A knowledge-based ant colony optimization for a grid workflow scheduling problem
ICSI'10 Proceedings of the First international conference on Advances in Swarm Intelligence - Volume Part I
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Task scheduling is one of the core problems in grid computing. How to accomplish tasks quickly and efficiently to meet users' requirements has always been being a hot issue in the fileds of theoretical and applied research. The algorithm presented in this paper is based on the ant colony algorithm and genetic algorithm. It realizes scheduling optimization for grid tasks by studying and exploring optimization grouping of four parameters in ant colony algorithm with the quick global search randomly in genetic algorithm. In order to evaluate the performance, we design a simulating program to validate it after finishing the Gridsim study. Simulation results show that optimization grouping of parameters not only improve the efficiency of task distributing and scheduling but also balance the load. At last, further research direction is bringing forward.