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Prioritized User Demand Approach for Scheduling Meta Tasks on Heterogeneous Grid Environment

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International Journal of Computer Applications
© 2011 by IJCA Journal
Number 1 - Article 2
Year of Publication: 2011
Authors:
P.Suresh
Dr.P.Balasubramanie
P.Keerthika
10.5120/2856-3670

P.Suresh, Dr.P.Balasubramanie and P.Keerthika. Article:Prioritized User Demand Approach for Scheduling Meta Tasks on Heterogeneous Grid Environment. International Journal of Computer Applications 23(1):6–12, June 2011. Full text available. BibTeX

@article{key:article,
	author = {P.Suresh and Dr.P.Balasubramanie and P.Keerthika},
	title = {Article:Prioritized User Demand Approach for Scheduling Meta Tasks on Heterogeneous Grid Environment},
	journal = {International Journal of Computer Applications},
	year = {2011},
	volume = {23},
	number = {1},
	pages = {6--12},
	month = {June},
	note = {Full text available}
}

Abstract

Due to the rapid evolution of grid computing, which deals with the effective utilization of the globally distributed computer resources to solve massive problems, grid scheduling is the major focus. Efficient scheduling algorithms are the need of the hour to achieve efficient utilization of the unused CPU cycles distributed geographically in various locations. The existing job scheduling algorithms in grid computing had mainly concentrated on the system performance rather than the user satisfaction. In this paper we have presented a new prioritized user demand algorithm that mainly focuses on better meeting the deadlines of the statically available jobs as expected by the users. This algorithm also concentrates on the better utilization of the available heterogeneous resources. The performance analysis shows that the prioritized user demand algorithm performs better than the other heuristic scheduling algorithms in terms of makespan and resource utilization rate.

Reference

  • Jie Lin, Bin Gong, Hui Liu, Chaoying Yang, Yuhui Tian, 2007. An Application Demand aware Scheduling Algorithm in Heterogeneous Environment. IEEE Proceedings of the 11th International Conference on Computer Supported Cooperative Work in Design., IEEE Xplore press, Melbourne, Vic, pp509-604, DOI:10.1109/CSCWD.2007.4281504
  • Li Wenzheng, Zhang Wenyue, 2009. An Improved Scheduling Algorithm for Grid Tasks. International Symposium on Intelligent Ubiquitous Computing and Education, pp 9-12, DOI:10.1109/IUCE.2009.35
  • Tracy D.Braun, Howard Jay Siegel, and Noah Beck, 2001. 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, pp.810-837, DOI:10.1006/jpdc.2000.1714
  • Ivan Rodero, Francesc Guim, and Julita Corbalan, 2009. Evaluation of Coordinated Grid Scheduling Strategies. 11th IEEE International Conference on High Performance Computing and Communications, DOI:10.1109/HPCC.2009.28
  • J.M.Schopf, “A General Architecture for Scheduling on the Grid, 2002. special issue of JPDC on Grid Computing.
  • T.Braun, H.Siegel, N.Beck, L.Boloni, M.Maheshwaran, A.Reuther, J.Robertson, M.Theys, B.Yao, D.Hensgen, and R.Freund, 1999. A Comparison Study of Static Mapping Heuristics for a Class of Meta-tasks on Heterogeneous Computing Systems. In 8th IEEE Heterogeneous Computing Workshop(HCW’99), IEEE Computer Society Washington, DC, USA. pp.15-29. DOI:10.1109/HCW.1999.765093
  • R.F.Freund, and M.Gherrity, 1998. Scheduling Resources in Multi-user Heterogeneous Computing Environment with Smart Net. In Proceedings of the 7th IEEE HCW, DOI:10.1109/HCW.1998.666558
  • Thomas G.Robertazzi and Dantong Yu, 2006.Multi-Source Grid Scheduling for Divisible Loads. 40th Annual Conference on Information Sciences and Systems, Princeton University, IEEE, DOI:10.1109/CISS.2006.286459
  • Zhang Qian, Li Zhen, 2009. Design of Grid Resource Management System Based on Divided Min-min scheduling Algorithm. IEEE First International Workshop on Education Technology and Computer Science, pp. 613-618, DOI:10.1109/ETCS.2009.670
  • Hojjat Baghban, Amir Masoud Rahmani, 2008. A Heuristic on Job Scheduling in Grid Computing Environment. In Proceedings of the seventh IEEE International Conference on Grid and Cooperative Computing, pp. 141-146, DOI:10.1109/GCC.2008.22
  • He Xiaoshan, Xia-He Sun, Gregor Von Laszewski, 2003. QoS Guided Min-min Heuristic for Grid Task Scheduling. Journal of Computer Science and Technology, pp. 442-451, DOI:10.1007/BF02948918
  • He Xiaoshan, Xia-He Sun, Gregor Von Laszewski, 2003. QoS Guided Min-min Heuristic for Grid Task Scheduling. Journal of Computer Science and Technology, pp. 442-451, DOI:10.1007/BF02948918.
  • Fangpeng Dong and Selim G. Akl, 2006. Scheduling Algorithms for Grid Computing: State of the Art and Open Problems. Technical Report, School of Computing, Queen's University, Canada.
  • Y. Zhu, 2003. A Survey on Grid Scheduling Systems, Department of Computer Science, Hong Kong University of science and Technology.
  • Wantao Liu, Rajkumar Kettimuthu, Bo Li, Ian Foster, 2010. An Adaptive Strategy for Scheduling Data-Intensive Applications in Grid Environments. IEEE 17th International Conference on Telecommunications, DOI:10.1109/ICTEL.2010.5478755
  • H.Chen, N.S.Flann, and D.W.Watson, 1998. Parallel Genetic Simulated Annealing: A Massively Parallel SIMD Approach. IEEE transactions on Parallel and distributed Computing, 9(2), pp. 126-136, DOI:10.1109/71.663870
  • I.D.Falco, R.D.Balio, E.Tarantino, and R.Vaccaro, 1994. Improving Search by Incorporating Evolution Principles in Parallel Tabu Search. IEEE Conference on Evolutionary Computation, pp. 823-828.
  • L.Wang, H.J.Siegel, V.P.Roychowdhury, and A.A.Macicjewski,1997. Task Matching and Scheduling in Heterogeneous Computing Environments Using a Genetic Algorithm Based Approach. Journal of Parallel and Distributed Computing, 47(1), pp. 1-15.