Abstract
Both parallel and distributed network environment systems play a vital role in the improvement of high performance computing. The primary concern when analyzing these systems is multiprocessor task scheduling. This paper addresses the problem of efficient multiprocessor task scheduling. A multiprocessor task scheduling problem is represented as directed acyclic task graph (DAG), for execution on multiprocessors with communication costs. In this paper we have investigated the effectiveness of a proposed paradigm based on genetic algorithms (GAs). GAs is a class of robust stochastic search algorithms for various combinatorial optimization problems. We have designed a GA based encoding mechanism that uses multi-chromosome encoding scheme. The implementation of the technique is simple. The performance of the designed algorithm has been tested on a variety of multiprocessor systems both heterogeneous as well as homogeneous.
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References
Baskiyar, S., SaiRanga, P.C.: Scheduling Directed Acyclic Task Graphs on Heterogeneous Network of Workstations to Minimize Schedule Length. In: Proceedings of the IEEE International Conference on Parallel Processing Workshops, ICPPW 2003 (2003)
Blythe, J., Jain, S., Deelman, E., Gil, Y., Vahi, K., Mandal, A., Kennedy, K.: Task scheduling strategies for workflow-based applications in grids. In: CCGRID, pp. 759–767 (2005)
Gen, M., Cheng, R.: Genetic algorithm and engineering optimization. Wiley, NewYork (2000)
Hou, E.S.H., Ansari, N., Hong, R.: A genetic algorithm for multiprocessor scheduling. IEEE Transactions on Parallel and Distributed Systems 5(2), 113–120 (1994)
Hwang, R., Gen, M.: Multiprocessor scheduling using genetic algorithm with priority-based coding. In: Proceedings of IEEJ Conference on Electronics, Information and Systems (2004)
Hwang, R., Gen, M., Katayama, H.: A comparison of multiprocessor task scheduling algorithms with communication costs. Computers and Operations Research 35, 976–993 (2008)
Ilavarasan, E., Thambidurai, P., Mahilmannan: Performance Effective Task Scheduling Algorithm for Heterogeneous Computing System. In: Proceedings of the 4th International Symposium on Parallel and Distributed Computing, ISPDC 2005 (2005)
Kwok, Y.K., Ahmad, I.: Static Scheduling algorithms for allocating directed task graphs to multiprocessors. ACM Comput. Surv. 31(4), 406–471 (1999)
EI-Rewini, H., Lewis, T.G., Ali, H.H.: Task Scheduling in parallel and distributed Systems. Prentice Hall, Englewood Cliffs (1994)
Topcuoglu, H., Hariri, S., Wu, M.-Y.: Performance-Effective and Low-Complexity Task Scheduling for Heterogenous Computing. IEEE Transactions on Parallel and Distributed Systems 13(3) (March 2002)
Tsujimura, Y., Gen, M.: Genetic algorithms for solving multiprocessor scheduling problems. In: Simulated Evolution and Learning, pp. 106–115. Springer, Heidelberg (1995)
Yang, J., Ma, X., Hou, C., Yao, Z.: A Static Multiprocessor Scheduling Algorithm for Arbitrary Directed Task Graphs in Uncertain Environments, pp. 18–29. Springer, Berlin (2008)
Bohler, M., Moore, F., Pan, Y.: Improved Multiprocessor task Scheduling using Genetic Algorithm. In: Proceeding of Midwest Artificial Intelligence and Cognitive Science Conference (April 2009)
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Panwar, P., Lal, A.K., Singh, J. (2012). A Genetic Algorithm Based Technique for Efficient Scheduling of Tasks on Multiprocessor System. In: Deep, K., Nagar, A., Pant, M., Bansal, J. (eds) Proceedings of the International Conference on Soft Computing for Problem Solving (SocProS 2011) December 20-22, 2011. Advances in Intelligent and Soft Computing, vol 131. Springer, New Delhi. https://doi.org/10.1007/978-81-322-0491-6_84
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DOI: https://doi.org/10.1007/978-81-322-0491-6_84
Publisher Name: Springer, New Delhi
Print ISBN: 978-81-322-0490-9
Online ISBN: 978-81-322-0491-6
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