Abstract
Grid facilitates global computing infrastructure for user to consume the services over the network. To optimize the workflow grid execution, a robust multi-objective scheduling algorithm is needed. In this paper, we considered two conflicting objectives like execution time (makespan) and total cost. We propose a multi-objective scheduling algorithm, using ε –MOEA approach based on evolutionary computing paradigm. Simulation results show that the proposed algorithm generates multiple scheduling solutions near the Pareto optimal front with uniform spacing and better convergence in small computation time.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Foster, I., Kesselman, C.: The Grid: Blueprint for a New Computing Infrastructure. Morgan Kaufmann, San Francisco (1999)
Braun, T., Siegal, H., Beck, N.: A comparison of Eleven Static Heuristics for Mapping a Class of Independent Tasks onto Heterogeneous Distributed Computing Systems. Journal of Parallel and Distributed Computing 61, 810–837 (2001)
Wang, L., Siegel, H., Roychowdhury, V., Maciejewski, A.: Task Matching and Scheduling in Heterogeneous Computing Environments using a Genetic-Algorithm-Based Approach. Journal of Parallel Distributed Computing 47, 9–22 (1997)
Wieczorek, M., Prodan, R., Fahringer, T.: Scheduling of Scientific Workflows in the SKALON Grid Environment. SIGMOD Rec., 34, 56–62 (2005)
Wieczorek, M., Podlipning, S., Prodan, R., Fahringer, T.: Bi-criteria Scheduling of Scientific Workflows for the Grid. IEEE, Los Alamitos (2008) 978-0-7675-3156-4/08
Yu, J., Buyya, R.: Scheduling Scientific Workflow Applications with Deadline and Budget Constraints using Genetic Algorithms. Scientific Programming 14, 217–230 (2006)
Tsiakkouri, E., Sakellariou, R., Zhao, H., Dikaiakos, M.: Scheduling Workflows with Budget Constraints. In: CoreGRID Integration Workshop, Pisa, Italy, pp. 347–357 (2005)
Haluk, T., Hariri, S., Wu, M.: Performance-Effective and Low-Complexity Task Scheduling for Heterogeneous Computing. IEEE Transactions on Parallel and Distributed Systems 13, 260–274 (2002)
Prodan, R., Fahringer, T.: Dynamic scheduling of Scientific Workflow Applications on the Grid: A case study. In: SAC 2005, pp. 687–694. ACM, New York (2005)
Yu, J., Kirley, M., Buyya, R.: Multi-objective Planning for Workflow Execution on Grids. In: Proceedings of the 8th IEEE/ACM International conference on Grid Computing (2007), ISBN:978-1-4244-1559-5, doi:10.1109/GRID.2007.4354110
Talukder, A., Kirley, M., Buyya, R.: Multiobjective Differential Evolution for Scheduling Workflow Applications on Global Grids. John Wiley & Sons, Ltd, Chichester (2009), doi:10.1002/cpe.1417
Deb, K.: Multi-Objective Optimization using Evolutionary Algorithms. Wiley & Sons, England (2001)
Deb, K., Pratap, A., Aggarwal, S., Meyarivan, T.: A Fast Elitist Multi-Objective Genetic Algorithm: NSGA-II. In: Deb, K., Rudolph, G., Lutton, E., Merelo, J.J., Schoenauer, M., Schwefel, H.-P., Yao, X. (eds.) PPSN 2000. LNCS, vol. 1917, pp. 553–562. Springer, Heidelberg (2000)
Zitzler, E., Laumanns, M., Thiele, L.: SPEA2: Improving the Strength Pareto Evolutionary Algorithm for Multiobjective Optimization. In: Giannakoglou, K.C., Tshalis, D.T., Periaux, J., Papailion, K.D., Fogarty, T. (eds.) Evolutionary Methods for Design Optimization and Control with Applications to Industrial Problems, Athens, Greece, pp. 95–100 (2001)
Knowles, J., Corne, D.: The Pareto Archive Evolution Strategy: A New Baseline Algorithm for Multi-Objective Optimization. In: The Congress on Evolutionary Computation, pp. 98- 105 (1999)
Deb, K., Mohan, M., Mishra, S.: A Fast Multi-objective Evolutionary Algorithm for Finding Well-Spread Pareto-Optimal Solutions. KanGAL Report Number: 2003002, Indian Institute of Technology, Kanpur, India (2003)
Camelo, M., Donoso, Y., Castro, H.: A Multi-Objective Performance Evaluation in Grid Task Scheduling using Evolutionary Algorithms. In: Applied Mathematics and Informatics (2010) ISBN: 978-960-474-260-8
Grosan, C., Abraham, A., Helvik, B.: Multiobjective Evolutionary Algorithms for Scheduling Jobs on Computational Grids. In: International Conference on Applied Computing, pp. 459-463, Salamanca, Spain (2007) ISBN 978-972-8924-30-0
Dogan, A., Ozguner, F.: Biobjective Scheduling Algorithms for Execution Time-Reliability trade-off in Heterogeneous Computing Systems. Comput. J. 48(3), 300–314 (2005)
Buyya, R.: GridSim: A Toolkit for Modeling and Simulation of Grid Resource Management and Scheduling, http://www.buyya.com/gridsim
Deb, K., Jain, S.: Running Performance Metrics for Evolutionary Multi-objective Optimization. In: Simulated Evolution and Learning (SEAL 2002), pp. 13–20 (2002)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Garg, R., Singh, D. (2011). ε –Pareto Dominance Based Multi-objective Optimization to Workflow Grid Scheduling. In: Aluru, S., et al. Contemporary Computing. IC3 2011. Communications in Computer and Information Science, vol 168. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-22606-9_7
Download citation
DOI: https://doi.org/10.1007/978-3-642-22606-9_7
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-22605-2
Online ISBN: 978-3-642-22606-9
eBook Packages: Computer ScienceComputer Science (R0)