| The idea of utilizing nature inspired algorithms to find near optimal solutions to various real world NP complete optimization problems has been extensively explored by researchers. One such problem is the task matching problem in large heterogeneous distributed computing environments like Grids and Clouds. Researchers have explored Particle Swarm Optimization(PSO), which is branch of swarm intelligence, to find a near optimal solution for the task matching problem.;In this work, I investigated the effectiveness of the smallest position value (SPV) technique in mapping the continuous version of the PSO algorithm to the task matching problem in a heterogeneous computing environment. The experimental evaluation demonstrated that the task matching generated by this technique will result in an imbalanced load distribution. In this work, I have therefore also designed a load-rebalance PSO heuristic (PSO-LR) that results in minimization of makespan and balanced utilization of the available compute nodes even in heterogeneous computing environments. |
