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Comparative Analysis of Scheduling Algorithms in Computational Grid Environment

by Uttaran Bhattacharya, Dipannita Dey
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 107 - Number 4
Year of Publication: 2014
Authors: Uttaran Bhattacharya, Dipannita Dey

Uttaran Bhattacharya, Dipannita Dey . Comparative Analysis of Scheduling Algorithms in Computational Grid Environment. International Journal of Computer Applications. 107, 4 ( December 2014), 27-33. DOI=10.5120/18742-9994

@article{ 10.5120/18742-9994,
author = { Uttaran Bhattacharya, Dipannita Dey },
title = { Comparative Analysis of Scheduling Algorithms in Computational Grid Environment },
journal = { International Journal of Computer Applications },
issue_date = { December 2014 },
volume = { 107 },
number = { 4 },
month = { December },
year = { 2014 },
issn = { 0975-8887 },
pages = { 27-33 },
numpages = {9},
url = { },
doi = { 10.5120/18742-9994 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
%0 Journal Article
%1 2024-02-06T22:40:13.073069+05:30
%A Uttaran Bhattacharya
%A Dipannita Dey
%T Comparative Analysis of Scheduling Algorithms in Computational Grid Environment
%J International Journal of Computer Applications
%@ 0975-8887
%V 107
%N 4
%P 27-33
%D 2014
%I Foundation of Computer Science (FCS), NY, USA

This paper tracks the development of Grid Computing since its inception in the late 1990s to its dominance in today's world of Distributed Computing and Information Technology. It focuses on the recent developments that spurred our interest to take up this field of research with emphasis on the algorithms we are researching for job scheduling and load balancing in the Grid Environment. The entire structure of the Grid Environment is dynamic and hybrid by nature, changing with the availability and the capability of resources or hosts that perform user tasks and the Quality of Service requirements of the tasks themselves. This makes the problem of developing an optimal task-to-resource schedule that ensures proper load balancing and also produces the minimum overall makespan (time to complete scheduled tasks) an NP-Hard Problem. Our attempt in this research is not to find the optimal solution for this problem, but to analyze and test various algorithms that produce acceptable performance in the commonly occurring practical scenarios. The algorithms considered for research and analysis include the classical First Come First Served Algorithm that schedules jobs to the best possible resources on arrival basis, Adaptive Workload Balancing Algorithm that essentially considers the job pool and the resource pool at a given point of time to give the optimal load-balanced schedule, and a brief insight into Genetic Algorithms that apply heuristics to perform optimization for some job criteria. Lastly, we include two algorithms, Fastest Processor to Largest Task First and Nearest Deadline First Served, which we have indigenously developed and are currently testing for performance.

  1. Thomas B Winans and John Seely Brown, Cloud computing A collection of working papers, Delloite Development LLC, 2009.
  2. I. Foster and C. Kesselman (editors), The Grid: Blueprint for a Future Computing Infrastructure, Morgan Kaufmann Publishers, USA, 1999.
  3. R. Buyya, D. Abramson, J. Giddy and H. Stockinger, Economic Models for Resource Management and Scheduling in Grid Computing, in J of concurrency and computation: Practice and Experience, Volume 14, Issue 13-15, PP. 1507-1542, Wiley Press, December 2002.
  4. K. Czajkowski, S. Fitzgerald, I. Foster, and C. Kesselman, Grid Information Services for Distributed Resource Sharing, Proceedings of the 10th IEEE International Symposium on High- Performance Distributed Computing (HPDC-10), pp. 181-194, San Francisco, California, USA, August 2001.
  5. Fangpeng Dong and Selim G. Akl, Scheduling Algorithms for Grid Computing: State of the Art and Open Problems, Technical Report No. 2006-504, School of Computing, Queen's University, Kingston, Ontario, January 2006.
  6. Pinky Rosemarry, Payal Singhal and Ravinder Singh, A Study of Various Job & Resource Scheduling Algorithms in Grid Computing, International Journal of Computer Science and Information Technologies, Vol. 3 (6), 2012, 5504-5507.
  7. Rajkumar Buyya, David Abramson and Jonathan Giddy, Nimrod/G: An Architecture for a Resource Management and Scheduling System in a Global Computational Grid, High Performance Computing Asia 2000, Beijing, China, May 14-17, 2000, pp. 283-289.
  8. Yajun Li, Yuhang Yanga, Maode Mab, Liang Zhou, A hybrid load balancing strategy of sequential tasks for grid computing Environments, Future Generation Computer Systems 25 (2009) 819-828.
  9. Raksha Sharma, Vishnu Kant Soni et al. , An Agent Based Dynamic Resource Scheduling Model with FCFS-Job Grouping Strategy in Grid Computing, World Academy of Science, Engineering and Technology, 2010, pp. 467-471.
  10. Vladimir V. Korkhova, Jakub T. Moscicki, Valeria V. Krzhizhanovskaya, Dynamic workload balancing of parallel applications with user-level scheduling on the Grid, Future Generation Computer Systems 25 (2009) 28–34.
  11. D. Daniel, Mrs. S. P. Jeno Lovesum M. E. , D. Asir and A. Catherine Esther Karunya, Adaptive Job Scheduling with Load Balancing for Workflow Application in Grid Platform, International Journal of Computer Engineering and Technology, Volume 2 Number 1, January 2011, pp. 09-21.
  12. Jairam Naik K. , K. Vijaya Kumar and N. Satyanarayana, Scheduling Tasks on Most Suitable Fault tolerant Resource for Execution in Computational Grid, International Journal of Grid and Distributed Computing, Vol. 5, No. 3, September, 2012, pp. 121-132.
  13. Sukalyan Goswami and Ajanta De Sarkar, A Comparative Study of Load Balancing Algorithms in Computational Grid Environment, Proceedings of the Fifth International Conference on Computational Intelligence, Modelling and Simulation by IEEE Computer Society, 2013, pp. 99-104.
  14. Luis Ferreira, Viktors Berstis, Jonathan Armstrong et al. , Introduction to Grid Computing with Globus, First Edition, IBM RedBooks, December 2002.
  15. Bernd Schuller and the UNICORE team, General Introduction to UNICORE 6, Jülich Supercomputing Centre, Forschungszentrum Jülich GmbH, March 17, 2010, OGF28 Munich.
Index Terms

Computer Science
Information Sciences


Clients Users Resources Resource Domains Hosts Processing Elements (PEs) Jobs Tasks Workload Makespan FCFS AWLB FPLTF NDFS Globus Toolkit Unicore.