An Evolutionary Hybrid Scheduling Algorithm for Computational Grids
Shajulin Benedict*, Rejitha R. S**, and V. Vasudevan*
*Software Technologies Lab, TIFAC Core in Network Engineering
**Department of Computer Engineering, Kalasalingam University
Sirivilliputhur, India- 626190
Grids promote user collaboration through flexible, coordinated sharing of distributed resources to solve a single large problem. Grid scheduling, similar to resource discovery and monitoring, is inherently more complex in Grid environments. We propose two approaches for solving Grid scheduling problems with the simultaneous objectives of maximizing the number of workflow executions and minimizing the waiting time variance among tasks of each workflow. One is the multiple objective Niched Pareto Genetic Algorithm (NPGA) that involves evolution during a comprehensive search and work on multiple solutions. After the Genetic search, we strengthen the search using Simulated Annealing as a local search meta-heuristic. For comparison, we evaluate other scheduling, such as, Tabu Search (TS), Simulated annealing (SA), and Discrete Particle Swarm Optimization (Discrete PSO). Results show that our proposed evolutionary Hybrid scheduling involving NPGA with an SA search, works better than other scheduling in considering workflow execution time within a deadline and waiting time variance in tasks with minimal iterations.
 F. Berman, G. Fox, and T. Hey (eds), “Grid Computing: Makiing the Global Infrastructure a Reality,” Wiley, pp. 110-115, 2003.
 M. Cannataro, D. Talia, and P.Trunfio, “Distributed data mining on the grid, Future Generation Computer Systems,” Vol.18, pp. 1101-1112, 2002.
 J. Frey, T. Tannenbaum, I. Foster, M. Livny, and S. Thecke, “Condor-G: A Computation Management Agent for Multi-Instituitional Grids,” Journal of Cluster Computing, Vol.5, pp. 237-246, 2002.
 P. F. Gorder, “Grid computing yields earthquake forecast,” News in Computing in Science and Engineering, pp. 6-10, 2007.
 P. Manish and J. C. Browne, “Conceptual and Implementation models for the grids,” Proc. of the IEEE Vol.93, No.3. pp. 653-668, March, 2005.
 S. Song, K. Hwang, and Y.-K. Kwok, “Risk Resilient Heuristics and Genetic algorithm for security assured Grid Job Scheduling,” IEEE Trans. on Computers, Vol.5, No.6, pp. 703-719, June. 2006.
 J. L. Vazquez-Poletti et al., “Workflow Management in a protein clustering application,” in Proc. Of 7th Int. Conf. on cluster computing and the Grid (CCGrid' 07), 2007.
 J. Y. Halpern and Y. Moses, “Knowledge and common knowledge in a distributed environment,” Journal of ACM, Vol.37, No.3, pp. 549-587, 1990.
 R. Real, A. Yamin, L. da Silva, G. Frainer, I. Augustin, J. Barbosa, and C. Geyer, “Resouirce Scheduing on Grid: Handling Uncertainty,” Proc. of the Fourth Int. Workshop on Grid computing, pp. 205-207, 2005.
 W. Marek, P. Radu, and F. Thoas, “Scheduling of Scientific Workflows in the ASKALON Grid Enivironment,” SIGMOD Record, Vol.34, No.3, pp. 56-62, Sept., 2005.
 A. Alain, et al. [Online] “Open Issues for Grid scheduling,” Available here.
 C.-H. Chien, P. H.-M. Chang, and V.-W. Soo, “Market Oriented Multiple Resource Scheduling in Grid Computing Environments,” Proc. of the 19th Int. Conf. on Advanced Information Networking and Applications (AINA'05), pp. 862-872, IEEE 2005.
 Y. Hui, et al., “An Improved ant algorithm for job scheduling in grid computing,” In Proc. of 4th Int. Conf. on machine, learning and cybernetics, pp. 2957-2961, Aug., 2005.
 V. Di Martino and M. Mililotti, “Scheduling in a Grid computing environment using Genetic Algorithms,” Proc. of the Int. Parallel and Distributed Processing Symposium, IEEE 2002.
 J. Horn, N. Nafploitis, and D. E. Goldberg, “A Niched Pareto Genetic Algorithm for Multi Objective Optimization,” Proc. of the First IEEE Int. Conf. on Evolutionary Computation, IEEE Press, Piscataway, NJ, pp. 82-87, 1994.
 S. Benedict, et al, “Scheduling scientific workflows using Niched Pareto GA for Grids,” in Proc of IEEE Int. Conf. of Services, Operations, Logistics, pp. 908-912, June, 2006.
 S. Benedict and V. Vasudevan, “Improving Scheduling of scientific workflows using Tabu Search for Computational Grids,” Information Technology Journal, 7(1), pp. 91-97, 2008.
 S. Benedict and V. Vasudevan, “Scheduling of scientific workflows using Simulated annealing algorithm for Computational Grids,” Int. Journal of Soft Computing, Vol.2, No.5, pp. 606-611, July 2007.
 S. Benedict and V. Vasudevan, “Scheduling of scientific workflows using Discrete PSO for Grids,” JCIT: Journal of Convergence and Information Technology, Vol.2, No.4, pp. 29-35, 2007.
 P. J. M. Van Laarhovan and E. H. L. Aarts, “Simulated annealing: Theory and applications,” Dordrecht, Kluwer academic, 1987.
 S. Benedict and V. Vasudevan, “A Niched Pareto GA approach of scheduling scientific workflows in wireless Grids,” accepted for publication in Journal of computing and Information Technology, Europe, 2007.
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