A company runs containerized applications on Amazon Elastic Kubernetes Service (Amazon EKS) using the Kubernetes Horizontal Pod Autoscaler. The workload fluctuates throughout the day. The number of nodes does not automatically scale out when the cluster nodes reach maximum capacity, causing performance issues. Which solution resolves this with the LEAST administrative overhead?

The Kubernetes Cluster Autoscaler automatically adjusts the number of nodes in your EKS cluster based on pending pods and node utilization, directly addressing the issue of nodes not scaling out when capacity is reached. This is a native Kubernetes solution designed for this exact purpose, offering the least administrative overhead as it integrates seamlessly with EKS and Auto Scaling groups. Scaling out nodes by monitoring memory usage is a manual or custom scripting approach, increasing overhead. Using an AWS Lambda function for resizing is also a custom solution requiring development and maintenance. While an Amazon EC2 Auto Scaling group is used by the Cluster Autoscaler, simply using it alone doesn't provide the Kubernetes-aware scaling logic needed to react to pending pods.