Implementing Job Scheduling to Optimize Computational Tasks in Grid Computing using PSO

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IJCA Proceedings on National Conference on Knowledge, Innovation in Technology and Engineering (NCKITE 2015)
© 2015 by IJCA Journal
NCKITE 2015 - Number 3
Year of Publication: 2015
Authors:
Surendra Kumar Patel
Anil Kumar Sharma
Anurag Seetha

Surendra Kumar Patel, Anil Kumar Sharma and Anurag Seetha. Article: Implementing Job Scheduling to Optimize Computational Tasks in Grid Computing using PSO. IJCA Proceedings on National Conference on Knowledge, Innovation in Technology and Engineering (NCKITE 2015) NCKITE 2015(3):20-24, July 2015. Full text available. BibTeX

@article{key:article,
	author = {Surendra Kumar Patel and Anil Kumar Sharma and Anurag Seetha},
	title = {Article: Implementing Job Scheduling to Optimize Computational Tasks in Grid Computing using PSO},
	journal = {IJCA Proceedings on National Conference on Knowledge, Innovation in Technology and Engineering (NCKITE 2015)},
	year = {2015},
	volume = {NCKITE 2015},
	number = {3},
	pages = {20-24},
	month = {July},
	note = {Full text available}
}

Abstract

Grid computing is a recent advancement technology that enables resource sharing and dynamic allocation of computational resources, thus getting higher access to distributed data, promoting operational elasticity and collaboration. So, efficient resource management is one of the fundamental requirements in grid computing. Resource management is required in an environment where resources are quite limited and need to be utilised properly. Due to the complex and dynamic properties of grid environments, existing traditional model-based methods may result in poor scheduling performance. To overcome of such problem, we need to develop improved algorithm that reduces the computation time. This paper proposed PSO algorithm specifically focused on improving computational grid performance in terms of equal load balance for all jobs and total computation time, which enhance grid throughput, utilization, response time and more economic profits.

References

  • Foster I. , Kesselman C. , 2004, "The Grid 2: Blueprint for a New Computing Infrastructure", Second Edition, Elsevier and Morgan Kaufmann Press.
  • Foster, and C. Kesselman. 2003,"The Grid 2: Blueprint for a New Computing Infrastructure", Morgan Kaufmann, USA.
  • R. Buyya, D. Abramson, and S. Venugopal. 2005, "The Grid Economy". Proceedings of the IEEE, pp. 698-714.
  • J. Kennedy, R. C. Eberhart, Particle swarm optimization, in: Proceedings of the IEEE International Conference on Neural Networks (1995) 1942–1948.
  • A. Salman, I. Ahmad, S. Al-Madani, Particle swarm optimization for task assignment problem, Microprocessors and Microsystems 26 (2002) 363–371.
  • J. Kennedy, R. C. Eberhart, A discrete binary version of the particle swarm algorithm, IEEE international conference on Systems, Man, and Cybernetics (1997) 4104 – 4108.
  • Weijun X, Zhiming W, Wei ZH, Genke Y (2004) A new hybrid optimization algorithm for the job-shop scheduling problem. In: Proceeding of the 2004 American control conference, vol 6, Boston, pp 5552–5557.
  • Izakian H, Tork Ladani B, Zamanifar K, Abraham A 2009) A novel particle swarm optimization approach for grid job scheduling. Commun Comput Inf Sci 31:100–109.
  • R. Buyya, "A grid simulation toolkit for resource modelling and application scheduling for parallel and distributed computing", www. buyya. com/gridsim/, accessed on January '2014.
  • EU DataGrid Project. The DataGrid Architecture. Technical Report DataGrid-12-D12. 4-333671-3-0, CERN, Geneva, Switzerland, 2001.
  • Cameron, D. G. , A. P. Millar, C. Nicholson, R. Carvajal-Schiaffino, F. Zini, and K. Stockinger 2004. Optorsim: a simulation tool for scheduling and replica optimisation in data grids. In Computing in High Energy and Nuclear Physics.
  • Cameron, D. G. , A. P. Millar, C. Nicholson, R. Carvajal-Schiaffino, F. Zini, and K. Stockinger 2004. Optorsim: a simulation tool for scheduling and replica optimisation in data grids. In Computing in High Energy and Nuclear Physics.
  • Blythe, James, Sonal Jain, EwaDeelman, Yolanda Gil, Karan Vahi, AnirbanMandal, and Ken Kennedy. "Task scheduling strategies for workflow-based applications in grids. " In Cluster Computing and the Grid, 2005. CCGrid 2005. IEEE International Symposium on, vol. 2, pp. 759-767. IEEE, 2005.
  • Somasundaram, K. "Dynamic resource allocation in grid computing. " (2014).
  • Higashino, Wilson A. , Miriam AM Capretz, and Maria Beatriz Felgar De Toledo. "Evaluation of Particle Swarm Optimization Applied to Grid Scheduling. " InWETICE Conference (WETICE), 2014 IEEE 23rd International, pp. 173-178. IEEE, 2014.
  • Izakian, Hesam, BehrouzTorkLadani, Kamran Zamanifar, and Ajith Abraham. "A novel particle swarm optimization approach for grid job scheduling. " In Information Systems, Technology and Management, pp. 100-109. Springer Berlin Heidelberg, 2009.
  • Ismail, Leila. "Dynamic resource allocation mechanisms for grid computing environment. " In Testbeds and Research Infrastructure for the Development of Networks and Communities, 2007. TridentCom 2007. 3rd International Conference on, pp. 1-5. IEEE, 2007.