Multi-objective Workflow Grid Scheduling Based on Discrete Particle Swarm Optimization

Grid computing infrastructure emerged as a next generation of high performance computing by providing availability of vast heterogenous resources. In the dynamic envirnment of grid, a schedling decision is still challenging. In this paper, we present efficient scheduling scheme for workflow grid based on discrete particle swarm optimization. We attempt to create an optimized schedule by considering two conflicting objectives, namely the execution time (makespan) and total cost, for workflow execution. Multiple solutions have been produced using non dominated sort particle swarm optimization (NSPSO) [13]. Moreover, the selection of a solution out of multiple solutions has been left to the user. The effectiveness of the used algorithm is demostrated by comparing it with well known genetic algorithm NSGA-II. Simulation analysis manifests that NSPSO is able to find set of optimal solutions with better convergence and uniform diversity in small computation overhead.