Abstract—one of the open issues in grid computing is efficient job scheduling. Job scheduling is known to be NP-complete, therefore the use of non-heuristics is the de facto approach in order to cope in practice with its difficulty. In this paper, we propose a modified artificial fish swarm algorithm (MAFSA) for job scheduling. The basic idea of AFSA is to imitate the fish behaviors such as preying, swarming, and following with local search of fish individual for reaching the global optimum. The results show that our method is insensitive to initial values, has a strong robustness and has the faster convergence speed and better estimation precision than the estimation method by Genetic Algorithm (GA) and simulated annealing (SA).
Index Terms—AFSA, GA, Grid computing, Job scheduling, SA.
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Cite: Saeed Farzi, "Efficient Job Scheduling in Grid Computing with Modified Artificial Fish Swarm Algorithm,"
International Journal of Computer Theory and Engineering vol. 1, no. 1, pp. 13-18, 2009.
