Abstract
In this paper we propose an efficient parallel job scheduling algorithm for a grid environment. The algorithm implies two stage scheduling. At the first stage, the algorithm allocates jobs to the suitable machines, where at the second stage jobs are independently scheduled on each machine. Allocation of jobs on the first stage of the algorithm is performed with use of a relatively new evolutionary algorithm called Generalized Extremal Optimization (GEO). Scheduling on the second stage is performed by some proposed heuristic. We compare GEO-based scheduling algorithm applied on the first stage with Genetic Algorithm (GA)-based scheduling algorithm. Experimental results show that the GEO, despite of its simplicity, outperforms the GA algorithm in all range of scheduling instances.
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References
Ernemann, C., Yahyapour, R.: Applying Economic Scheduling Methods to Grid Environments. In: Grid Resource Management - State of the Art and Future Trends, pp. 491–506. Kluwer Academic Publishers (2003)
Ernemann, C., Hamscher, V., Schwiegelshohn, U., Streit, A., Yahyapour, R.: On Advantages of Grid Computing for Parallel Job Scheduling. In: Proc. of 2nd IEEE Int. Symposium on Cluster Computing and the Grid, pp. 39–46 (2002)
Foster, I., Kesselman, C., Tuecke, S.: The Anatomy of the Grid: Enabling Scalable Virtual Organizations. Int. Journal Supercomputer Applications. 15(3) (2001)
Ghafoor, A., Yang, J.: A Distributed Heterogeneous Supercomputing Management System. Computer 26(6), 78–86 (1993)
Hall, R., Rosenberg, A.L., Venkataramani, A.: A Comparison of Dag-Scheduling Strategies for Internet-Based Computing. In: IEEE International Parallel and Distributed Processing Symposium, p. 55 (2007)
Schwiegelshohn, U.: An Owner-centric Metric for the Evaluation of Online Job Schedules. In: Proceedings of the 2009 Multidisciplinary International Conference on Scheduling: Theory and Applications, pp. 557–569 (2009)
Sinnen, O.: Task Scheduling for Parallel Systems, pp. 108–111. J. Wiley & Sons (2007)
Sousa, F.L., Ramos, F.M., Galski, R.L., Muraoka, I.: Generalized Extremal Optimization: A New Meta-heuristic Inspired by a Model of Natural Evolution. In: Recent Developments in Biologically Inspired Computing, pp. 41–60 (2004)
Tchernykh, A., Schwiegelshohn, U., Yahyapour, R., Kuzjurin, N.: On-line hierarchical job scheduling on grids with admissible allocation. Journal of Scheduling 13(5), 545–552 (2010)
Vazquez-Poletti, J.L., Huedo, E., Montero, R.S., Llorente, I.M.: A comparison between two grid scheduling philosophies: EGEE WMS and Grid Way. Journal Multiagent and Grid Systems 3(4), 429–440 (2007)
Xhafa, F., Abraham, A. (eds.): Meta-heuristics for Grid Scheduling Problems in Distributed Computing Environments. SCI, vol. 146, pp. 1–37 (2008)
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Switalski, P., Seredynski, F. (2012). A Grid Scheduling Based on Generalized Extremal Optimization for Parallel Job Model. In: Wyrzykowski, R., Dongarra, J., Karczewski, K., Waśniewski, J. (eds) Parallel Processing and Applied Mathematics. PPAM 2011. Lecture Notes in Computer Science, vol 7204. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31500-8_5
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DOI: https://doi.org/10.1007/978-3-642-31500-8_5
Publisher Name: Springer, Berlin, Heidelberg
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