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Reseach Article

A Hybrid Heuristic Algorithm for Task Scheduling in Grid Environment

Published on October 2013 by D. Ramyachitra, P. Suganthi
National Conference on Recent Trends in Computer Applications
Foundation of Computer Science USA
NCRTCA - Number 1
October 2013
Authors: D. Ramyachitra, P. Suganthi
ef4b6ac9-cb88-4a69-a74f-4ac2bc380f85

D. Ramyachitra, P. Suganthi . A Hybrid Heuristic Algorithm for Task Scheduling in Grid Environment. National Conference on Recent Trends in Computer Applications. NCRTCA, 1 (October 2013), 6-10.

@article{
author = { D. Ramyachitra, P. Suganthi },
title = { A Hybrid Heuristic Algorithm for Task Scheduling in Grid Environment },
journal = { National Conference on Recent Trends in Computer Applications },
issue_date = { October 2013 },
volume = { NCRTCA },
number = { 1 },
month = { October },
year = { 2013 },
issn = 0975-8887,
pages = { 6-10 },
numpages = 5,
url = { /proceedings/ncrtca/number1/13632-1302/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 National Conference on Recent Trends in Computer Applications
%A D. Ramyachitra
%A P. Suganthi
%T A Hybrid Heuristic Algorithm for Task Scheduling in Grid Environment
%J National Conference on Recent Trends in Computer Applications
%@ 0975-8887
%V NCRTCA
%N 1
%P 6-10
%D 2013
%I International Journal of Computer Applications
Abstract

Grid computing is an emerging trend that provides a high performance computing platform to solve larger scale applications by coordinating and sharing computational power, data storage and network resources. A grid coordinates and integrates resources and users of different administrative domains inside the same company or in different countries. Task scheduling is one of the key research areas in grid computing. The goal of scheduling is to achieve highest possible system throughput and to match the application's need with the available computing resources. This paper primarily focuses on task scheduling process of Artificial Bee Colony (ABC) algorithm. The objective of this algorithm is to generate optimal solution dynamically. By this scheduling, complete the task in minimum time and use the available resources in efficient manner. The best assignment of tasks produced by ABC is selected and applies Genetic Algorithm (GA) for achieving better performance and evaluation. This heuristic algorithm provides an optimal task scheduling in heterogeneous computing environments.

References
  1. Foster, C. Kesselman and S. Tuecke, "The Anatomy of the Grid: Enabling Scalable Virtual organizations", International Journal of Supercomputer Applications, 15(2001).
  2. Ian Foster "What is the Grid? A Three Point Checklist" Argonne National Laboratory & University of Chicago.
  3. Peter Gradwell "Overview of Grid Scheduling Systems" Department of Computer Science, University of Bath.
  4. Fatos Xhafa, Ajith Abraham, "Computational models and heuristic methods for Grid scheduling problems", Future Generation Computer Systems 26 (2010) 608_621.
  5. T. Casavant and J. Kuhl, "A Taxonomy of Scheduling in General Pupose Distributed Computing Systems", IEEE Trans. on Software Engineering Vol. 14, No. 2, PP 141-154, February 1988.
  6. M. Arora, S. K. Das, R. Biswas, "A Decentralized Scheduling and Load Balancing Algorithm for Heterogeneous Grid Environments", in Proc. Of International Conference on Parallel Processing Workshops (ICPPW'02), pp. :499-505, Vancouver, British Columbia Cananda, August 2002.
  7. L. Lee, C. Liang, H. Chang, "An adaptive task scheduling system for Grid Computing", Proceedings of the Sixth IEEE international Conference on Computer and information Technology, CIT'06, 2006, p. 57.
  8. J. Yu, R. Buyya and K. Ramamohanrao, "Workflow Scheduling Algorithms for Grid Computing, Meta. for Sched. in Distri. Comp. Envi. ", SCI 146, springerlink. com, pp. 173–214, 2008.
  9. O. H. Ibarra and C. E. Kim, "Heuristic algorithms for scheduling independent tasks on non-identical processors", J. Assoc. Compute. Mach. 24, 2 (Apr. 1977), 280_289.
  10. M. Dorigo, C. Blum, "Ant colony optimization theory: A survey ", Theoretical Computer Science 344 (2–3) (2005) 243–278.
  11. M. Dorigo, "Ant colony optimization", http://www. aco-metaheuristic. org.
  12. M. Dorigo, L. M. Gambardella, "Ant colony system: A cooperative learning approach to the traveling salesman problem", IEEE Transactions on Evolutionary Computation 1 (1) (1997) 53–66.
  13. Dervis Karaboga, Bahriye Basturk, "A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm", 12 February 2007 / Published online: 13 April 2007 © Springer Science+Business Media B. V. 2007.
  14. Dr. K. Vivekanandan, D. Ramyachitra, B. Anbu, "Artificial Bee Colony Algorithm For Grid Scheduling", Journal of Convergence Information Technology, Volume6, Number7, July 2011.
  15. Jin Xu, Albert Y. S. Lam, and Victor O. K. Li, "Chemical Reaction Optimization for Task Scheduling in Grid Computing", IEEE Transactions On Parallel And Distributed Systems.
  16. www. buyya. com
  17. www. gridcomputing. com
  18. www. gridforum. org
Index Terms

Computer Science
Information Sciences

Keywords

Grid Computing Resource Sharing Task Scheduling Heuristic Algorithms.