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Horizontal Pod Autoscaling

This document describes the current state of Horizontal Pod Autoscaling in Kubernetes.

What is Horizontal Pod Autoscaling?

With Horizontal Pod Autoscaling, Kubernetes automatically scales the number of pods in a replication controller, deployment or replica set based on observed CPU utilization (or, with alpha support, on some other, application-provided metrics).

The Horizontal Pod Autoscaler is implemented as a Kubernetes API resource and a controller. The resource determines the behavior of the controller. The controller periodically adjusts the number of replicas in a replication controller or deployment to match the observed average CPU utilization to the target specified by user.

How does the Horizontal Pod Autoscaler work?

Horizontal Pod Autoscaler diagram

The autoscaler is implemented as a control loop. It periodically queries CPU utilization for the pods it targets. (The period of the autoscaler is controlled by --horizontal-pod-autoscaler-sync-period flag of controller manager. The default value is 30 seconds). Then, it compares the arithmetic mean of the pods’ CPU utilization with the target and adjust the number of replicas if needed.

CPU utilization is the recent CPU usage of a pod divided by the sum of CPU requested by the pod’s containers. Please note that if some of the pod’s containers do not have CPU request set, CPU utilization for the pod will not be defined and the autoscaler will not take any action. Further details of the autoscaling algorithm are given here.

The autoscaler uses heapster to collect CPU utilization. Therefore, it is required to deploy heapster monitoring in your cluster for autoscaling to work.

The autoscaler accesses corresponding replication controller, deployment or replica set by scale sub-resource. Scale is an interface which allows to dynamically set the number of replicas and to learn the current state of them. More details on scale sub-resource can be found here.

API Object

Horizontal Pod Autoscaler is a top-level resource in the Kubernetes REST API. In Kubernetes 1.2 HPA was graduated from beta to stable (more details about api versioning) with compatibility between versions. The stable version is available in the autoscaling/v1 api group whereas the beta vesion is available in the extensions/v1beta1 api group as before. The transition plan is to deprecate beta version of HPA in Kubernetes 1.3, and get it rid off completely in Kubernetes 1.4.

Warning! Please have in mind that all Kubernetes components still use HPA in extensions/v1beta1 in Kubernetes 1.2.

More details about the API object can be found at HorizontalPodAutoscaler Object.

Support for Horizontal Pod Autoscaler in kubectl

Horizontal Pod Autoscaler, like every API resource, is supported in a standard way by kubectl. We can create a new autoscaler using kubectl create command. We can list autoscalers by kubectl get hpa and get detailed description by kubectl describe hpa. Finally, we can delete an autoscaler using kubectl delete hpa.

In addition, there is a special kubectl autoscale command for easy creation of a Horizontal Pod Autoscaler. For instance, executing kubectl autoscale rc foo --min=2 --max=5 --cpu-percent=80 will create an autoscaler for replication controller foo, with target CPU utilization set to 80% and the number of replicas between 2 and 5. The detailed documentation of kubectl autoscale can be found here.

Autoscaling during rolling update

Currently in Kubernetes, it is possible to perform a rolling update by managing replication controllers directly, or by using the deployment object, which manages the underlying replication controllers for you. Horizontal Pod Autoscaler only supports the latter approach: the Horizontal Pod Autoscaler is bound to the deployment object, it sets the size for the deployment object, and the deployment is responsible for setting sizes of underlying replication controllers.

Horizontal Pod Autoscaler does not work with rolling update using direct manipulation of replication controllers, i.e. you cannot bind a Horizontal Pod Autoscaler to a replication controller and do rolling update (e.g. using kubectl rolling-update). The reason this doesn’t work is that when rolling update creates a new replication controller, the Horizontal Pod Autoscaler will not be bound to the new replication controller.

Support for custom metrics

Kubernetes 1.2 adds alpha support for scaling based on application-specific metrics like QPS (queries per second) or average request latency.


The cluster has to be started with ENABLE_CUSTOM_METRICS environment variable set to true.

Pod configuration

The pods to be scaled must have cAdvisor-specific custom (aka application) metrics endpoint configured. The configuration format is described here. Kubernetes expects the configuration to be placed in definition.json mounted via a config map in /etc/custom-metrics. A sample config map may look like this:

apiVersion: v1
kind: ConfigMap
  name: cm-config
  definition.json: "{\"endpoint\" : \"http://localhost:8080/metrics\"}"

Warning Due to the way cAdvisor currently works localhost refers to the node itself, not to the running pod. Thus the appropriate container in the pod must ask for a node port. Example:

    - hostPort: 8080
      containerPort: 8080

Specifying target

HPA for custom metrics is configured via an annotation. The value in the annotation is interpreted as a target metric value averaged over all running pods. Example:

      alpha/ '{"items":[{"name":"qps", "value": "10"}]}'

In this case, if there are four pods running and each pod reports a QPS metric of 15 or higher, horizontal pod autoscaling will start two additional pods (for a total of six pods running).

If you specify multiple metrics in your annotation or if you set a target CPU utilization, horizontal pod autoscaling will scale to according to the metric that requires the highest number of replicas.

If you do not specify a target for CPU utilization, Kubernetes defaults to an 80% utilization threshold for horizontal pod autoscaling.

If you want to ensure that horizontal pod autoscaling calculates the number of required replicas based only on custom metrics, you should set the CPU utilization target to a very large value (such as 100000%). As this level of CPU utilization isn’t possible, horizontal pod autoscaling will calculate based only on the custom metrics (and min/max limits).

Further reading


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