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EACS Approach for Grid Workflow Scheduling in a Computational Grid

  • E. Saravana Kumar
  • A. Sumathi
Part of the Communications in Computer and Information Science book series (CCIS, volume 250)

Abstract

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.

Keywords

Ant Colony Optimization (ACO) Grid Computing Workflow Scheduling 

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References

  1. 1.
    Foster, I., Kesselman, C.: The Grid: Blueprint for a Future Computing Infrastructure. Morgan Kaufmann Publishers, USA (1999)Google Scholar
  2. 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. 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
  4. 4.
    Shi, Z., Dongarra, J.J.: Scheduling workflow applications on processors with different capabilities. Future Gen. Comput. Syst. 22, 665–675 (2006)CrossRefGoogle Scholar
  5. 5.
    Garey, M.R., Johnson, D.S.: Computers and Intractability: A Guide to the Theory of NP-Completeness. Freeman, New York (1979)zbMATHGoogle Scholar
  6. 6.
    Feitelson, K.D.G., Rudolph, L., Schwiegelshohn, U., Sevcik, K.C., Wong, P.: Theory and practice in parallel job scheduling. In: Feitelson, D.G., Rudolph, L. (eds.) IPPS-WS 1997 and JSSPP 1997. LNCS, vol. 1291, pp. 1–34. Springer, Heidelberg (1997)CrossRefGoogle Scholar
  7. 7.
    Krallmann, J., Schwiegelshohn, U., Yahyapour, R.: On the design and evaluation of job scheduling algorithms. In: Feitelson, D.G., Rudolph, L. (eds.) JSSPP 1999, IPPS-WS 1999, and SPDP-WS 1999. LNCS, vol. 1659, pp. 17–42. Springer, Heidelberg (1999)CrossRefGoogle Scholar
  8. 8.
    Li: Job scheduling and processor allocation for grid computing on Metacomputers. Journal of Parallel and Distributed Computing (2005)Google Scholar
  9. 9.
    Krauter, K., Buyya, R., Maheswaran, M.: A taxonomy and survey of Grid resource management systems for distributed computing. Software Pract. Exp. 2, 135–164 (2002)CrossRefzbMATHGoogle Scholar
  10. 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. 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

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • E. Saravana Kumar
    • 1
  • A. Sumathi
    • 2
  1. 1.Anna University of TechnologyCoimbatoreIndia
  2. 2.Dept of ECEAdhiyamaan College of EngineeringHosurIndia

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