A Min-Min Max-Min Selective Algorithm for Grid Task Scheduling

Today, the high cost of supercomputers in the one hand and the need for large-scale computational resources on the other hand, has led to use network of computational resources known as Grid. Numerous research groups in universities, research labs, and industries around the world are now working on a type of Grid called Computational Grids that enable aggregation of distributed resources for solving large-scale data intensive problems in science, engineering, and commerce. Several institutions and universities have started research and teaching programs on Grid computing as part of their parallel and distributed computing curriculum. To better use tremendous capabilities of this distributed system, effective and efficient scheduling algorithms are needed. In this paper, we introduce a new scheduling algorithm based on two conventional scheduling algorithms, Min-Min and Max-Min, to use their cons and at the same time, cover their pros. It selects between the two algorithms based on standard deviation of the expected completion time of tasks on resources. We evaluate our scheduling heuristic, the Selective algorithm, within a grid simulator called GridSim. We also compared our approach to its two basic heuristics. The experimental results show that the new heuristic can lead to significant performance gain for a variety of scenarios.