Job Scheduling on Computational Grids Using Fuzzy Particle Swarm Optimization
基于模糊粒子群优化的计算网格工作调度算法

Computational Grids are the computing framework to meet the growing computational demands, which re-organizes the resources of many computers in the networks to solve a large of complex problems. Essential grid services contain intelli- gent functional mechanism for discovery, publishing of resources as well as scheduling, submission and monitoring of jobs. The paper introduces a novel approach based on fuzzy Particle Swarm Optimization (PSO) for scheduling jobs on computational grids. Our approach is to dynamically generate an optimal schedule so as to complete the tasks in a minimum period of time as well as utilizing the resources in an efficient way. We evaluate the performance of our proposed approach with a direct Genetic Algorithm (GA), Simulated Annealing (SA) and Ant Colony Algorithm (ACO) approach. The results illustrated the poten- this of our approach, especially on the balance between time and precision.