Abstract
Grid scheduling techniques are widely studied in the related literature to fulfill scientist requirements of deadline or budget for their experiments. Due to the conflictive nature of these requirements - minimum response time usually implies expensive resources - a multi-objective approach is implemented to solve this problem. In this paper, we present the Multi-Objective Small World Optimization (MOSWO) as a multi-objective adaptation from algorithms based on the small world phenomenon. This novel algorithm exploits the so-called small-world effect from complex networks, to optimize the job scheduling on Grid environments. Our algorithm has been compared with the well-known multi-objective algorithm Non-dominated Sorting Genetic Algorithm-II (NSGA-II) to evaluate the multi-objective properties and prove its reliability. Moreover, MOSWO has been compared with real schedulers, the Workload Management System (WMS) from gLite and the Deadline Budget Constraint (DBC) from Nimrod-G, improving their results.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Buyya, R., Murshed, M., Abramson, D.: A deadline and budget constrained cost-time optimisation algorithm for scheduling task farming applications on global grids. In: Int. Conf. on Parallel and Distributed Processing Techniques and Applications, Las Vegas, Nevada, USA, pp. 2183–2189 (2002)
Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast elitist multi-objective genetic algorithm: Nsga-ii. IEEE Transactions on Evolutionary Computation 6, 182–197 (2000)
Du, H., Wu, X., Zhuang, J.: Small-World Optimization Algorithm for Function Optimization. In: Jiao, L., Wang, L., Gao, X.-b., Liu, J., Wu, F. (eds.) ICNC 2006. LNCS, vol. 4222, pp. 264–273. Springer, Heidelberg (2006)
Khare, V., Yao, X., Deb, K.: Evolutionary Multi-Criterion Optimization, vol. 2632. Springer, Heidelberg (2003)
Kleinberg, J.: The small-world phenomenon: an algorithm perspective. In: Proceedings of the Thirty-Second Annual ACM Symposium on Theory of Computing, STOC 2000, pp. 163–170. ACM, New York (2000)
Li, X., Zhang, J., Wang, S., Li, M., Li, K.: A small world algorithm for high-dimensional function optimization. In: Proceedings of the 8th IEEE International Conference on Computational Intelligence in Robotics and Automation, CIRA 2009, pp. 55–59. IEEE Press, Piscataway (2009)
Mao, W., Yan, G., Dong, L., Hu, D.: Model selection for least squares support vector regressions based on small-world strategy. Expert Syst. Appl. 38, 3227–3237 (2011)
Milgram, S.: The small world problem. Psychology Today 2, 60–67 (1967)
Strogatz, S.H.: Exploring complex networks. Nature 410(6825), 268–276 (2001)
Sulistio, A., Poduval, G., Buyya, R., Tham, C.: On incorporating differentiated levels of network service into gridsim. Future Gener. Comput. Syst. 23(4), 606–615 (2007)
Talukder, A.K.M.K.A., Kirley, M., Buyya, R.: Multiobjective differential evolution for scheduling workflow applications on global grids. Concurr. Comput.: Pract. Exper. 21(13), 1742–1756 (2009)
Tsuchiya, T., Osada, T., Kikuno, T.: Genetics-based multiprocessor scheduling using task duplication. Microprocessors and Microsystems 22(3-4), 197–207 (1998)
Wang, X., Cai, S., Huang, M.: A Small-World Optimization Algorithm Based and ABC Supported QoS Unicast Routing Scheme. In: Li, K., Jesshope, C., Jin, H., Gaudiot, J.-L. (eds.) NPC 2007. LNCS, vol. 4672, pp. 242–249. Springer, Heidelberg (2007)
Watts, D.J., Strogatz, S.H.: Collective dynamics of ’small-world’ networks. Nature 393(6684), 440–442 (1998)
Ye, G., Rao, R., Li, M.: A multiobjective resources scheduling approach based on genetic algorithms in grid environment. In: International Conference on Grid and Cooperative Computing Workshops, pp. 504–509 (2006)
Yu, J., Kirley, M., Buyya, R.: Multi-objective planning for workflow execution on grids. In: GRID 2007: Proceedings of the 8th IEEE/ACM International Conference on Grid Computing, pp. 10–17. IEEE Computer Society, Washington, DC, USA (2007)
Zeng, B., Wei, J., Wang, W., Wang, P.: Cooperative Grid Jobs Scheduling with Multi-objective Genetic Algorithm. In: Stojmenovic, I., Thulasiram, R.K., Yang, L.T., Jia, W., Guo, M., de Mello, R.F. (eds.) ISPA 2007. LNCS, vol. 4742, pp. 545–555. Springer, Heidelberg (2007)
Zitzler, E., Thiele, L.: Multiobjective Optimization Using Evolutionary Algorithms - A Comparative Case Study. In: Eiben, A.E., Bäck, T., Schoenauer, M., Schwefel, H.-P. (eds.) PPSN 1998. LNCS, vol. 1498, pp. 292–304. Springer, Heidelberg (1998)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Arsuaga-Ríos, M., Prieto-Castrillo, F., Vega-Rodríguez, M.A. (2012). Small-World Optimization Applied to Job Scheduling on Grid Environments from a Multi-Objective Perspective. In: Di Chio, C., et al. Applications of Evolutionary Computation. EvoApplications 2012. Lecture Notes in Computer Science, vol 7248. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29178-4_5
Download citation
DOI: https://doi.org/10.1007/978-3-642-29178-4_5
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-29177-7
Online ISBN: 978-3-642-29178-4
eBook Packages: Computer ScienceComputer Science (R0)