Scheduling jobs on computational grids using a fuzzy particle swarm optimization algorithm

Grid computing is a computational framework used to meet growing computational demands. This paper introduces a novel approach based on Particle Swarm Optimization (PSO) for scheduling jobs on computational grids. The representations of the position and velocity of the particles in conventional PSO is extended from the real vectors to fuzzy matrices. The proposed approach is to dynamically generate an optimal schedule so as to complete the tasks within a minimum period of time as well as utilizing the resources in an efficient way. We evaluate the performance of the proposed PSO algorithm with a Genetic Algorithm (GA) and Simulated Annealing (SA) approach. Empirical results illustrate that an important advantage of the PSO algorithm is its speed of convergence and the ability to obtain faster and feasible schedules.