A Fuzzy Differential Evolution Algorithm for Job Scheduling on Computational Grids

  IJCTT-book-cover
 
International Journal of Computer Trends and Technology (IJCTT)          
 
© 2014 by IJCTT Journal
Volume-13 Number-2
Year of Publication : 2014
Authors : Ch.Srinivasa Rao , Dr.B.Raveendra Babu
DOI :  10.14445/22312803/IJCTT-V13P116

MLA

Ch.Srinivasa Rao , Dr.B.Raveendra Babu. "A Fuzzy Differential Evolution Algorithm for Job Scheduling on Computational Grids". International Journal of Computer Trends and Technology (IJCTT) V13(2):72-77, July 2014. ISSN:2231-2803. www.ijcttjournal.org. Published by Seventh Sense Research Group.

Abstract -
Grid computing is the recently growing area of computing that share data, storage, computing across geographically dispersed area. This paper proposes a novel fuzzy approach using Differential Evolution (DE) for scheduling jobs oncomputational grids. The fuzzy based DE generatesan optimal plan to complete the jobs within a minimum period of time. We evaluate the performance of the proposed fuzzy based DE algorithm with GeneticAlgorithm (GA), Simulated Annealing (SA), Differential Evolution and fuzzy PSO. Experimental results have shown that the new algorithm produces more optimal solutions for the job scheduling problems compared to other algorithms.

References
[1] Foster I, Kesselman C (eds.), The Grid: Blueprint for a Future Computing Infrastructure. Morgan Kaufmann: San Francisco, CA, 1999.
[2] KhushbooYadav, Deepika Jindal, RamandeepSingh, “Job Scheduling in Grid Computing”, International Journal of Computer Applications, Vol 69, No.22, 2013, pp 13-16.
[3] Storn, R and Price, K.V., 1995. Differential evolution: A simple and efficient adaptive scheme for global optimization over continuous spaces, ICSI, USA, Tech. Rep. TR-95-012, [Online]. Available: http://icsi.berkeley.edu/storn/litera.html
[4] Storn, R., K. Price., 1997 “Differential evolution: A simple and efficient heuristic for global optimization over continuous spaces, J. Global Optimization, vol. 11, no. 4, pp. 341–359.
[5] Price, K.V., 1997. “Differential evolution vs. the functions of the 2nd ICEO”, in Proc. IEEE Int. Conf. Evol. Comput., pp. 153–157.
[6] Price, K.V., Storn, R., 1997. Differential evolution: A simple evolution strategy for fast optimization, Dr. Dobb’s J., vol. 22, no. 4, pp. 18–24.
[7] Price, K.V., 1999. “An introduction to differential evolution, in New Ideas in Optimization”, D. Corne, M. Dorigo, and V. Glover, Eds. London, U.K.: McGraw-Hill, pp. 79–108.
[8] Swagatam Das, Ajith Abraham and AmitKonar, 2009 “Metaheuristic Clustering”, Springer-Verlag Berlin Heidelberg, ISBN 978-3-540-92172-1, ISSN 1860949X
[9] Swagatam Das, P. NagaratnamSuganthan., 2011. Differential Evolution: A Survey of the State-of-the-Art”, IEEE Transactions On Evolutionary Computation, vol. 15, no. 1, pp.4-32.
[10] Swagatam Das, Ajith Abraham, 2008, “Automatic Clustering Using An Improved Differential Evolution Algorithm, IEEE Transactions On Systems, Man, And Cybernetics—Part A: Systems And Humans, vol. 38 no.1,218-237.
[11] Swagatam Das, Amit Konar,2009 “Automatic image pixel clustering with an improved differential evolution”, Applied Soft Computing vol.9, 226–236
[12] Gong W., Cai Z., 2009, “An improved multiobjective differential evolution based on Pareto-adaptive epsilon-dominance and orthogonal design”, European Journal of Operational Research vol.198, no.2, 576–601.
[13] Qu B.Y., Suganthan P.N.,2010, “Multi-objective differential evolution with diversity enhancement”, Journal of Zhejiang University Science A, vol.11, 538–543.
[14] K.Krauter, R.Buyya, M.Maheswaran,A taxonomy and survey of grid resource management systems for distributed computing, Software-Practice and Experience,32:135-164, 2002.
[15] S.A.Jarvis, D.P.Spooner, H.N.Lim Choi Keung, G.R.Nudd, J.Cao, S.Saini, Performance prediction and its use in parallel and distributed computing systems. In the Proceedings of the IEEE/ACM International Workshop on Performance Evaluation and Optimization of Parallel and distributed Systems, Nice, France.2003.
[16] T.D.Braun, H.J.Siegel, N.Beck, D.A.Hensgen, R.F.Freund, A comparison of eleven static heuristics for mapping a class of independent tasks on heterogeneous distributed systems, Journal of Parallel and Distributed Computing, 2001, pp.810-837.
[17] H.Liu, A.Abraham, A.E.Hassanien, Scheduling Jobs on computational grids using a fuzzy particle swarm optimization algorithm, Future Generation Computer Systems (2009).
[18] Ch.SrinivasaRao, B.RaveendraBabu, DE Based Job Scheduling in Grid Environments, Journal of Computer Networks, 2013, Vol. 1, No. 2, 28-31.
[19] S.K.Garg, R.Buyya, H.J.Siegel, Time and cost trade-off management for scheduling parallel applications on Utility Grids, Future Generation Computer Systems (2009), doi:10.1016/j. future.2009.07.003.
[20] H.Liu, A.Abraham, A.E.Hassanien, Scheduling Jobs on computational grids using a fuzzy particle swarm optimization algorithm, Future Generation Computer Systems (2009), doi:10,1016/j.future.2009.05.022.

Keywords
Grid computing, Job scheduling, Fuzzy Differential Evolution.