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Comparitive Analysis of ACO and PSO in Grid Job Scheduling

B. Radha, Dr. V. Sumathy

Abstract


Grid computing is a one that coordinates and shares computation, application, data storage, or network resources across dynamic and geographically dispersed organizations. Scheduling is one of the main critical design issues of grid computing. It becomes a challenge because the capability and availability of resources vary in a dynamic nature. The complexity of scheduling problem increases with the size of the grid and becomes difficult to solve effectively. To solve this issue we go for designing new optimal solutions. It mainly focuses on new heuristic techniques that provide an optimal or near optimal solution for larger computational grids. Ant Colony Optimization based scheduling algorithm and PSO the population based search algorithm is used .The PSO is mainly based on the simulation of the social behavior of bird flocking fish schooling approach. The proposed scheduler allocates an application to a host from a pool of available hosts and applications by selecting the best match. Based on the experimental results, we prove that the proposed PSO algorithm confidently demonstrates its practicability and competitiveness with ACO algorithms.


Keywords


ACO, Grid computing, Job Scheduling, PSO

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References


I. Foster, C. Kesselman, and S. Tuecke. The anatomy of the grid: Enabling scalable virtual organizations. International Journal of Supercomputer Applications, 15:2001, 2001.

M. Dorigo. Optimization, Learning and Natural Algorithms. Phd Thesis,Politecnico Di Milano, Italy, 1992.

M. Dorigo, V. Maniezzo, and A. Colorni. Positive Feedback as a Search Strategy.Technical Report, 1991.

M. S. Kwang and H. W. Sun. Ant colony optimization for routing and load-balancing:survey and new directions. IEEE Transactions on Systems, Man and Cybernetics, PartA, 33(5):560-572, 2003.

J. L. Deneubourg, S. Aron, S. Goss, , and J. M. Pasteels. The selforganizing exploratory pattern of the argentine ant. Journal of Insect Behavior, (2):159-168,1990.

J. E. Bell and P. R. Mcmullen. Ant colony optimization techniques for the vehicle routing problem. Adv. Eng. Inf., 18(1):41-48, 2004.

M. Dorigo and L. M. Gambardella. Ant colonies for the travelling salesman problem, 1996

M. S. Kwang and H. W. Sun. Ant colony optimization for routing and load-balancing:survey and new directions. IEEE Transactions on Systems, Man and Cybernetics, PartA, 33(5):560-572, 2003.

K.M. Sim and W. H. Sun. Multiple Ant Colony Optimization for Load Balancing.Springer Berlin / Heidelberg, 2003.

Chen, B. Zhang, X. Hao, and Y. Dai. Task scheduling in grid based on particle swarm optimization. In ISPDC '06: Proceedings of the Fifth international Symposium on Parallel and Distributed Computing, pages 238-245, Washington, DC, USA, 2006.IEEE Computer Society.

Kennedy and R. C. Eberhart. Swarm intelligence. Morgan Kaufmann Publishers,2001

R. Eberhart and J. Kennedy. A new optimizer using particle swarm theory. Proceedings of the Sixth International Symposium on Micro Machine and Human Science, MHS '95. Pages 39-43, Oct 1995.

J. E. Bell and P. R. Mcmullen. Ant colony optimization techniques for the vehicle routing problem. Adv. Eng. Inf., 18(1):41-48, 2004.

W. Yi, Q. Liu, and Y. He. Dynamic distributed genetic algorithms. Proceedings of the 2000 Congress on Evolutionary Computation,2:1132-1136 vol.2, 2000.

I. C. Trelea. The particle swarm optimization algorithm: convergence analysis and parameter selection. Information Processing Letters,85(6):317-325, 2003.


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