Summary
Recently, the scheduling problem in distributed data-intensive computing environments has been an active research topic. This Chapter models the scheduling problem for work-flow applications in distributed data-intensive computing environments (FDSP) and makes an attempt to formulate the problem. Several meta-heuristics inspired from particle swarm optimization algorithm are proposed to formulate efficient schedules. The proposed variable neighborhood particle particle swarm optimization algorithm is compared with a multi-start particle swarm optimization and multi-start genetic algorithm. Experiment results illustrate the algorithm performance and its feasibility and effectiveness for scheduling work-flow applications.
Access provided by Autonomous University of Puebla. Download to read the full chapter text
Chapter PDF
Similar content being viewed by others
Key words
References
I. Foster and C. Kesselman (Eds.) “ The Grid: Blueprint for a New Computing Infrastructure”. Morgan-Kaufmann, 1998. S. Venugopal, and R. Buyya. “A Set Coverage-based Mapping Heuristic for Scheduling Distributed Data-Intensive Applications on Global Grids”. Technical Report, GRIDS-TR-2006-3, Grid Computing and Distributed Systems Laboratory, The University of Melbourne, Australia, March 8, 2006.
N. Zhong, J. Hu, S. Motomura, J. Wu, and C. Liu. “Building A Data-mining Grid for Multiple Human Brain Data Analysis”. Computational Intelligence, 2005, 21(2), pp. 177.
F. Dong, and S.G. Akl. “Scheduling Algorithms for Grid Computing: State of the Art and Open Problems”. Technical Report, 2006-504, School of Computing, Queen’s University, Canada, January 2006.
K.E. Parsopoulos, and M.N. Vrahatis. “Recent Approaches to Global Optimization Problems through Particle Swarm Optimization”. Natural Computing, 2002, 1, pp. 235–306.
J. Kennedy, and R. Eberhart. Swarm Intelligence. Morgan Kaufmann, CA, 2001.
M. Clerc. Particle Swarm Optimization. ISTE Publishing Company, London, 2006.
R.C. Eberhart, and Y. Shi. “Comparison Between Genetic Algorithms And Particle Swarm Optimization”. Proceedings of IEEE International Conference on Evolutionary Computation, 1998, pp. 611–616.
D.W. Boeringer, and D.H. Werner. “Particle Swarm Optimization versus Genetic Algorithms for Phased Array Synthesis”. IEEE Transactions on Antennas and Propagation, 2004, 52(3), pp. 771–779.
A. Abraham, H. Guo, and H. Liu. “Swarm intelligence: Foundations, Perspectives And Applications”. Swarm Intelligent Systems, Nedjah N, Mourelle L (eds.), Nova Publishers, USA, 2006.
J. Kennedy J and R. Mendes. “Population structure and particle swarm performance”. Proceeding of IEEE conference on Evolutionary Computation, 2002, pp. 1671–1676.
H. Liu, B. Li, Y. Ji and T. Sun. “Particle Swarm Optimisation from lbest to gbest”. Applied Soft Computing Technologies: The Challenge of Complexit, Springer Verlag, 2006, pp. 537–545.
Y. H. Shi and R. C. Eberhart. “Fuzzy adaptive particle swarm optimization”. Proceedings of IEEE International Conference on Evolutionary Computation, 2001, pp. 101–106.
H. Liu and A. Abraham. “Fuzzy Adaptive Turbulent Particle Swarm Optimization”. Proceedings of the Fifth International conference on Hybrid Intelligent Systems, 2005, pp. 445–450.
P. Hansen and N. Mladenović. “Variable neighbourhood search:Principles and applications”. European Journal of Operations Research, 2001, 130, pp. 449–467.
P. Hansen and N. Mladenović. “Variable neighbourhood search”. Handbook of Metaheuristics, Dordrecht, Kluwer Academic Publishers, 2003.
M. Clerc, and J. Kennedy. “The Particle Swarm-explosion, Stability, and Convergence in A Multidimensional Complex Space”. IEEE Transactions on Evolutionary Computation, 2002, 6, pp. 58–73.
X. Jin and G. Min, Performance analysis of priority scheduling mechanisms under heterogeneous network traffic Journal of Computer and System Sciences, Volume 73, Issue 8, pp. 1207-1220, 2007.
F. Sabrina, C.D. Nguyen, S. Jha, D. Platt and F. Safaei, Processing resource scheduling in programmable networks Computer Communications, Volume 28, Issue 6, pp. 676-687, 2005.
L.C.A. Rodrigues, R. Carnieri and F. Neves Jr., Scheduling of continuous processes using constraint-based search: An application to branch and bound Computer Aided Chemical Engineering, Volume 10, pp. 751-756, 2002.
A. Abraham, H. Liu, and T.G. Chang, Variable Neighborhood Particle Swarm Optimization Algorithm, Genetic and Evolutionary Computation Conference (GECCO-2006), Seattle, USA, 2006.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Abraham, A., Liu, H., Zhao, M. (2008). Particle Swarm Scheduling for Work-Flow Applications in Distributed Computing Environments. In: Xhafa, F., Abraham, A. (eds) Metaheuristics for Scheduling in Industrial and Manufacturing Applications. Studies in Computational Intelligence, vol 128. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-78985-7_13
Download citation
DOI: https://doi.org/10.1007/978-3-540-78985-7_13
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-78984-0
Online ISBN: 978-3-540-78985-7
eBook Packages: EngineeringEngineering (R0)