Abstract
This paper proposes a simple population based heuristic for task scheduling in heterogeneous distributed systems. The heuristic is based on a hybrid perturbation operator which combines greedy and random strategies in order to ensure local improvement of the schedules. The behaviour of the scheduling algorithm is tested for batch and online scheduling problems and is compared with other scheduling heuristics.
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References
Braun, T.D., Siegel, H.J., Beck, N., et al.: A comparison of eleven static heuristics for mapping a class of independent tasks onto heterogeneous distributed computing systems. Journal of Parallel and Distributed Computing 61(6), 810–837 (2001)
Carretero, J., Xhafa, F.: Using Genetic Algorithms for Scheduling Jobs in Large Scale Grid Applications. Journal of Technological and Economic Development - A Research Journal of Vilnius Gediminas Technical University 12(1), 11–17 (2006)
Feitelson, D.G.: Workload modeling for computer systems performance evaluation (2010), http://www.cs.huji.ac.il/~feit/wlmod/
Frincu, M.: Dynamic Scheduling Algorithm for Heterogeneous Environments with Regular Task Input from Multiple Requests. In: Abdennadher, N., Petcu, D. (eds.) GPC 2009. LNCS, vol. 5529, pp. 199–210. Springer, Heidelberg (2009)
Frincu, M., Macariu, G., Carstea, A.: Dynamic and Adaptive Workflow Execution Platform for Symbolic Computations. Pollack Periodica, Akademiai Kiado 4(1), 145–156 (2009)
Page, A.J., Keane, T.M., Naughton, T.J.: Multi-heuristic dynamic task allocation using genetic algorithms in a heterogeneus distributed system. J. Parallel Distrib. Comput. (2010), doi:10.1016/j.jpdc.2010.03.11
Ritchie, G., Levine, J.: A hybrid ant algorithm for scheduling independent jobs in heterogeneous computing environments. In: Proc. of 23rd Workshop of the UK Planning and Scheduling Special Interest Group (2004)
Page, A.J., Naughton, T.J.: Dynamic task scheduling using genetic algorithms for heterogeneous distributed computing. In: Proc. of 19th IEEE/ACM International Parallel and Distributed Processing Symposium, Denver, pp. 1530–2075 (2005)
Xhafa, F., Abraham, A.: Computational models and heuristic methods for Grid scheduling problems. Future Generation Computer Systems 26, 608–621 (2010)
Xhafa, F.: A Hybrid Evolutionary Heuristic for Job Scheduling on Computational Grids. In: Hybrid Evolutionary Algorithms. Studies in Computational Intelligence, vol. 75, pp. 269–311. Springer, Heidelberg (2007)
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Zamfirache, F., Frîncu, M., Zaharie, D. (2011). Population-Based Metaheuristics for Tasks Scheduling in Heterogeneous Distributed Systems. In: Dimov, I., Dimova, S., Kolkovska, N. (eds) Numerical Methods and Applications. NMA 2010. Lecture Notes in Computer Science, vol 6046. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-18466-6_38
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DOI: https://doi.org/10.1007/978-3-642-18466-6_38
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-18465-9
Online ISBN: 978-3-642-18466-6
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