container. CPU and memory requests higher than these values are likely to be wasted.
uncappedTarget	ResourceList The most recent resource recommendation computed by the autoscaler, based on actual resource usage, not taking into account the ContainerResourcePolicy. If actual resource usage causes the target to violate the ContainerResourcePolicy, this might be different from the bounded recommendation. This field does not affect actual resource assignment. It is used only as a status indication.

VerticalPodAutoscalerCondition v1 autoscaling.k8s.io

Fields
type	VerticalPodAutoscalerConditionType The type of condition being described. Possible values are "RecommendationProvided", "LowConfidence", "NoPodsMatched", and "FetchingHistory".
status	ConditionStatus The status of the condition. Possible values are True, False, and Unknown.
lastTransitionTime	Time The last time the condition made a transition from one status to another.
reason	string The reason for the last transition from one status to another.
message	string A human-readable string that gives details about the last transition from one status to another.