Optimizing Amazon EKS Autoscaling: A Deep Dive into Cluster Autoscaler vs. Karpenter
Amazon Elastic Kubernetes Service (EKS) offers a managed and scalable Kubernetes environment on AWS. For efficient use of resources and cost-effectiveness, autoscaling is an important feature that dynamically scales the infrastructure according to workload requirements.
In this blog, we will discuss two of the most important solutions for Amazon EKS autoscaling: Cluster Autoscaler and Karpenter.
What is Cluster Autoscaler?
Cluster Autoscaler is an open-source tool, owned by the Kubernetes community under SIG Autoscaling, that maintains clusters of sufficient nodes to schedule pods without going idle. It keeps an eye out for unschedulable pods (i.e., those unable to be placed because resources are not enough) and underutilized nodes and then tests adding or deleting nodes before implementing changes.
What is Karpenter?
Karpenter is an open-source, high-throughput Kubernetes cluster autoscaler built by AWS. Released for production in November 2021, it is designed to enhance application availability and cluster utilization by provisioning right-sized compute resources (e.g., Amazon EC2 instances) based on fluctuating application load, typically in less than a minute.
How Does It Work in EKS?
In the Amazon EKS environment, Cluster Autoscaler works in conjunction with EC2 Auto Scaling Groups (ASGs). As it identifies an increased need for capacity, it raises the desired capacity of the ASG, causing new EC2 instances to be launched. If nodes happen to be idle, it reduces the desired capacity, resulting in instance termination. All this is controlled by configuration parameters like scan intervals and scale-down delays that can be fine-tuned for performance.
Key operational aspects are:
- It is run as a Kubernetes Deployment, with leader election for high availability, though it runs with one replica.
- Version compatibility with the cluster is essential since cross-version support is not supported.
- Auto Discovery is advised for simplicity of deployment, unless sophisticated use cases necessitate manual configuration.
Karpenter monitors the total resource requests of unschedulable pods and decides to add and remove nodes to reduce scheduling latencies and infrastructure expense. It integrates well with EKS, specifically via EKS Auto Mode, which uses Karpenter for automated compute scaling. It provides just-in-time compute resources, such as compute, storage, acceleration, and scheduling, according to workload demands.
Major features are:
- It supports any conformant Kubernetes cluster, with full support for EKS.
- It needs to be installed, set up, and operated by customers, although AWS supports unmodified ones on compatible EKS clusters with technical assistance.
- It is distributed under the Apache License 2.0, inviting community contributions.
Comparing Cluster Autoscaler and Karpenter
Cluster Autoscaler and Karpenter vary in a number of significant aspects.
- On the aspect of scaling speed, Cluster Autoscaler relies on Auto Scaling Groups and is therefore quite slower than Karpenter, which provisions nodes directly through EC2, cutting down on latency.
- On instance choice, Cluster Autoscaler uses pre-defined node groups, whereas Karpenter chooses the most suitable instance type dynamically depending on workload requirements.
- From a cost efficiency perspective, Cluster Autoscaler has limited cost optimizations, while Karpenter leads by utilizing Spot Instances and dynamically provisioning cost-effective resources.
- Moreover, Cluster Autoscaler involves dealing with ASG configurations, thus making it more complicated, while Karpenter has a Kubernetes-native API for an easy configuration experience.
Implementing Autoscaling using AWS Consulting Services
Businesses wanting to streamline their Amazon EKS clusters can benefit from collaboration with an AWS Consulting Company. AWS Consulting Services assist businesses in designing, deploying, and optimizing EKS autoscaling policies to attain maximum performance.
Primary Advantages of AWS Consulting Services
- Professional Guidance: Obtain expert recommendations for deciding between Cluster Autoscaler and Karpenter.
- Optimization of Performance: Ensure that your workloads scale efficiently without unnecessary expense.
- Security Best Practices: Leverage autoscaling with comprehensive security and compliance practices.
- Ongoing Support: Have 24/7 monitoring and troubleshooting assistance.
Final Thoughts
Both Karpenter and Cluster Autoscaler provide stable solutions for EKS autoscaling, with Karpenter best for high-performance, AWS-native requirements, particularly via EKS Auto Mode. Cluster Autoscaler is still a viable option for multi-cloud scenarios or where greater community support is desired. They can be switched between, as shown by the migration guides of Karpenter, and the selection is based on individual organizational requirements.
