Abstract
The efficient scheduling of the independent and sequential tasks on distributed and heterogeneous computing resources within grid computing environments is an NP-complete problem. Therefore, using heuristic approaches to solve the scheduling problem is a very common and also acceptable method in these environments. In this paper, a new task scheduling algorithm based on bee colony optimization approach is proposed. The algorithm uses artificial bees to appropriately schedule the submitted tasks to the grid resources. Applying the proposed algorithm to the grid computing environments, the maximum delay and finish times of the tasks are reduced. Furthermore, the total makespan of the environment is minimized when the algorithm is applied. The proposed algorithm not only minimizes the makespan of the environment, but also satisfies the deadline and priority requirements of the tasks. Simulation results obtained from applying the algorithm to different grid environments show the prominence of the algorithm to other similar scheduling algorithms.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Foster, I., Kesselman, C.: The Grid 2: Blueprint for a New Computing Infrastructure, 2nd edn. Elsevier and Morgan Kaufmann Press (2004)
Khanli, L.M., Analoui, M.: Resource Scheduling in Desktop Grid by Grid-jqa. In: The 3rd IEEE International Conference on Grid and Pervasive Computing, pp. 63–68 (2008)
Parsa, S., Entezari-Maleki, R.: Rasa: A New Grid Task Scheduling Algorithm. International Journal of Digital Content Technology and its Applications 3(4), 91–99 (2009)
Ferreira, L., Berstis, V., Armostrong, J., Kendzierski, M., Neukoetter, A., Takagi, M., Bing-Wo, R., Amir, A., Murakawa, R., Hernandez, O., Magowan, J., Ieberstein, N.: Introduction to Grid Computing with Globus, 2nd edn, IBM International Technical Support Organization (2003)
Teodorovic, D., Davidovic, T., Selmic, M.: Bee colony optimization: The Applications Survey. ACM Transactions on Computational Logic (Published online)
Bonabeau, E., Dorigo, M., Theraulaz, G.: Swarm intelligence, 1st edn. Oxford University Press, Oxford (1997)
Yanli, H., Lining, X., Weiming, Z., Weidong, X., Daquan, T.: A knowledge-based ant colony optimization for a grid workflow scheduling problem. In: Tan, Y., Shi, Y., Tan, K.C. (eds.) ICSI 2010. LNCS, vol. 6145, pp. 241–248. Springer, Heidelberg (2010)
Zheng, S., Shu, W., Gao, L.: Task Scheduling Using Parallel Genetic Simulated Annealing Algorithm. In: IEEE International Conference on Service Operations and Logistics, and Informatics, pp. 46–50 (2006)
Liu, A., Wang, Z.: Grid Task Scheduling Based on Adaptive Ant Colony Algorithm. In: International Conference on Management of e-Commerce and e-Government, pp. 415–418 (2008)
He, X., Sun, X.H., Laszewski, G.V.: Qos Guided Min-Min Heuristic for Grid task Scheduling. Journal of Computer Science and Technology 18(4), 442–451 (2003)
Entezari-Maleki, R., Movaghar, A.: A Genetic-Based Scheduling Algorithm to Minimize the Makespan of the Grid Applications. In: Kim, T., Yau, S., Gervasi, O., Kang, B., Stoica, A., Slezak, D. (eds.) GDC and CA 2010. CCIS, vol. 121, pp. 22–31. Springer, Heidelberg (2010)
Davidovic, T., Selmic, M., Teodorovic, D.: Scheduling independent tasks: Bee Colony Optimization Approach. In: 17th Mediterranean Conference on Control and Automation, pp. 1020–1025 (2009)
Wong, L.P., Low, M.Y.H., Chong, C.S.: A Bee Colony Optimization Algorithm for Traveling Salesman Problem. In: 6th IEEE International Conference on Industrial Informatics, pp. 1019–1025 (2008)
Chong, C.S., Low, M.Y.H., Sivakumar, A.I., Gay, K.L.: A Bee Colony Optimization Algorithm to Job Shop Scheduling Simulation. In: Perrone, L., Wieland, F., Liu, J., Lawson, B., Nicol, D., Fujimoto, R. (eds.) The Winter Simulation Conference, pp. 1954–1961 (2006)
Quijano, N., Passino, K., Univ, M.: Honey Bee Social Foraging Algorithms for Resource Allocation. In: American Control Conference, pp. 3389–3394 (2007)
Lucic, P., Teodorovic, D.: Computing with Bees: Attacking Complex Transportation Engineering Problems. International Journal on Artificial Intelligence Tools 12(2), 375–394 (2003)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Mousavinasab, Z., Entezari-Maleki, R., Movaghar, A. (2011). A Bee Colony Task Scheduling Algorithm in Computational Grids. In: Snasel, V., Platos, J., El-Qawasmeh, E. (eds) Digital Information Processing and Communications. ICDIPC 2011. Communications in Computer and Information Science, vol 188. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-22389-1_19
Download citation
DOI: https://doi.org/10.1007/978-3-642-22389-1_19
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-22388-4
Online ISBN: 978-3-642-22389-1
eBook Packages: Computer ScienceComputer Science (R0)