Heuristic-Based Scheduling to Maximize Throughput of Data-Intensive Grid Applications
نویسندگان
چکیده
Job scheduling in data grids must consider not only computation loads at each grid node but also the distributions of data required by each job. Furthermore, recent trends in grid applications emphasize high throughput more than high performance. In this paper, we propose a centralized scheduling scheme, which uses a scheduling heuristic called Maximum Residual Resource (MRR) that targets high throughput for data grid applications. MRR takes into account both computation times and data resources available at a site when evaluating the site as a candidate for a given job. It tries to minimize the number of job executions that require remote data transfer, meanwhile maintaining good load balance among grid sites. We have analyzed the performance potentials of MRR, and have developed a simulator to evaluate it with typical grid configurations. Our results show that MRR brings significant performance improvements over existing online and batch heuristics like MCT, Min-min and Max-min.
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