A Dynamic Biased Random Sampling Scheme for Scalable and Reliable Grid Networks

Abstract

The growth in computer and networking technologies over the past decades produced new type of collaborative computing environment called Grid Network. Grid is a parallel and distributed computing network system that possesses the ability to achieve a higher throughput computing by taking advantage of many computing resources available in the network. Therefore, to achieve a scalable and reliable Grid network system, the load needs to be efficiently distributed among the resources accessible on the network. In this paper, we present a distributed and scalable load balancing framework for Grid networks using biased random sampling. The generated network system is self-organized and depends only on local information for load distribution and resource discovery. We demonstrate that introducing a geographic awareness factor in the random walk sampling can reduce the effects of communication latency in the Grid network environment. Simulation results show that the generated network system provides an effective, scalable, and reliable load balancing scheme for the distributed resources available on Grid networks.