Locality-Preserving Clustering and Discovery of Resources in Wide-Area Distributed Computational Grids

Abstract

In large-scale computational Grids, discovery of heterogeneous resources as a working group is crucial to achieving scalable performance. This paper presents a resource management scheme including a hierarchical cycloid overlay architecture, resource clustering and discovery algorithms for wide-area distributed Grid systems. We establish program/data locality by clustering resources based on their physical proximity and functional matching with user applications. We further develop dynamism-resilient resource management algorithm, cluster-token forwarding algorithm, and deadline-driven resource management algorithms. The advantage of the proposed scheme lies in low overhead, fast and dynamism-resilient multiresource discovery. The paper presents the scheme, new performance metrics, and experimental simulation results. This scheme compares favorably with other resource discovery methods in static and dynamic Grid applications. In particular, it supports efficient resource clustering, reduces communications cost, and enhances resource discovery success rate in promoting large-scale distributed supercomputing applications.