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Autoscaling Workloads With autoscaling, you can automatically update your workloads in one way or another. This allows your cluster to react to changes in resource demand more elastically and efficiently. concept 40

In Kubernetes, you can scale a workload depending on the current demand of resources. This allows your cluster to react to changes in resource demand more elastically and efficiently.

When you scale a workload, you can either increase or decrease the number of replicas managed by the workload, or adjust the resources available to the replicas in-place.

The first approach is referred to as horizontal scaling, while the second is referred to as vertical scaling.

There are manual and automatic ways to scale your workloads, depending on your use case.

Scaling workloads manually

Kubernetes supports manual scaling of workloads. Horizontal scaling can be done using the kubectl CLI. For vertical scaling, you need to patch the resource definition of your workload.

See below for examples of both strategies.

Scaling workloads automatically

Kubernetes also supports automatic scaling of workloads, which is the focus of this page.

The concept of Autoscaling in Kubernetes refers to the ability to automatically update an object that manages a set of Pods (for example a {{< glossary_tooltip text="Deployment" term_id="deployment" >}}).

Scaling workloads horizontally

In Kubernetes, you can automatically scale a workload horizontally using a HorizontalPodAutoscaler (HPA).

It is implemented as a Kubernetes API resource and a {{< glossary_tooltip text="controller" term_id="controller" >}} and periodically adjusts the number of {{< glossary_tooltip text="replicas" term_id="replica" >}} in a workload to match observed resource utilization such as CPU or memory usage.

There is a walkthrough tutorial of configuring a HorizontalPodAutoscaler for a Deployment.

Scaling workloads vertically

{{< feature-state for_k8s_version="v1.25" state="stable" >}}

You can automatically scale a workload vertically using a VerticalPodAutoscaler (VPA). Unlike the HPA, the VPA doesn't come with Kubernetes by default, but is a separate project that can be found on GitHub.

Once installed, it allows you to create {{< glossary_tooltip text="CustomResourceDefinitions" term_id="customresourcedefinition" >}} (CRDs) for your workloads which define how and when to scale the resources of the managed replicas.

{{< note >}} You will need to have the Metrics Server installed to your cluster for the HPA to work. {{< /note >}}

At the moment, the VPA can operate in four different modes:

{{< table caption="Different modes of the VPA" >}}

Mode Description
Auto Currently, Recreate might change to in-place updates in the future
Recreate The VPA assigns resource requests on pod creation as well as updates them on existing pods by evicting them when the requested resources differ significantly from the new recommendation
Initial The VPA only assigns resource requests on pod creation and never changes them later.
Off The VPA does not automatically change the resource requirements of the pods. The recommendations are calculated and can be inspected in the VPA object.
{{< /table >}}

Requirements for in-place resizing

{{< feature-state for_k8s_version="v1.27" state="alpha" >}}

Resizing a workload in-place without restarting the {{< glossary_tooltip text="Pods" term_id="pod" >}} or its {{< glossary_tooltip text="Containers" term_id="container" >}} requires Kubernetes version 1.27 or later. Additionally, the InPlaceVerticalScaling feature gate needs to be enabled.

{{< feature-gate-description name="InPlacePodVerticalScaling" >}}

Autoscaling based on cluster size

For workloads that need to be scaled based on the size of the cluster (for example cluster-dns or other system components), you can use the Cluster Proportional Autoscaler. Just like the VPA, it is not part of the Kubernetes core, but hosted as its own project on GitHub.

The Cluster Proportional Autoscaler watches the number of schedulable {{< glossary_tooltip text="nodes" term_id="node" >}} and cores and scales the number of replicas of the target workload accordingly.

If the number of replicas should stay the same, you can scale your workloads vertically according to the cluster size using the Cluster Proportional Vertical Autoscaler. The project is currently in beta and can be found on GitHub.

While the Cluster Proportional Autoscaler scales the number of replicas of a workload, the Cluster Proportional Vertical Autoscaler adjusts the resource requests for a workload (for example a Deployment or DaemonSet) based on the number of nodes and/or cores in the cluster.

Event driven Autoscaling

It is also possible to scale workloads based on events, for example using the Kubernetes Event Driven Autoscaler (KEDA).

KEDA is a CNCF graduated enabling you to scale your workloads based on the number of events to be processed, for example the amount of messages in a queue. There exists a wide range of adapters for different event sources to choose from.

Autoscaling based on schedules

Another strategy for scaling your workloads is to schedule the scaling operations, for example in order to reduce resource consumption during off-peak hours.

Similar to event driven autoscaling, such behavior can be achieved using KEDA in conjunction with its Cron scaler. The Cron scaler allows you to define schedules (and time zones) for scaling your workloads in or out.

Scaling cluster infrastructure

If scaling workloads isn't enough to meet your needs, you can also scale your cluster infrastructure itself.

Scaling the cluster infrastructure normally means adding or removing {{< glossary_tooltip text="nodes" term_id="node" >}}. Read cluster autoscaling for more information.

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