MerKury: Adaptive Resource Allocation to Enhance the Kubernetes Performance for Large-Scale Clusters

As a prevalent paradigm of modern web applications, cloud computing has experienced a surge in adoption. The deployment of vast and various workloads encapsulated within containers has become ubiquitous across cloud platforms, imposing substantial demands on the supporting infrastructure. However, Kubernetes (k8s), the de-facto standard for container orchestration, struggles with low scheduling throughput and high latency in large-scale clusters. The primary challenges are identified as excessive loads of read requests and resource contention among co-located components. In response to these challenges, in this paper, we present MerKury, a lightweight framework to enhance the Kubernetes performance for large-scale clusters. It employs a dual strategy: first, it preprocesses specific requests to alleviate unnecessary load, and second, it introduces an adaptive resource allocation algorithm to mitigate resource contention. Evaluations under different scenarios of varying cluster scale have demonstrated that MerKury notably augments cluster scheduling throughput up to 16.4× and reduces request latency by up to 39.3%, outperforming vanilla Kubernetes and baseline resource allocation methods. CCS CONCEPTS • Computer systems organization → Cloud computing.