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Reseach Article

Job Scheduling in the Grid Computing using Criteria

by Latha C. A
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 90 - Number 6
Year of Publication: 2014
Authors: Latha C. A
10.5120/15576-4252

Latha C. A . Job Scheduling in the Grid Computing using Criteria. International Journal of Computer Applications. 90, 6 ( March 2014), 5-9. DOI=10.5120/15576-4252

@article{ 10.5120/15576-4252,
author = { Latha C. A },
title = { Job Scheduling in the Grid Computing using Criteria },
journal = { International Journal of Computer Applications },
issue_date = { March 2014 },
volume = { 90 },
number = { 6 },
month = { March },
year = { 2014 },
issn = { 0975-8887 },
pages = { 5-9 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume90/number6/15576-4252/ },
doi = { 10.5120/15576-4252 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:10:19.537453+05:30
%A Latha C. A
%T Job Scheduling in the Grid Computing using Criteria
%J International Journal of Computer Applications
%@ 0975-8887
%V 90
%N 6
%P 5-9
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Clusters of computers have emerged as mainstream parallel and distributed platforms for high performance, high throughput, and high availability computing. To enable effective load balancing in distributed systems, numerous schedulers have been designed. Migration of the job from an overloaded node to the idle node, involves matching the possessions of the idle computer with the job requirements. Both code and data are to be transferred to the idle node from overloaded node. The job is executed at the idle node. The results are transferred back to the host node. These consume a lot of bandwidth, processor time, and memory. A good selection of job results in less execution time, efficient usage of resources and overall increase in the throughput of the system with the minimum cost. The selection of the job and its subsequent execution is an interesting area of research. The proposed criteria based method assigns a weight for each criterion of each job of several predefined criteria. Then the total weights of all the jobs are found out. The job with the highest weight will be considered for submission.

References
  1. J Bustos, et. al. 2008, "Load Information Sharing Polices in communication Intensive Parallel Application", From Grids to Service and Pervasive Computing, pp. 111-121.
  2. Helen D. Karatza, Ralph C. Hilzer 2003, "Parallel job scheduling in homogeneous distributed systems", The Society for modeling and Simulation International. Simulation, Vol. 79, No. 5–6, May–June.
  3. Karatza H. D, Hilzer R. C. 2003, "Performance analysis of parallel job scheduling in distributed systems". 36th Annual Simulation Symposium, Mar-Apr, Orland, pp. 109-116.
  4. Karatza H. D. 2000, "A comparative analysis of scheduling policies in a distributed system using simulation", International Journal of Simulation Systems, Science &Technology, pp. 12-20.
  5. Karatza, H. D. 2000, "Scheduling Strategies for Multitasking in a Distributed System". The 33rd Annual. Simulation Symposium, IEEE Computer system. Apr, Washington, DC, pp. 83-90.
  6. Koip P. 2005, "Parallel Algorithms for Combinatorial Search Problems", University of Massachusetts,
  7. Legrand, H. B. Newman 2000, "A self-organizing neural network for job scheduling in distributed systems", Contribution to ACAT.
  8. Luling R, Monien. B 1993. "A Dynamic Distributed Load Balancing Algorithm with Provable Good Performance", 5th ACM Symposium on Parallel Algorithms and Architectures, pp. 164-173.
  9. Renato P. et. al. 2007, "A complex network-based approach for job scheduling in grid environments", HPCC 2007, lncs 4782, pp. 204–215.
  10. Veeravalli, B. Wong Han Min 2004, "Scheduling divisible loads on heterogeneous linear daisy chain networks with arbitrary processor release time", Vol. 15, No. 3, March, pp. 273 – 288.
  11. Zhang Y, A. Sivasubramaniam 2001, "Scheduling best-effort and real-time pipelined applications on timeshared clusters", 13th Annual ACM Symposium on Parallel Algorithms and Architectures, July 4-6, Crete Island, Greece, pp. 209-219.
  12. P. Agrawal, D. Kifer, and C. Olston 2008. "Scheduling Shared Scans of Large Data Files". In Proc. VLDB, pages 958–969.
  13. J. Dean and S. Ghemawat 2008. Mapreduce: simpli?ed data processing on large clusters. Communications of the ACM, 51(1):107–113.
  14. D. Thain, T. Tannenbaum, and M. Livny 2005. "Distributed Computing in Practice: The Condor Experience. Concurrency and Computation: Practice and Experience", 17(2):323–356, February.
  15. M. Zaharia, D. Borthakur, J. S. Sarma, K. Elmeleegy, S. Shenker, and I. Stoica 2009. "Job Scheduling for Multi-User MapReduce Clusters". Technical Report UCB/EECS-2009-55, University of California at Berkeley, April.
  16. Gulati, I. Ahmad, and C. A. Waldspurger 2009. "PARDA: Proportional Allocation of Resources for Distributed Storage Access". In Proceedings of the Seventh USENIX Conference on File and Storage Technologies (FAST'09), pages 85–98, February.
Index Terms

Computer Science
Information Sciences

Keywords

Distributed system load balance Idle overloaded criteria weight