Difference between Cluster Autoscaler & Karpenter
Tags: kubernetes, aws, beginners, opensource
Published: 2022-10-27 15:50:41

## Cluster Autoscaler

**Cluster Autoscaler** is a Kubernetes utility that increases or decreases the size of a Kubernetes cluster (by adding or removing nodes), based on the presence of pending pods and node utilization metrics.

Automatically resize the Kubernetes cluster when one of the following conditions is met:

1. There are pods that cannot run on the cluster due to insufficient resources.
2. There are nodes in the cluster that have been underused for a long period of time and their pods can be placed on other existing nodes.

A Kubernetes node autoscaling solution is a tool that automatically scales the Kubernetes cluster based on the demands of our workloads. Therefore, there is no need to manually create (or delete) a new Kubernetes node every time we need it.

> Karpenter automatically provides new nodes in response to non-programmable pods. It does this by observing events within the Kubernetes cluster and then sending commands to the underlying cloud provider. It is designed to work with any Kubernetes cluster in any environment.

## Architecture of Cluster Autoscaler

- The cluster autoscaler looks for pods that cannot be scheduled and nodes that are underutilized. 
- It then simulates adding or removing nodes before applying the change to your cluster. 
- The AWS cloud provider implementation in Cluster Autoscaler controls the _.DesiredReplicas_ field of your EC2 Auto Scaling groups. 
- The Kubernetes cluster autoscaler automatically adjusts the number of nodes in your cluster when pods fail or are rescheduled to other nodes. 
- The cluster autoscaler is typically installed as a deployment on your cluster.

[![image.png](https://cdn.hashnode.com/res/hashnode/image/upload/v1660067686701/S5bRYexKf.png)](https://aws.github.io/aws-eks-best-practices/cluster-autoscaling/)

## Architecture of Karpenter

Karpenter works with the Kubernetes scheduler observing incoming pods throughout the lifetime of the cluster. It starts or stops nodes to optimize application availability and cluster utilization. When there is enough capacity in the cluster, the Kubernetes scheduler will place the incoming pods as usual.

When pods are started that cannot be scheduled using existing cluster capacity, Karpenter bypasses the Kubernetes scheduler and works directly with your provider's compute service to start the minimum compute resources needed in those pods and associate pods with provisioning nodes.

When pods are removed , Karpenter looks for opportunities to terminate underutilized nodes.

[![image.png](https://cdn.hashnode.com/res/hashnode/image/upload/v1660067427610/5ibQrI-vF.png)](https://aws.amazon.com/blogs/aws/introducing-karpenter-an-open-source-high-performance-kubernetes-cluster-autoscaler/)

Karpenter claims to offer the following enhancements:

### Designed to handle the full flexibility of the cloud

Karpenter has the ability to efficiently address the full range of instance types available through AWS. Cluster Autoscaler was not originally built with the flexibility to manage hundreds of instance types, zones and purchasing options.

### Node provisioning without groups

Karpenter manages each instance directly, without using additional orchestration mechanisms such as node groups. This allows you to retry in milliseconds instead of minutes when capacity is not available. It also allows Karpenter to take advantage of different types of instances, Availability Zones and purchasing options without creating hundreds of node groups.

### Applying Planning

Cluster Autoscaler does not associate pods with nodes it creates. Instead, it relies on the kube scheduler to make the same scheduling decision after the node is online. The kubelet does not need to wait for the scheduler or the node to prepare. It can immediately start preparing the container runtime environment, including pre-fetching the image. This can reduce node startup latency by a few seconds.



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