Amazon EKS에서 고급 Kubernetes 컨트롤 플레인 구성 기능 도입

Customers running large Amazon Elastic Kubernetes Service (Amazon EKS) clusters want to optimize pod placement strategies for higher resource utilization and lower compute costs. Teams running high-churn batch, continuous integration and continuous delivery (CI/CD), and artificial intelligence and machine learning (AI/ML) workloads want to customize resource lifecycle management settings such as event retention duration to reduce etcd storage pressure. And customers migrating from self-managed Kubernetes want to preserve scheduler settings they have tuned over the years.

Previously, many of these needs meant building and maintaining your own solution, adding operational overhead and maintenance burden. Today, Amazon EKS introduces advanced Kubernetes control plane configuration. You can now set parameters on the API server, scheduler, and controller manager directly in Amazon EKS. You continue to receive the same Amazon EKS availability and performance characteristics.

In this post, we walk through the feature, review the supported parameters, and then get hands-on with two walkthroughs: first, we configure pod placement strategies using the MostAllocated scoring strategy to prefer resource utilization over the default spreading behavior. Then, we configure event retention duration to balance debugging capabilities with storage efficiency. With the scheduler customization, you can configure pod placement strategies to control how nodes are scored for pod scheduling.

By default, the Kubernetes scheduler uses the LeastAllocated scoring strategy, which prefers nodes with lower resource allocation to favor availability. The MostAllocated scoring strategy prefers nodes with higher existing allocation, optimizing resource utilization. By packing pods onto nodes that are already well utilized, the MostAllocated strategy runs the same workloads on fewer nodes, which reduces compute costs and leaves fewer partially idle instances in your cluster. The controller manager configuration gives you control over how frequently the Horizontal Pod Autoscaler (HPA) evaluates scaling decisions.