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Reseach Article

Load Balancing Approach for Scheduling Sequential Task in Grid Computing Environment

by Neeraj Pandey, Shashi Kant Verma, Vivek Kumar Tamta
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 78 - Number 1
Year of Publication: 2013
Authors: Neeraj Pandey, Shashi Kant Verma, Vivek Kumar Tamta
10.5120/13457-1142

Neeraj Pandey, Shashi Kant Verma, Vivek Kumar Tamta . Load Balancing Approach for Scheduling Sequential Task in Grid Computing Environment. International Journal of Computer Applications. 78, 1 ( September 2013), 42-48. DOI=10.5120/13457-1142

@article{ 10.5120/13457-1142,
author = { Neeraj Pandey, Shashi Kant Verma, Vivek Kumar Tamta },
title = { Load Balancing Approach for Scheduling Sequential Task in Grid Computing Environment },
journal = { International Journal of Computer Applications },
issue_date = { September 2013 },
volume = { 78 },
number = { 1 },
month = { September },
year = { 2013 },
issn = { 0975-8887 },
pages = { 42-48 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume78/number1/13457-1142/ },
doi = { 10.5120/13457-1142 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:50:32.833359+05:30
%A Neeraj Pandey
%A Shashi Kant Verma
%A Vivek Kumar Tamta
%T Load Balancing Approach for Scheduling Sequential Task in Grid Computing Environment
%J International Journal of Computer Applications
%@ 0975-8887
%V 78
%N 1
%P 42-48
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In grid computing environment, the efficiency of computing node is affected by several factors such as node utilization, allocation of jobs etc. The incoming job is allocated to appropriate node in such a way that the node utilization is maximized and a well-balanced load across all the participating computing nodes that enhances the overall performance of grid computing. This paper presents an amended hybrid approach for scheduling sequential task. The proposed approach uses combination of first-come-first-served (FCFS) and genetic algorithm (GA). A sliding-window technique is presented to initiate alteration between the FCFS and GA, to offers a rapid task assignment. For GA we initially generate random population and use straightforward encoding. The proposed method is evaluated in the terms of makespan value and node utilization with a varying set of simulation cases and parameters after then it is compared with a well design first-come-first-server (FCFS) and hybrid genetic algorithm (HGA). Experimental results have shown significant improvement compared to the both FCFS and HGA algorithms. The result gives minimized makespan value with increased node utilization for both homogeneous and heterogeneous types of nodes.

References
  1. I. Foster, C. Kesselman, and S. Tuecke, The anatomy of the grid: Enabling scalable virtual organizations, The International Journal of High Performance Computing Applications, 15 (3), 200-222. (2001)
  2. Rajkumar Buyya, and Srikumar Venugopal, A Gentle Introduction to Grid Computing and Technologies, Computer Socity of India, CSI Communication, July (2005).
  3. Albert Y. Zomaya, Yee-Hwei, Observations on Using Genetic Algorithms for Dynamic Load-Balancing, IEEE Transections on Parallel and Distributed System, Vol. 12, No. 9, 899-911. (2001)
  4. Yajun Li, Yuhang Yang, Maode Ma, and Liang Zhoy, A hybrid load balancing strategy of sequential tasks for grid computing environments, Future Generation Computer Systems, 25, 819-828. (2009)
  5. Junwei Cao, Daniel P. Spooner, Stephen A. Jarvis, Graham R. Nudd: Grid load balancing using intelligent agents, Future Generation Computer Systems 21, 135–149. (2005)
  6. The OMNeT++ Discrete Event Simulation System. http://www. omnetpp. org/. (2012)
  7. OMNeT++ User Manual, http://www. omnetpp. org/doc/omnetpp/manual/usman. html. (2012)
  8. Albert Y. Zomaya, Chris Ward, and Ben Macey: Genetic Scheduling for Parallel Processor Systems: Comparative Studies and Performance Issues, IEEE Transections on Parallel and Distributed System, Vol. 10, No. 8, 795-812. (1999)
  9. Shanshan Song, 0. 6Kai Hwang, and Yu-Kwong Kwok: Risk-Resilient Heu0. 4ristics and Genetic Algorithms for Security-Assured Grid Job Scheduling, IEEE Transections on Computers, Vol. 55, No. 6, 703-719. (2008)
  10. K. Q. Yan, S. C. Wang, C. P. Chang, J. S. Lin: A hybrid load balancing policy underlying grid computing environment, Computer Standards & Interfaces 29, 161–173. (2007)
  11. Kuo-Qin Yan, Shun-Sheng Wang, Shu-Ching Wang, Chiu-Ping Chang: Towards a hybrid load balancing policy in grid computing system, Expert Systems with Applications 36, 12054–12064. (2009)
  12. A. Varga, and R. Hornig, An Overview of the OMNeT++ Simulation Environment. In the Proceedings of First International Conference on Simulation Tools and Techniques for Communications, Networks and Systems (SIMUTools 2008), Marseille, France. (2008)
  13. S. N. Sivanandam, and S. N. Deepa: Introduction to Genetic Algorithm, Springer-India. (2008))
  14. R. L. Haupt, and S. E. Haupt: Practical Genetic Algortihms, John Wiley & Sons. (2004)
  15. INET Framework Documentation, http://www. omnetpp. org/doc/INET. (2012)
  16. Andrew J. Page, Thomas M. Keane, Thomas J. Naughton: Multi-heuristic dynamic task allocation using genetic algorithms in a heterogeneous distributed system, Journal of parallel and distributed computing, 70, 758–766. (2010).
Index Terms

Computer Science
Information Sciences

Keywords

Grid Computing Load Balancing Task Scheduling Genetic Algorithm Performance evaluation