CFP last date
20 May 2024
Reseach Article

Simulation of MJ_CDTmin based Scheduling Algorithm in Grid Environment

by Deepti Malhotra, Devanand, Anik Gupta
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 42 - Number 11
Year of Publication: 2012
Authors: Deepti Malhotra, Devanand, Anik Gupta
10.5120/5738-7920

Deepti Malhotra, Devanand, Anik Gupta . Simulation of MJ_CDTmin based Scheduling Algorithm in Grid Environment. International Journal of Computer Applications. 42, 11 ( March 2012), 24-29. DOI=10.5120/5738-7920

@article{ 10.5120/5738-7920,
author = { Deepti Malhotra, Devanand, Anik Gupta },
title = { Simulation of MJ_CDTmin based Scheduling Algorithm in Grid Environment },
journal = { International Journal of Computer Applications },
issue_date = { March 2012 },
volume = { 42 },
number = { 11 },
month = { March },
year = { 2012 },
issn = { 0975-8887 },
pages = { 24-29 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume42/number11/5738-7920/ },
doi = { 10.5120/5738-7920 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:31:05.047153+05:30
%A Deepti Malhotra
%A Devanand
%A Anik Gupta
%T Simulation of MJ_CDTmin based Scheduling Algorithm in Grid Environment
%J International Journal of Computer Applications
%@ 0975-8887
%V 42
%N 11
%P 24-29
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

To achieve the promising potentials of tremendous distributed resources, effective and efficient scheduling algorithms are fundamentally important. Unfortunately, scheduling algorithms in traditional parallel and distributed systems, which usually run on homogeneous and dedicated resources, e. g. computer clusters, cannot work well in the new circumstances. In this research paper, we introduce a New Grid Job Scheduling algorithm MJ_CDTmin (multiple jobs based on the minimum cumulative departure time). The MJ_CDTmin is based on the rule that the cumulative arrival time of the next job arriving at the processor is compared with the minimum cumulative departure time of the processor. The main aim of proposed scheduling algorithm is to increase the system efficiency and to satisfy the job requirements from the available resources. In this research work the proposed algorithm has been implemented and validated. To demonstrate the usability of proposed techniques, a Simulation test bench was implemented using the Turbo C platform and successful simulation was achieved. The experimental results showed a significant improvement in terms of a smaller makespan time as compared to the already existing FCFS scheduling algorithm.

References
  1. I. Foster. 2002. What is the Grid? Daily News And Information For The Global Grid Community, Vol. 1, No. 6.
  2. Rajkummar Buyya. 2002. Economic-based Distributed Resource Management and Scheduling for grid computing. PhD thesis, Monash university, Melborn, Australia.
  3. K. Al-Saqabi, S. Sarwar, and K. Saleh. 1997. Distributed gang scheduling in networks of heterogeneous workstations. Computer Communications Journal, pp. 338-348.
  4. Maheswaran M, Ali S, Siegel H J, et al. 1999. Dynamic mapping of a class of independent tasks on to heterogeneous computing systems. In the 8th IEEE Heterogeneous Computing Workshop (HCW '99),San Juan, Puerto Rico, pp. 30-44.
  5. XiaoShan He, XianHe Sun, and Gregor von Laszewski. 2003. QoS Guided Min-Min Heuristic for Grid Task Scheduling. Computer Science and Technology, 18(4):442-451.
  6. J. Krallmann,U. Schwiegelshohn, and R. Yahyapour. 1999. On the design and evaluation of job scheduling algorithms. In 5th Workshop on Job Scheduling Strategies for Parallel Processing, volume LNCS 1659, pages 17–42.
  7. V. Hamscher, U. Schwiegelshohn,A. Streit, R. Yahyapour. 2000. Evaluation of job-scheduling strategies for grid computing. In Proceedings of First IEEE/ACM International Workshop on Grid Computing, Lecture Notes in Computer Science, vol. 1971, Springer, Berlin,pp. 191–202.
  8. Deepti Malhotra, Devanand. 2012. Framework for Job scheduling in grid environment. International journal of computer applications (0975-8887),vol 38-No. 7,pages42-48.
  9. Malarvizhi. N, Rhymend Uthariaraj. 2008. A Broker-Based Approach to Resource Discovery and Selection in Grid Environments. In IEEE International Conference on Computer and Electrical Engineering, pp. 322-326.
  10. Feitelson, D. G. Rudolph R. 1995. Parallel Job Scheduling: Issues and Approaches, Springer Lecture Notes in Computer Science, vol 949, pp. 1- 18.
  11. M. Harchol-Balter, T. Leighton, D. Lewin. 1999. Resource Discovery in Distributed Networks. In 18th ACM Symposium on Principles of Distributed Computing.
  12. Anne Benoit, Murray Cole, Stephen Gilmore and Jane Hillston. 2005. Enhancing the effective utilization of Grid clusters by exploiting on-line performability analysis. In IEEE International symposium on Cluster Computing and the Grid, pp. 317-324.
  13. Shun-Li Ding, Jing-Bo Yuan and Ji-U-Bin Ju. 2004. An algorithm for agent based task scheduling in grid environments, proceedings of International Conference on Machine Learning and Cybernetics, p2809- 2814.
  14. Huyn zhang, chanle wu, Q. xiong, and L. Wu,G. Ye. 2006. Research on an Effective Mechanism of Task Scheduling in Grid Environment. In IEEE, Fifth International Conference on Grid and Cooperative Computing (GCC'06).
  15. T. D. Braun, H. J. Siegel, N. Beck, L. L. Bölöni, M. Maheswaran, A. I. Reuther, J. P. Robertson, M. D. 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. Vol. 61(6):Pages 810-837.
  16. G. Ritchie and J. Levine. A. 2003. A fast, effective local search for scheduling independent jobs in heterogeneous computing environments. Technical report,Centre for Intelligent Systems and their Applications, School of Informatics,University of Edinburgh
  17. Li Liu, Yi Yang, Lian Li and Wanbin Shi. 2006. Using Ant Optimization for super scheduling in Computational Grid. In IEEE proceedings of the Asia-pasific Conference on Services Computing (APSCC' 06).
  18. HUI YAN, XUE-QIN SHEN, XING LI, MING-HU MU. 2005. An Improved ant algorithm for Job Scheduling in Grid Computing. In fourth IEEE International Conference on Machine Learning and Cybernetics, Guangzhou.
  19. A. Anjum, R. McClatchey, H. Stockinger, A. Ali, I. Willers, M. Thomas, M. Sagheer, K. Hasham, and O. Alvi. 2006. DIANA Scheduling Hierarchies for Optimizing Bulk Job Scheduling. In Proc. Second IEEE International Conference On e-Science and grid computing (e- Science'06).
  20. T. Wang, X. zhou, Q. Liu, Z. Yang, and Y. Wang. 2006. An adaptive Resource Scheduling Algorithm for Computational Grid. In IEEE Asiapacific Conference on Services Computing (APSCC'06).
  21. Jie Song, Chee-Kian Koh, Simon See, and Gay Kheng. 2005. Performance Investigation of Weighted Meta-scheduling Algorithm for Scientific Grid. The 4th International Conference on Grid and Cooperative Computing (GCC 2005) LNCS 3795,pp. 1021-1030.
Index Terms

Computer Science
Information Sciences

Keywords

Grid Computing Job Scheduling Scheduler Makespan Mcdt