Journal of Computer Networks
ISSN (Print): 2372-4749 ISSN (Online): 2372-4757 Website: Editor-in-chief: Sergii Kavun, Naima kaabouch
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Journal of Computer Networks. 2013, 1(2), 28-31
DOI: 10.12691/jcn-1-2-2
Open AccessArticle

DE Based Job Scheduling in Grid Environments

Ch.Srinivasa Rao1, and Dr.B.Raveendra Babu2

1R.V.R. & J.C. College of Engineering, Guntur, A.P., India

2VNR Vignana Jyothi Institute of Engineering & Technology, Hyderabad, A.P., India

Pub. Date: May 18, 2013

Cite this paper:
Ch.Srinivasa Rao and Dr.B.Raveendra Babu. DE Based Job Scheduling in Grid Environments. Journal of Computer Networks. 2013; 1(2):28-31. doi: 10.12691/jcn-1-2-2


Grid Computing is a computing framework developed to meet the growing computational demands. Essential grid services contain more intelligent functions for resource management, grid service marketing, collaboration etc. The load sharing of computational jobs is the major task of computational grids. Grid resource manager provides functional mechanism for discovery, publishing of resources as well as scheduling, submission and monitoring of jobs. This paper introduces an approach, based on Differential Evolution Algorithm for scheduling jobs on computational grid. The proposed approach generates an optimal schedule which helps in completing the jobs within a minimum period of time. We evaluate the performance of our proposed approach with a direct Genetic Algorithm (GA), Simulated Annealing (SA) and Particle Swarm Optimization (PSO) approach.

grid computing job scheduling differential evolution algorithm optimization makespan

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[1]  I.Foster,C.Kesselmann,(Eds.), The Grid: Blueprint for a New Computing Infrastructure, Morgan Kaufmann Publishers, , 1999.
[2]  O.H.Ibarra and C.E.Kim, Heuristic algorithms for scheduling independent tasks on nonidentical processors, J.ACM 24, 2 (Apr.1977), pp.280-289.
[3]  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.
[4]  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.
[5]  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.
[6]  H.Liu, A.Abraham, A.E.Hassanien, Scheduling Jobs on computational grids using a fuzzy particle swarm optimization algorithm, Future Generation Computer Systems (2009).
[7]  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).
[8]  F.P.Chang, C.Hwang, Design of digital PID Controllers for continuous time plants with performance criteria, Journal of the chineese Institute of Chemical Engineers, 35(6), 2004, pp.683-696.
[9]  Y.P.Chang, C.J.Wu, Optimal multiobjective planning of large-scale passive harmonic filters using hybrid differential evolution method considering parameter and loading uncertainty, IEEE transactions on Power Delivery, 20(1), 2005, pp.408-416.
[10]  G.Onwubolu, D.Davendra, Discrete Optmization Scheduling flow shops using differential evolution algorithm, European Journal of Operational Research 171 2006, pp.674-692.
[11]  A.K.M.K.A.Talukder, M.Kirley, R.Buyya, Multi objective Differential Evolution for Scheduling workflow applications on global Grids, Concurrency and Computation: Practice and Experience, published online in Wiley Interscience, (2009).
[12]  K.Price, R.Storn, Differential evolution (DE) for Continuous function optimization, 2007.