Resource Optimization and Cost Management in Kubernetes Clusters: A Framework for Container Orchestration Platforms

Abstract

The use of container orchestration platforms has changed the application deployment patterns, but cost management has remained an operational issue. Kubernetes environments are often resource inefficient due to over-provisioning, node fragmentation, workload imbalance, too much storage allocation, and high network transfer costs. This framework introduces systematic approaches to cost optimization in Kubernetes clusters, leveraging built-in visibility strategies, configuration tuning, and workload-based optimization strategies. The proposed approach integrates a cluster telemetry system and allocation tracking to create end-to-end cost visibility for containerized infrastructure. This optimization of resources can be achieved by right-sizing using utilization signals, vertical scaling policy, horizontal scaling policy, and bin-packing efficiency-enhancing techniques that minimize idle capacity by using dynamic provisioning policies. Mixed-instance node fleet deployment, the adoption of spot instances with disruption-aware scheduling, and long-term capacity planning based on savings plans are among the compute cost reduction methods.   These strategies and practical tips help companies using Kubernetes to consistently save on infrastructure costs while ensuring that their workloads remain reliable, perform well, and meet compliance requirements during production.