Abstract
Scheduling is one of the most crucial issue in a grid environment because it strongly affects the performance of the whole system. In literature there are several algorithms that try to obtain the best performance possible for the specified requirements; taking into account that the issue of allocating jobs on resources is a combinatorial optimization problem, NP-hard in most cases, several heuristics have been proposed to provide good performance. In this work an algorithm inspired to Ant Colony Optimization theory is proposed: this algorithm, named Aliened Ant Algorithm, is based on a different interpretation of pheromone trails.
The goodness of the proposed algorithm, in term of load balancing and average queue waiting time, has been evaluated by mean of a vast campaign of simulations carried out on some real scenarios of a grid infrastructure.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Dorigo, M., Blum, C.: Ant colony optimization theory: a survey. Journal Theoretical Computer Science 344(2-3), 243–278 (2005)
Sun, K.M.S.W.H.: Ant colony optimization for routing and load-balancing: survey and new directions. IEEE Trans. on Systems, Man and Cybernetics, Part A 33(5), 560–572 (2003)
Stytzle, T., Hoos, H.H.: MAX-MIN Ant system. Future Generation Computer Systems 16(9), 889–914 (2000)
Dorigo, M., Maniezzo, V., Colorni, A.: Ant system: optimization by a colony of cooperating agents. IEEE Trans. on Systems, Man and Cybernetics, Part B 26(1), 29–41 (1996)
Dorigo, M., Gambardella, L.M.: Ant colony system: a cooperative learning approach to the traveling salesman problem. IEEE Trans. on Evolutionary Computation 1(1), 53–66 (1997)
Merkle, D., Middendorf, M., Schmeck, H.: Ant colony optimization for resource-constrained project scheduling. IEEE Trans. on Evolutionary Computation 6(4) (2003)
Blum, C., Sampels, M.: An ant colony optimization algorithm for shop scheduling problems. Journal of Mathematical Modeling and Algorithms 3(3) (2004)
Kesselman, C., Foster, I., Tuecke, S.: The Anatomy of the Grid - Enabling Scalable Virtual Organizations. International Journal of High Performance Computing Applications 15(3), 200–222 (2001)
Foster, I., Kesselman, C., Nick, J., Tuecke, S.: The Physiology of the Grid: An Open Grid Services Architecture for Distributed Systems Integration. Open Grid Service Infrastructure WG, Global Grid Forum (2002)
Foster, I., Kesselman, C.: The Grid: Blueprint for a New Computing Infrastructure. Morgan Kaufmann Publishers (1999) ISBN: 1-558660-475-8
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Bandieramonte, M., Di Stefano, A., Morana, G. (2008). An ACO Inspired Strategy to Improve Jobs Scheduling in a Grid Environment. In: Bourgeois, A.G., Zheng, S.Q. (eds) Algorithms and Architectures for Parallel Processing. ICA3PP 2008. Lecture Notes in Computer Science, vol 5022. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-69501-1_5
Download citation
DOI: https://doi.org/10.1007/978-3-540-69501-1_5
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-69500-4
Online ISBN: 978-3-540-69501-1
eBook Packages: Computer ScienceComputer Science (R0)