Achieving Scalable Cluster System Analysis and Management with a Gossip-Based Network Service

Clusters of workstations are increasingly used for applications requiring high levels of both performance and reliability. Certain fundamental services are highly desirable to achieve these twin goals of network-based cluster system analysis and management. Among these services is the ability to detect network and node failures and the capability to efficiently determine computer and network load levels. Furthermore, the ability to allow for the distribution of administrative directives is also integral to the goal of cluster management. This paper presents a scalable approach to providing these vital support capabilities for distributed computing integrated into a cluster management system. Previous approaches to cluster management have suffered from problems of scalability and the inability to properly support heterogeneous systems in a non-proprietary fashion. This cluster management system employs gossip techniques to address the problem of scalability in network-based system management. The results of two case studies show that the cluster management system is scalable and has little adverse impact on the performance of sequential and parallel applications running on the managed system.