Research on Monitoring for Heterogeneous Kubernetes Clusters
Guifa Sun, Jianpeng Sun, Xiguo Xie · 2025
Effectively monitoring the real-time status of resources on each node within heterogeneous clusters is a crucial aspect of cluster management. Cloud computing clusters typically rely on management solutions based on the Kubernetes platform and Docker container virtualization. As the deployment scale of cloud computing centers continues to expand and the diversity of hardware resources increases, the default, simplified cluster resource monitoring and management mechanism of Kubernetes is no longer adequate to address the complex monitoring and management challenges posed by heterogeneous clusters. The default monitoring mechanism of Kubernetes only considers monitoring CPU utilization and memory usage states of nodes, making it difficult to meet the comprehensive monitoring and management needs of current complex heterogeneous clusters. We propose a novel cluster monitoring and management architecture aimed at extending the monitoring and management mechanisms of Kubernetes to better accommodate the proactive monitoring, alerting, and management requirements of large-scale and complex heterogeneous clusters. This architecture not only expands the monitoring methods for heterogeneous resources on nodes and enhances the visualization of resource monitoring but also introduces alerting and alert handling modules, effectively fulfilling cluster anomaly alerting and handling tasks. It provides a new perspective and solution for the monitoring and management of large-scale heterogeneous clusters in the future.