IJSER Home >> Journal >> IJSER
International Journal of Scientific and Engineering Research
ISSN Online 2229-5518
ISSN Print: 2229-5518 6    
Website: http://www.ijser.org
scirp IJSER >> Volume 3,Issue 6,June 2012
Grid Scheduling using Improved Particle Swarm Optimization with Digital Pheromones
Full Text(PDF, )  PP.I06-111  
SarathChandar A P, Priyesh V, Doreen Hephzibah Miriam D
Digital Pheromones, Flow time, Grid Computing, Particle Swarm Optimization, Task Scheduling
Scheduling is one of the core steps to efficiently exploit the capabilities of emergent computational systems such as grid computing. Grid environment is a dynamic, heterogenous and unpredictable computing system which shares different services among various users. Because of heterogenous and dynamic nature of the grid, the methods used in traditional systems could not be applied to grid scheduling and therefore new methods should be designed to address this research problem. This paper represents the technique of particle swarm optimization with digital pheromones which hasimproved solution characteristics. The main objective of the proposed algorithm is to find a solution that generates an optimal schedule which minimizes the flowtime in grid environment.Simulations have been carried out using GridSim for the improved PSO algorithm. Experimental studies illustrates that the proposed methodology, Improved PSO with digital pheromones is more efficient and surpasses those of PSO algorithms for the grid scheduling problem.
[1] A. Abraham, R. Buyya, and B. Nath. Nature’s heuristics for scheduling jobs on computational grids. The 8th IEEE International Conference on Advanced Computing and Communications, pages 45-52, 2000.

[2] M. Aggarwal, R. Kent, and A. Ngom. Genetic algorithm based scheduler for computational grids. 2005.

[3] T.D. Braun, H. J. Siegel, N. Beck, L. L. Boloni, M. Maheswaran, A. I. Reuther, J. P. Robertson, M. D. Theys, B. Yao, D. Hensgen, and R. F. Freund. A comparison of eleven static heuristics for mapping a class of independent tasks onto heterogenous distributed computing systems. J. Parallel Distrib. Comput., 61:810-837, June 2001.

[4] R. Buyya and M. Murshed. Gridsim: A toolkit for the modeling and simulation of distributed resource management and scheduling for grid computing. Concurrency and Computation: Practice and Experience (CCPE), 14:1175-1220, 2002.

[5] M. FatihTasgetiren, Y.-C. Liang, M. Sevkli, and G.Gencyilmaz. Particle swarm optimization and differential evolution for single machine total weighted tardiness problem. International Journal of Production Research, 44:4737-4754, 2006.

[6] I. Foster and C. Kesselman. The grid 2 : Blueprint for a new computing infrastructure. Morgan Kaufmann, 2003.

[7] Y. Gao, H. Rong, and J. Huang. Adaptive grid job schedulings with genetic algorithms. Future Generation Computer Systems, 21:151-161, 2005.

[8] L. Jian-Ning and W. Hu-Zhong. Scheduling in grid computting environment based on genetic algorithm. Journal of computer research and development, 2004.

[9] V. Kalivarapu, J.-1.Foo, and E. Winer. Improving solution charaacteristics of particle swarm optimization using digital pheromones. Struct multidisc Optim, 37:415-427,2009

[10] J. Kennedy and R. Eberhart, particle swarm optimization. In proceedings of IEEE international conference of Neural Networks, 5:1942-1948, 1995

[11] A.P.SarathChandar, S. Dheeban, V.Deepak and S.Elias. Personalized ecourse composition approach using digital pheromones in improved particle swarm optimization. In sixth international conference on Natural Computation, pages 2677-2681. IEEE, 2010.

[12] S. Song, Y. kwork, and K. Hwang. Security driven heuristics and a fast genetic algorithm for trusted grid job scheduling.2006.

[13] L. Wang, H. Seigel , V. Roychowdhury, and A. Maciejewski. Task matching and scheduling in heterogenous computing environments using a genetic algorithm based approach. Journal of parallel and distributed computing, 47:8-22, 1997.

[14] L. Zhang, Y. Chen, R. Sun and B. Jing, S ang Yang. A task scheduling algorithm based on pso grid computing. International journal of Computational Intelligence Reseach, 4:37-43, 2008

Untitled Page