A comparison of four popular heuristics for task scheduling problem in computational grid

The grid computational environment is very suit to meet the computational demands of large, diverse groups of tasks. And the task scheduling problem in it has been a research hotspot in recent years. Some heuristic methods have been used to optimize it and have got some good results. However, selecting the best one to use in a given environment remains a difficult problem, because comparisons are often clouded by different underlying assumptions in the original study of each algorithm. Some comparisons have been made to them, but some new algorithms are not included in the comparisons. So, in this paper, four popular researched algorithms recently are selected, implemented, and analyzed. The four heuristics are Genetic Algorithm, Ant Colony Optimization Algorithm, Particle Swarm Optimization Algorithm and Simulated Annealing Algorithm. The evaluations include the schedule creating time, the makespan and the mean response time. It shows that for the cases studied here, the PSO heuristic performs better in comparison to the other techniques.