Abstract

Grid computing is the collection of computer resources from multiple locations to reach a common goal. The grid is a special type of distributed system with non-interactive workloads that involve a large number of files. Partitioning of the application program/ software into a number of small groups of modules among dissimilar processors is an important parameter to determine the efficient utilization of available resources in a grid computing environment. It also enhances the computation speed. The task partitioning and task allocation activities influence the distributed program/ software properties such as IPC. This paper presents a metaheuristic model, that performs static allocation of a set of "m" modules of distributed tasks/program considering the two conflicting objectives i. e. minimizing the makespan time and balanced utilization of a set of "n" available resources of a grid computing. Experimental results using genetic algorithm indicates that the proposed algorithm achieved these two objectives as well as improve the dynamic heuristics presented in literature.

References

• I. Foster, C. Kesselman, J. Nick and S. Tuecke, "The Physiology of the Grid: An Open Grid Services Architecture for Distributed Systems Integration", Technical Report, Open Grid Service Infrastructure WG, Global Grid Forum, 2002.
• A. J. S. Santiago, A. J. Yuste, J. E. M. Exposito, S. G. Galan and R. P. D. Prado," A Multi-Criteria Meta-Fuzzy-Scheduler for Independent Tasks in Grid Computing",Computing and Informatics vol. 30:1201-1223 (2011).
• K. Vivekanandan and D. Ramyachitra,"A Study on Scheduling in Grid Environment", International Journal on Computer Science and Engineering, vol. 3, No. 2, Feb 2011.
• H. M. Lee, J. S. Su and C. H. Chung,"Resource Allocation Analysis Model Based on Grid Environment", International Journal of Innovative Computing, Information and Control, vol. 7, No. 5(A), May 2011.
• S. Selvi, D. Manimegalai and A. Suruliandi,"Efficient Job Scheduling on Computational Grid with Differential Evolution Algorithm", International Journal of Computer Theory and Engineering, vol. 3, No. 2, April 2011.
• S. Roy and A. Rana,"Comparitive Study of Heuristics Techniques for Resource Allocation in Grid Computing Environment", International Journal of Technology vol. 2, No. 2, 2012.
• D. Thilagavathi, and A. S. Thanamani,"A Survey on Dynamic Job Scheduling in Grid Environment Based on Heuristic Algorithms", International Journal of Computer Trends and Technology vol. 3, Issue 4, 2012.
• S. F. El-Zoghdy, M. Nofal, M. A. Shohla and A. El-sawy,"An Efficient Algorithm Resource Allocation in Parallel and Distributed Computing Systems", International Journal of Advanced Computer Science and Applications, vol. 4, No. 2, 2013.
• S. Mandloi and H. Gupta," A Review of Resource Allocation and Task Scheduling for Computational Grids based on Meta-Heuristic Function", International Journal of Research in Computer and Communication Technology, vol. 2, Issue 3, March-2013.
• J. Kolodziej and S. U. Khan," Multi-Level hierarchic genetic-based scheduling of independent jobs in dynamic heterogeneous grid environment", Information Sciences: An International Journal, vol. 214, pp. 1-19, Dec. 2012.
• P. Y. Yin, S. S. Yu, P. P. Wang and Y. T. Wang," Multi-objective task allocation in distributed computing systems by hybrid particle swarm optimization", Applied Mathematics and Computation vol. 184, page no. 407-420.
• S. Y. Rashida and H. Navidi, " A Bargaining based scheduling for resources advanced reservation using simulated annealing into grid system", Internation Journal of Computer Science, vol. 6, no. 9, issue 6, No. 1, Nov. 2012.
• R. S. Chang, J. S. Chang, and P. S. Lin (2009),"An ant algorithm for balanced job scheduling in grids", Future Generation Computer Systems, 25, 1, pp. 20-27.
• C. Fayad, J. M. Garibaldi and D. Ouelhadj," Fuzzy Grid Scheduling Using Tabu Search", IEEE, 2007
• Z. Pooranian, M. Shojafar, R. Tavoli, M. Singhal and A. Abraham," A Hybrid Metaheuristic Algorithm for Job Scheduling on Computational Grids", Informatica, vol. 37, no. 2, 2013 June, p. 157(8)
• A. Yousif, A. H. Abdullah, S. M. Nor and A. A. Arbdelaziz," Scheduling Jobs on Grid Computing Using Firefly Algorithm", Journal of Theoretical and Applied Information Technology, vol. 33, No. 2, November 2011.
• Radha and V. Sumathy," A Hybrid Genetic Algorithm with Elitist Ant System in Grid Scheduling", Life Science Journal, 2013; 10(7s): 510-515.
• W. Abdulal, A. Jabas, S. Ramachandram and Omar Al Jadaan," Task Scheduling in Grid Environment Using Simulated Annealing and Genetic Algorithm", in book Grid Computing-Technology and Applications, Widespread Coverage and New Horizons edited by Soha Maad, ISBN 978-953-51-0604-3, Published: May 16, 2012 , chapter 5.
• J. Holland, "Adaptation in Natural and Artificial Systems," University of Michigan Press, Ann Arbor, ISBN: 0-262-58111-6, 1975.
• Y. Hamed,"Task Allocation for Maximizing Reliability of Distributed Computing Systems Using Genetic Algorithms", International Journal of Computer Networks and Wireless Communications vol. 2, No. 5, 2012.
• Z. Pooranian, M. Shojafar, J. H. Abawajy, and M. Singhal,"GLOA: A New Job Scheduling Algorithm for Grid Computing", International Journal of Artificial Intelligence and Interactive Multimedia, Vol. 2, No. 1, pp. 59-64, 2013.
• M. P. Singh, P. K. Yadav and A. Aggarwal,"Response time optimization of a grid computing system using genetic approach", in conference proceeding, Dhanbad, Jharkhand, 2013, pp. 171-179.
• . K. Yadav, Preet Pal Singh and P. Pradhan,"A Tasks Allocation Algorithm for Optimum Utilization of Processor's in Heterogeneous Distributing Computing Systems", vol. 2, Issue 1, 2013.