CFP last date
20 May 2024
Reseach Article

A Time-Minimization Dynamic Job Grouping-based Scheduling in Grid Computing

by Manoj Kumar Mishra, Prithviraj Mohanty, G. B. Mund
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 40 - Number 16
Year of Publication: 2012
Authors: Manoj Kumar Mishra, Prithviraj Mohanty, G. B. Mund
10.5120/5064-7419

Manoj Kumar Mishra, Prithviraj Mohanty, G. B. Mund . A Time-Minimization Dynamic Job Grouping-based Scheduling in Grid Computing. International Journal of Computer Applications. 40, 16 ( February 2012), 16-25. DOI=10.5120/5064-7419

@article{ 10.5120/5064-7419,
author = { Manoj Kumar Mishra, Prithviraj Mohanty, G. B. Mund },
title = { A Time-Minimization Dynamic Job Grouping-based Scheduling in Grid Computing },
journal = { International Journal of Computer Applications },
issue_date = { February 2012 },
volume = { 40 },
number = { 16 },
month = { February },
year = { 2012 },
issn = { 0975-8887 },
pages = { 16-25 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume40/number16/5064-7419/ },
doi = { 10.5120/5064-7419 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:28:14.180400+05:30
%A Manoj Kumar Mishra
%A Prithviraj Mohanty
%A G. B. Mund
%T A Time-Minimization Dynamic Job Grouping-based Scheduling in Grid Computing
%J International Journal of Computer Applications
%@ 0975-8887
%V 40
%N 16
%P 16-25
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Grid computing is the novel framework that offers a flexible, secure and high performance computing, on demand for solving high compute-intensive applications with large number of independent jobs. However, user jobs developed for grid might be small and of varying lengths according to their computational needs and other requirements. Certainly, it is a real challenge to design an efficient scheduling strategy to achieve high performance in grid computing. But there exists some grouping based job scheduling strategy that intends to minimize total processing time by reducing overhead time and computation time, and on the other hand maximizing resource utilization than without grouping based scheduling. The purpose of the study is to analyze and achieve better performance by extending the concept of grouping based job scheduling. Therefore, this paper proposes “A Time-Minimization Dynamic Grouping-Based Job Scheduling in Grid Computing” with the objective of minimizing overhead time and computation time, thus reducing overall processing time of jobs. The work is verified through various observations made in different simulated grid environments. The results obtained shows that the proposed grouping-based scheduling algorithm is on average, comparable to, or even better than, other grouping based scheduling algorithms.

References
  1. Jeremy M. Norman (edited), From Gutenberg to the Internet: A Sourcebook on the History of Information Technology: 2005, pp. 870.
  2. L.Klienrock,“UCLA press release,” 1969, http://www.lk.cs.ucla.edu/LK/Bib/REPORT/ press.html
  3. I.Foster, and C. Kesselman, Globus: a metacomputing infrastructure toolkit, International Journal of High Performance Computing Applications, Vol. 2, pp. 115–128, 1997.
  4. Ian Foster and Carl Kesselman, “The Grid: Blueprint for a New Computing Infrastructure,” Elsevier Inc., Singapore, Second Edition, 2004.
  5. Myer, Thomas, “Grid Computing: Conceptual Flyover for Developers”, May 2003 ,http://www-106.ibm.com/developerworks/library/gr-fly.html gridsrc.pdf
  6. M. Baker, R. Buyya, D. Laforenza, “Grids and Grid Technologies for Wide-area Distributed Computing”. SoftwarePractice & Experience, Vol 32, No. 15, 2002,pp. 1437 -1466
  7. D. Bernstein, M. Rodeh and I. Gertner, “On the Complexity of Scheduling Problems for Parallel/Pipelined Machines“, IEEE Transactions on Computers, vol. 38, p. 1308, 1998.
  8. S. You, H. Kim, D. Hwang, S. Kim, “Task Scheduling Algorithm in GRID Considering Heterogeneous Environment”, in The 2004 International Conference on Parallel and Distributed Processing Techniques and Applications (PDPTA'04), Monte Carlo Resort, Las Vegas, Nevada, USA, June 21 - 24, 2004, pp. 240-245.
  9. N. Muthuvelu, Junyan Liu, N.L.Soe, S.venugopal, A.Sulistio, and R.Buyya, “A dynamic job grouping-based scheduling for deploying applications with fine-grained tasks on global grids,” in Proc of Australasian workshop on grid computing, vol. 4, pp. 41–48, 2005.
  10. Gray, J. (2003): Distributed Computing Economics.Newsletter of the IEEE Task Force on Cluster Computing, 5(1), July/August
  11. Sarkar, V. (1989): Partitioning and Scheduling Parallel Programs for Execution on Multiprocessors, Cambridge, MIT Press.
  12. Gerasoulis, A. and Yang, T. (1992): A comparison of clustering heuristics for scheduling directed graphs on multiprocessors. Journal of Parallel and Distributed Computing, 16(4):276-291.
  13. Radulescu, A. and van Gemund, A. (1998): GLB: A Low-Cost Scheduling Algorithm for Distributed-Memory Architectures. Proc. of the Fifth International Conference on High Performance Computing(HiPC 98), Madras, India, pp. 294-301, IEEE Press
  14. James, H. A., Hawick, K. A. and Coddington, P. D. (1999): Scheduling Independent Tasks on Metacomputing Systems. Proc. of Parallel and Distributed Computing (PDCS ’99), Fort Lauderdale, USA
  15. Buyya, R., Date, S., Mizuno-Matsumoto, Y., Venugopal, S. and Abramson, D. (2004): Neuroscience Instrumentation and Distributed Analysis of Brain Activity Data: A Case for eScience on Global Grids. Journal of Concurrency and Computation: Practice and Experience
  16. Jeremy M. Norman (edited), From Gutenberg to the Internet: A Sourcebook on the History of Information Technology: 2005, pp. 870
  17. Ng Wai Keat, Ang Tan Fong, “Scheduling Framework For Bandwidth-Aware Job Grouping-Based Scheduling In Grid Computing”, Malaysian Journal of Computer Science, vol. 19, No. 2, pp. 117-126, 2006
  18. Quan Liu, Yeqing Liao, “Grouping-based Fine-grained Job Scheduling in Grid Computing”, IEEE First International Workshop on Educational technology And Computer Science, vol.1, pp. 556-559, 2009.
  19. M.K.Mishra, R. Sharma, V. K. Soni, B. R. Parida, R. K. Das(2010): A Memory-Aware Dynamic Job Scheduling Model in Grid Computing. International Conference on Computer Design and Applications, 2010 IEEE, vol.1-545.
  20. Schopf, J.: A General Architecture for Scheduling on the Grid. Submitted to special issue of JPDC on Grid Computing (2002).
  21. Buyya, R., Abramson, D., Giddy, J.: An Economy Driven Resource Management Architecture for Global Computational Power Grids. International Conference on Parallel and Distributed Processing Techniques and Applications (2000).
  22. Abraham A., Buyya R., Nath B.: Nature's Heuristics for Scheduling Jobs on Computational Grids. International Conference on Advanced Computing and Communications (2000).
  23. Abramson, D., Buyya, R., Giddy, J.: A Computational Economy for Grid Computing and its Implementation in the Nimrod-G Resource Broker. Future Generation Computer Systems Journal, Volume 18, Issue 8, Elsevier Science (2002) 1061-1074.
  24. R. Buyya and M. Murshed, Gridsim :A toolkit for the modeling and simulation of distributed management and scheduling for grid computing, 2002
Index Terms

Computer Science
Information Sciences

Keywords

Grid computing Job grouping Job scheduling