Kubernetes right-sizing with metrics-driven GitOps automation

Kubernetes right-sizing with metrics-driven GitOps automation Understanding the challenges of resource management in Amazon EKS The impact of inefficient resource management Existing solutions for Kubernetes resource management How the proposed solution addresses the challenges Solution overview Workflow overview Key architectural considerations Walkthrough Prerequisites Implementing a GitOps-driven automation for resource optimization GitOps principle Setting up the recommendation generator Environment and metrics source Local or External Installation Automating the GitOps workflow Cleaning up Conclusion About the authors Efficient resource allocation in Kubernetes is essential for optimizing application performance and controlling costs. In Amazon Elastic Kubernetes Service (Amazon EKS) , managing resource requests and limits manually can be challenging and error-prone. This post introduces an automated, and GitOps-driven approach to resource optimization using Amazon Web Services (AWS) services such as Amazon Managed Service for Prometheus and Amazon Bedrock. This approach is particularly beneficial for users who prefer non-intrusive methods for resource optimization. Understanding resource management in Kubernetes is crucial for optimal cluster performance. When deploying pods, the Kubernetes scheduler evaluates resource requests to find suitable nodes that can accommodate the specified CPU and memory requirements. These requests act as the minimum guaranteed resources for the pod, while limits serve as upper bounds to prevent any single pod from monopolizing node resources. Over-provisioning and under-provisioning of resources in Kubernetes can lead to increased costs and performance issues. Striking the right balance is essential for optimal resource usage. In a shared environment, one pod consuming excessive resources can degrade the performance of others on the same node. Applications with fluctuating resource demands can be challenging to manage. Without adaptive resource allocation strategies, these workloads may experience performance degradation or resource waste.