A load-rebalance PSO heuristic for task matching in heterogeneous computing systems

The idea of utilizing nature inspired algorithms to find optimal solutions to various real world NP complete optimization problems has been extensively explored by researchers. One such problem is task matching problem in heterogeneous distributed computing environments like Grid and Cloud. Researchers have explored Swarm Intelligence algorithm, Particle Swarm Optimization (PSO), to find optimal solution for task matching problem. In this study, we investigate the effectiveness of smallest position value (SPV) technique in mapping continuous version of PSO algorithm to the task matching problem in a heterogeneous computing environment. We show that the task matching generated by this technique will result in in-efficient resource utilization. Thus, we present a novel load rebalance based particle swarm optimization heuristic (PSO-LR) for efficient load distribution among available compute nodes even in heterogeneous computing environments.