A Randomized Load Balancing Algorithm IN GRID USING MAX MIN PSO Algorithm

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Author(s):
C. Kalpana, U. Karthick Kumar, R. Gogulan
Published Date:
April 30, 2012
Issue:
Volume 2, Issue 3
Page(s):
17 - 23
DOI:
10.7815/ijorcs.23.2012.024
Views:
5055
Downloads:
898

Keywords:
component, computational grid, grid scheduling, load balancing, swarm intelligence
Citation:
C. Kalpana, U. Karthick Kumar, R. Gogulan, "A Randomized Load Balancing Algorithm IN GRID USING MAX MIN PSO Algorithm". International Journal of Research in Computer Science, 2 (3): pp. 17-23, April 2012. doi:10.7815/ijorcs.23.2012.024 Other Formats

Abstract

Grid computing is a new paradigm for next generation computing, it enables the sharing and selection of geographically distributed heterogeneous resources for solving large scale problems in science and engineering. Grid computing does require special software that is unique to the computing project for which the grid is being used. In this paper the proposed algorithm namely dynamic load balancing algorithm is created for job scheduling in Grid computing. Particle Swarm Intelligence (PSO) is the latest evolutionary optimization techniques in Swarm Intelligence. It has the better performance of global searching and has been successfully applied to many areas. The performance measure used for scheduling is done by Quality of service (QoS) such as makespan, cost and deadline. Max PSO and Min PSO algorithm has been partially integrated with PSO and finally load on the resources has been balanced.

  1. Dr.K.Vivekanandan, D.Ramyachitra, “A Study on Scheduling in Grid Environment”,International Journal on Computer Science and Engineering (IJCSE), Vol. 3 No. 2 Feb 2011.
  2. D.P.Spooner, S.A.Jarvis, J.Cao, S.Saini and G.R.Nudd, “Local Grid Scheduling Techniques using Performance Prediction”, High Performance Systems Group, Department of Computer Science, University of Warwick, Coventry, CV4 7AL, UK.
  3. Bart Jacob, Michael Brown, Kentaro Fukui, Nihar Trivedi, “Introduction to Grid Computing”, RedBook, December 2005.
  4. Wei-Neng Chen, Jun Zhang, “An Ant Colony Optimization Approach to a Grid workflow Scheduling Problem With Various QoS Requirements”, IEEE Transactions on Systems man and cybernetics—Part c: Applications and Reviews, vol. 39, no. 1, January 2009. doi:10.1109/TSMCC.2008.2001722
  5. Alberto Colorni, Marco Dorigo, Vittorio Maniezzo, “Distributed Optimization by Ant Colonies”, European Conference On Artificial Life, Paris, France, Elsevier Publishing, 134–142.
  6. http://en.wikipedia.org/wiki/Ant_colony_optimization_algorithms#cite_note-0
  7. J.Kennedy and R.Eberhart, “Particle swarm optimization”, In Neural Networks, Proceedings, IEEEInternational Conference on, volume 4, pages 1942–1948 vol.4, 1995.
  8. K. Kousalya and P. Balasubramanie, “Task Severance and Task Parceling Based Ant Algorithm for Grid Scheduling”, International journal of computational cognition (http://www.ijcc.us), vol. 7, no. 4, december 2009.
  9. Abraham, R. Buyya, and B. Nath, “Nature’s heuristics for scheduling jobs on computational Grid”, In The 8th IEEE International Conference on Advanced Computing and Communications (ADCOM 2000), India, 2000.
  10. Lei Zhang, et al, “A Task Scheduling Algorithm Based on PSO for Grid Computing”, International Journal of Computational Intelligence Research, ISSN 0973-1873 Vol.4, 1 (2008), pp. 37–43.
  11. Lei Zhang, Yuehui Chen, Bo Yang, “Task Scheduling Based on PSO Algorithm in Computational Grid”, 2006 Proceedings of the 6th International Conference on Intelligent Systems Design and Applications, vol-2, 16-18 Oct, 2006, Jinan, China. doi:10.1109/ISDA.2006.253921
  12. Karl Czajkowski, Steven Fitzgerald, Ian Foster, Carl Kesselman, “Grid Information Services for Distributed Resource Sharing”, To appear in Proc. 10th IEEE Symp. on High Performance Distributed Computing, 2001. doi:10.1109/HPDC.2001.945188
  13. D. Merkle, M. Middendorf, and H. Schmeck, “Ant colony optimization for resource-constrained project scheduling”, IEEE Trans. Evol. Comput.,vol. 6, no. 4, pp. 333–346, Aug. 2002. doi:10.1109/TEVC.2002.802450
  14. Pakize Erdogmus, “Particle swarm optimization performance on special linear programming problem”, Scientific Research and Essays Vol. 5(12), pp. 1506-1518, 18 June, 2010.
  15. Grosu, D., Chronopoulos, A.T, “Noncooperative load balancing in distributed systems”, Journal of Parallel Distrib.Comput. 65(9), 1022–1034 (2005). doi:10.1016/j.jpdc.2005.05.001
  16. Penmatsa, S., Chronopoulos, A.T, “Job allocation schemes in computational Grids based on cost optimization”, In: Proceedings of 19th IEEE International Parallel and Distributed Processing Symposium, Denver, (2005). doi:10.1109/IPDPS.2005.264

  • Mishra, Manvi, et al. "Comparative analysis of various evolutionary techniques of load balancing: a review." International Journal of Computer Applications 63.15 (2013): 8-13.
  • Mishra, Manoj Kumar, et al. "A Survey on scheduling heuristics in grid computing environment." International Journal of Modern Education and Computer Science (IJMECS) 6.10 (2014): 57.
  • Kumar, M., C. Samuel, and A. Jaiswal. "AN OVERVIEW OF DISTRIBUTED GENERATION IN POWER SECTOR."