K-TAHP: A Kubernetes Load Balancing Strategy Base on TOPSIS+AHP
Rong Gao, Xiaolan Xie, Qiang Guo
Abstract
Kubernetes is an orchestration platform designed for containerized applications,allows the application provider to scale automatically to match the flfluctuating intensity of processing demand. Container cluster technology is used to encapsulate, isolate, and deploy applications, addressing the issue of low system reliability due to interlocking failures.However, after running for a long time, Kubernetes clusters often suffer from uneven system load, leading to a performance decline. To address this issue, a load balancing strategy called K-TAHP, based on TOPSIS and AHP, is proposed. This strategy takes cpu, memory, and bandwidth usage as load factors and uses K-TAHP to construct load evaluation. By employing a warning module and a migration module, it migrates high-load pods from overloaded nodes to nodes with lower loads, thus improving load balancing in the Kubernetes cluster and resolving performance degradation caused by load imbalance after prolonged cluster operation. Experimental results demonstrate that the K-TAHP load balancing strategy effectively enhances load balancing in Kubernetes clusters, addresses load imbalance issues after long-term operation, and ensures uninterrupted pod services during migration, thereby maintaining cluster performance.