Pareto simplified swarm optimization for grid-computing reliability and service makspan in grid-RMS

In a grid-computing service, Grid-RMS must generate suitable assignment combinations (execution blocks) for dependable service quality and satisfactory makespan (service time). In this paper, service reliability of a grid environment and makespan of a grid application are estimated via the universal generating function methodology and probability theory. Then, we represent a simplified swarm optimization (SSO) with the Pareto-set cluster (PC) to search the best assignment combinations in a grid environment with star topology. In terms of the task partition and distribution for a grid application, we employ a Pareto-set cluster to guide particle evolution, an elitist strategy to promote solution quality, and a simplified update mechanism to enhance the multi-objective optimization effectiveness. Finally, we assess the performance of the PC-SSO by the interactive tradeoff problem based on the analysis of four scenarios with respect to the bi-objective problem and given restrictions.