Abstract
A grid computing environment is a parallel and distributed system that brings together various computing capacities to solve large computation problems. Task scheduling is a critical issue for grid computing, which maps tasks onto a parallel and distributed system for achieving good performance in terms of minimizing the overall execution time. This paper presents a genetic algorithm to solve this problem for improving the existing genetic algorithm with two main ideas: a new initialization strategy is introduced to generate the first population of chromosomes and the good characteristics of found solutions are preserved for new generations. Our proposed algorithm is implemented and evaluated using a set of well-known applications in our specific-defined system environment. The experimental results show that the proposed algorithm outperforms other algorithms within several parameter settings.
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References
Culler, D., Singh, J., Gupta, A.: Parallel Computer Architecture: A Hardware/Software Approach. Morgan Kaufmann Publisher, San Francisco (1998)
Choudhury, P., Chakrabarti, P.P., Kumar, R.: Online Scheduling of Dynamic Task Graphs with Communication and Contention for Multiprocessors. IEEE Trans. on Parallel and Distributed Systems 23(1), 126–133 (2012)
Falzon, G., Li, M.: Enhancing Genetic Algorithms for Dependent Job Scheduling in Grid Computing Environments. Journal of Supercomputing 62(1), 290–314 (2012)
Han, Q., Yu, L., Zheng, W., Cheng, N., Niu, X.: A Novel QKD Network Routing Algorithm Based on Optical-Path-Switching. Journal of Information Hiding and Multimedia Signal Processing 5(1), 13–19 (2014)
Holland, J.H.: Adaptation in Natural and Artificial Systems. University of Michigan Press, Ann Arbor (1975)
Hwang, K.: Advanced Computer Architecture: Parallelism, Scalability, Programmability. McGraw-Hill, Inc., New York (1993)
Kwok, Y.K., Ahmad, I.: Efficient Scheduling of Arbitrary Task Graphs to Multiprocessors Using a Parallel Genetic Algorithm. Journal of Parallel and Distributed Computing 47(1), 58 (1997)
Kwok, Y.K., Ahmad, I.: Static Scheduling Algorithms for Allocating Directed Task Graphs to Multiprocessors. ACM Computing Surveys 31(4), 406–471 (1999)
Loukhaoukha, K.: On the Security of Digital Watermarking Scheme Based on Singular Value Decomposition and Tiny Genetic Algorithm. Journal of Information Hiding and Multimedia Signal Processing 3(2), 135–141 (2012)
Omara, F.A., Arafa, M.M.: Genetic algorithms for task scheduling problem. Journal of Parallel and Distributed Computing 70(1), 13–22 (2010)
Topcuoglu, H., Hariri, S., Wu, M.-Y.: Performance-effective and Low-complexity Task Scheduling for Heterogeneous Computing. IEEE Trans. on Parallel and Distributed Systems 13(3), 260–274 (2002)
Wu, A.S., Yu, H., Jin, S., Lin, K., Schiavone, G.: An Incremental Genetic Algorithm Approach to Multiprocessor Scheduling. IEEE Trans. on Parallel and Distributed Systems 15(9), 824–834 (2004)
Wen, Y., Xu, H., Yang, J.: A Heuristic-based Hybrid Genetic-variable Neighborhood Search Algorithm for Task Scheduling in Heterogeneous Multiprocessor System. Information Sciences 181(3), 567–581 (2011)
Yu, H.: Optimizing Task Schedules using an Artificial Immune System Approach. In: Proceedings of the 10th Annual Conference on Genetic and Evolutionary Computation, pp. 151–158 (2008)
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© 2014 Springer International Publishing Switzerland
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Jiang, YS., Chen, WM. (2014). Task Scheduling in Grid Computing Environments. In: Pan, JS., Krömer, P., Snášel, V. (eds) Genetic and Evolutionary Computing. Advances in Intelligent Systems and Computing, vol 238. Springer, Cham. https://doi.org/10.1007/978-3-319-01796-9_3
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DOI: https://doi.org/10.1007/978-3-319-01796-9_3
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-01795-2
Online ISBN: 978-3-319-01796-9
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