An Adaptive Scoring Job Scheduling algorithm for grid computing
Information Sciences: an International Journal
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Grid computing is a new research subject. The computing power and storage space of grids is collected from heterogeneous or homogeneous resources in order to support complicated computing problems. Job scheduling in computing grid is a very important problem. Current scientific applications become more complex and need huge computing power and storage space. It may take a very long time to complete a complicated job. However, to utilize grids, we need an efficient job scheduling algorithm to assign jobs to resources in grids. In this paper, we propose a Balanced Ant Colony Optimization (BACO) algorithm for job scheduling in the Grid environment. There are two schemes introduced in this paper regarding local and global pheromone update. The main contributions of our work are to balance the entire system load and minimize the makespan of a given set of jobs. Compared with the other proposed algorithms, BACO can outperform them according to the experimental results.