Abstract
This paper presents a new approach to scheduling jobs on a service Grid using a genetic algorithm (GA). A fitness function is defined to minimize the average execution time of scheduling N jobs to M(≤ N) machines on the Grid. Two models are proposed to predict the execution time of a single job or multiple jobs on each machine with varied system load. The single service type model is used to schedule jobs of one single service to a machine while the multiple service types model schedules jobs of multiple services to a machine. The predicted execution times from these models are used as input to the genetic algorithm to schedule N jobs to M machines on the Grid. Experiments on a small Grid of four machines have shown a significant reduction of the average execution time by the new job scheduling approach.
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Gao, Y., Rong, H., Tong, F., Luo, Z., Huang, J. (2004). Adaptive Job Scheduling for a Service Grid Using a Genetic Algorithm. In: Li, M., Sun, XH., Deng, Q., Ni, J. (eds) Grid and Cooperative Computing. GCC 2003. Lecture Notes in Computer Science, vol 3033. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24680-0_9
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DOI: https://doi.org/10.1007/978-3-540-24680-0_9
Publisher Name: Springer, Berlin, Heidelberg
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