#Horizontal Pod Autoscaler API VersionsĪPI version autoscaling/v1 is the stable and default version this version of API only supports CPU utilization-based autoscaling.Īutoscaling/v2beta2 version of the API brings usage of multiple metrics, custom and external metrics support. The Vertical Pod Scaler is responsible for adjusting requests and limits on CPU and memory. Custom metrics and external metrics are supported, so they can be used by another autoscaler within the cluster as well. It can affect replication controllers, deployment, replica sets, or stateful sets. The Horizontal Pod Autoscaler scales the number of pods of an application based on the resource metrics such as CPU or memory usage or custom metrics. ![]() On the other side, if nodes do not have any workloads running, they can be terminated. If a pod cannot be scheduled due to the resource requests, then a node will be created to accommodate. The Cluster Autoscaler scales the nodes up/down depending on the pod’s CPU and memory requests. Kubernetes brings three types of auto-scaling to the table: Scale down more than required and your application will not be performant. ![]() Scale up more than needed, and you will have unused resources which you must pay for. One of the most powerful features of Kubernetes is autoscaling, as it’s vital that we find the correct balance when scaling resources in our infrastructures.
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