Locality-Preserving Clustering and Discovery of Wide-Area Grid Resources

In large-scale computational or P2P grids, discovery of heterogeneous resources as a working group is crucial to achieving scalable performance. This paper presents a hierarchical cycloid overlay (HCO) architecture with resource clustering and discovery algorithms for efficient and robust resource discovery in 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 randomized probing and cluster-token forwarding algorithms. The novelty of the HCO scheme lies in low overhead, fast speed and dynamism resilience in multi-resource discovery. The paper presents the HCO framework, new performance metrics, and simulation experimental results. This HCO scheme compares favorably with other resource management 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.