Zhongxue Yang
College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, 210016, Nanjing, China
Xiaolin Qin
College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, 210016, Nanjing, China
Wenrui Li
School of Mathematics and Information Technology, Nanjing Xiaozhuang University, 211171, Nanjing, China
Yingjie Yang
Centre for Computational Intelligence, De Montfort University, Leicester, LE19BH, UK
ABSTRACT
Cloud computing environment can offer dynamic and elastic virtual resources to cloud users on demand. It becomes an attractive challenge that how task scheduling satisfy the dynamic requirements of users and utilize the virtual resources efficiently in cloud environment. Particle Swarm Optimization (PSO) is a global metaheuristic method to solve optimization issues. The processing capacity (cost) and makespan associated with the task schedule and the resources allocated are taken into account to measure the performance of optimization algorithm in this study. PSO based fitness function scheduling heuristic to balance the load across the entire system is introduced while trying to minimize the makespan and increase the processing capacity. For comparison, ant colony algorithm is presented to simulate on the same datasets. The experiments results show that PSO based fitness function is more effective and efficient with shorter completion time and lower cost.
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How to cite this article
Zhongxue Yang, Xiaolin Qin, Wenrui Li and Yingjie Yang, 2013. Optimized Task Scheduling and Resource Allocation in Cloud Computing Using
PSO based Fitness Function. Information Technology Journal, 12: 7090-7095.
DOI: 10.3923/itj.2013.7090.7095
URL: https://scialert.net/abstract/?doi=itj.2013.7090.7095
DOI: 10.3923/itj.2013.7090.7095
URL: https://scialert.net/abstract/?doi=itj.2013.7090.7095
REFERENCES
- Bhupendra, P. and R.K. Kapoor, 2013. Dynamic VM allocation algorithm using clustering in cloud computing. Int. J. Adv. Res. Comput. Sci. Software Eng., 3: 143-150.
Direct Link - Cai, J. and W.D. Pan, 2012. On fast and accurate block-based motion estimation algorithms using particle swarm optimization. Inform. Sci., 197: 53-64.
CrossRefDirect Link - Du, W. and B. Li, 2008. Multi-strategy ensemble particle swarm optimization for dynamic optimization. Inform. Sci., 178: 3096-3109.
CrossRef - Eberhart, R.C. and J. Kennedy, 1995. A new optimizer using particle swarm theory. Proceedings of the 6th International Symposium on Micro Machine and Human Science, October 4-6, 1995, Nagoya, Japan, pp: 39-43.
CrossRefDirect Link - Chuanwen, J. and E. Bompard, 2005. A hybrid method of chaotic particle swarm optimization and linear interior for reactive power optimisation. Math. Comput. Simul., 68: 57-65.
CrossRef - Kennedy, J. and R. Eberhart, 1995. Particle swarm optimization. Proc. IEEE Int. Conf. Neural Networks, 4: 1942-1948.
CrossRefDirect Link - Li, Y., Y. Cao, Z. Liu, Y. Liu and Q. Jiang, 2009. Dynamic optimal reactive power dispatch based on parallel particle swarm optimization algorithm. Comput. Math. Appl., 57: 1835-1842.
CrossRef - Li, Y., P.P. Jing, D.F. Hu, B.H. Zhang and C.X. Mao et al., 2009. Optimal reactive power dispatch using particle swarms optimization algorithm based Pareto optimal set. Proceedings of the 6th International Symposium on Neural Networks, May 26-29, 2009, Wuhan, China, pp: 152-161.
CrossRef - Zhang, Y., D.W. Gong and Z. Ding, 2012. A bare-bones multi-objective particle swarm optimization algorithm for environmental/economic dispatch. Inform. Sci., 192: 213-227.
CrossRef