Abstract
In this paper we propose an efficient parallel job scheduling algorithm for a grid environment. The model implies two stage scheduling. At the first stage, algorithm allocates jobs to the suitable machines, where at the second stage jobs are independently scheduled on each machine. Allocation of jobs on the first stage of the algorithm is performed with use of a relatively new evolutionary algorithm called Generalized Extremal Optimization (GEO). GEO is inspired by a simple coevolutionary model known as Bak-Sneppen model. Scheduling on the second stage is performed by some proposed heuristic. We compare GEO-based scheduling algorithm applied on the first stage with Genetic Algorithm (GA)-based scheduling algorithm. Experimental results show that the GEO, despite of its simplicity, outperforms the GA algorithm in all range of scheduling instances.
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Switalski, P., Seredynski, F. (2011). An Efficient Evolutionary Scheduling Algorithm for Parallel Job Model in Grid Environment. In: Malyshkin, V. (eds) Parallel Computing Technologies. PaCT 2011. Lecture Notes in Computer Science, vol 6873. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23178-0_30
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DOI: https://doi.org/10.1007/978-3-642-23178-0_30
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