EACS Approach for Grid Workflow Scheduling in a Computational Grid
Grid is a collection of heterogeneous resources for solving the complex computational problems. Workflow is a collection of atomic tasks. In this article we propose an Enhanced Ant Colony System (EACS) approach to solve grid workflow scheduling problem with two QoS parameters time and cost to minimize the makespan with low cost. We design a five heuristics for EACS approach and propose an adaptive scheme that allows ants to select heuristics in a quick convergence manner for mapping of tasks to resources based on the modified pheromone updating value. The experiment is done by the simulation with different tasks in various workflow applications and we achieve QoS as well as optimized performance.
KeywordsAnt Colony Optimization (ACO) Grid Computing Workflow Scheduling
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- 1.Foster, I., Kesselman, C.: The Grid: Blueprint for a Future Computing Infrastructure. Morgan Kaufmann Publishers, USA (1999)Google Scholar
- 2.Foster, I., Kesselman, C., Tuecke, S.: The anatomy of the Grid: Enabling scalable virtual organizations. International Journal Supercomputer Applications 15(3) (2001)Google Scholar
- 3.Kyriazis, D., et al.: An innovative workflow mapping mechanism for grids in the frame of quality of service. Future Gen. Comput. Syst. (to be published)Google Scholar
- 8.Li: Job scheduling and processor allocation for grid computing on Metacomputers. Journal of Parallel and Distributed Computing (2005)Google Scholar
- 10.Braun, T.D., Siegel, H.J., Beck, N., Bölöni, L.L., Maheswaran, M., Reuther, A.I., Robertson, J.P., Theys, M.D., Yao, B., Hensgen, D., Freund, R.F.: A Comparison of Eleven Static Heuristics for Mapping a Class of Independent Tasks onto Heterogeneous Distributed Computing Systems. Journal of Parallel and Distributed Computing 61(6), 810–837 (2001)CrossRefzbMATHGoogle Scholar
- 11.Casanova, H., Legrand, A., Zagorodnov, D., Berman, F.: Heuristics for Scheduling parameter sweep applications in Grid environment. In: Heterogeneous Computing Workshop 2000, pp. 349–363. IEEE Computer Society Press (2000)Google Scholar