Comparative Study of Ant Colony Optimization and Particle Swarm Optimization for Grid Scheduling

Volume 2, Issue 3, pp 469--474 http://dx.doi.org/10.22436/jmcs.02.03.10

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Authors

R. Shakerian - Young Researchers Club, Islamic Azad University, Ayatollah Amoli Branch S. H. Kamali - Islamic Azad University, Qazvin Branch M. Hedayati - Islamic Azad University, Ghaemshahr Branch M. Alipour - Amol General Education, Student of Payam Noor University, Babol, Iran

Abstract

This paper represents the comparative study of Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO) for Grid scheduling. The objective of ACO and PSO is to dynamically generate an optimal schedule so as to complete the tasks in minimum period of time as well as utilizing the resources in an efficient way. Makespan is the performance measure used for the comparison of the scheduling techniques. This paper compares both the above said optimization techniques and it is concluded that particle swarm optimization has better performance compared to ant colony optimization for grid scheduling.

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Keywords

• Grid Computing
• Grid Scheduling
• ACO
• PSO
• Optimal Schedule.

MSC

•  93E20
•  68T05
•  90B35

References

• [1] A. Abraham, H. Liu, C. Grosan, F. Xhafa, Nature Inspired Meta- heuristics for Grid Scheduling: Single and Multi-objective Optimization Approaches, In: Metaheuristics for Scheduling in Distributed Computing Environments, 247--272 (2008)
  • View Article
  • Google Scholar


• [2] R. Wankar, Grid computing with globus: an overview and research challenges, International Techno Mathematics Journal of Computer Science and Applications, 5 (2008), 56--69
  • View Article
  • Google Scholar


• [3] S. Benedict, R. S. Rejitha, V. Vasudevan, An Evolutionary Hybrid Scheduling Algorithm for Computational Grids, JACIII, Vol. 12, 479--484 (2008)
  • Google Scholar


• [4] E. G. Coffman, J. L. Bruno, Computer and Job-Shop Scheduling Theory, John Wiley & Sons, New York (1976)
  • Google Scholar


• [5] A. YarKhan, J. J. Dongarra, Experiments with scheduling using simulated annealing in a grid environment, In: International Workshop on Grid Computing, 232--242 (2002)
  • View Article
  • Google Scholar


• [6] V. Di Martino, Sub optimal scheduling in a grid using genetic algorithms, Parallel Computing, 30 (2004), 553--565
  • View Article
  • Google Scholar


• [7] D. Liu, Y. Cao, CGA: Chaotic Genetic Algorithm for Fuzzy Job Scheduling in Grid Environment, In: International Conference on Computational and Information Science, 133--143 (2006)
  • View Article
  • Google Scholar


• [8] R. Poli, J. Kennedy, T. Blackwell, Particle swarm optimization, Swarm Intell., 33--57 (2007)
  • View Article
  • Google Scholar


• [9] G. Kaur, I. Chopra, Grid Computing-Challenges Confronted and Opportunities Offered, Proceedings of COIT, (2007)
  • Google Scholar


• [10] S. Pandey, L. Wu, S. M. Guru, R. Buyya, A Particle Swarm Optimization (PSO)-based Heuristic for Scheduling Workflow Applications in Cloud Computing Environments, 24th IEEE international conference on Advanced information networking and applications (AINA), 400--407 (2010)
  • View Article
  • Google Scholar


• [11] K. Kousalya, P. Balasubramanie, An Enhanced Ant Algorithm for Grid Scheduling Problem, International Journal of Computer Science and Network Security, Vol. 8, 262--271 (2008)
  • Google Scholar


• [12] M. Shahzad, S. Zahid, M. Farooq, A Hybrid GA-PSO Fuzzy System for User Identification on Smart Phones, Proceedings of the 11th Annual conference on Genetic and evolutionary computation, 1617--1624 (2009)
  • View Article
  • Google Scholar


• [13] S. Fidanova, M. Durchova, Ant Algorithm for Grid Scheduling Problem, International Conference on Large-Scale Scientific Computing, 405--412 (2005)
  • View Article
  • Google Scholar


• [14] J. Kennedy, R. C. Eberhart, Y. Shi, Swarm Intelligence, Morgan Kaufmann Publishers, San Francisco (2001)
  • View Article
  • Google Scholar


• [15] L. Zhang, Y. Chen, R. Sun, S. Jing, B. Yang, A Task Scheduling Algorithm Based on PSO for Grid Computing, International Journal of Computational Intelligence Research, 4 (2008), 37--43
  • View Article
  • Google Scholar