A Multiobjective Evolutionary Approach for Multisite Mapping on Grids

  • Ivanoe De Falco
  • Antonio Della Cioppa
  • Umberto Scafuri
  • Ernesto Tarantino
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4967)

Abstract

Grid systems, constituted by multisite and multi–owner time–shared resources, make a great amount of locally unemployed computational power accessible to users. To profitably exploit this power for processing computationally intensive grid applications, an efficient multisite mapping must be conceived. The mapping of cooperating and communicating application subtasks, already known as NP–complete for parallel systems, results even harder in grid computing because the availability and workload of grid resources change dynamically, so evolutionary techniques can be adopted to find near–optimal solutions. In this paper a mapping tool based on a multiobjective Differential Evolution algorithm is presented. The aim is to reduce the execution time of the application by selecting among all the potential solutions the one which minimizes the degree of use of the grid resources and, at the same time, complies with Quality of Service requirements. The proposed mapper is assessed on some artificial problems differing in application sizes and workload constraints.

Keywords

Grid computing mapping Differential Evolution 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Foster, I., Kesselmann, C.: The Grid: Blueprint for a New Computing Infrastructure. Morgan Kaufmann, San Francisco (1999)Google Scholar
  2. 2.
    Mateescu, G.: Quality of service on the grid via metascheduling with resource co-scheduling and co-reservation. International Journal of High Performance Computing Applications 17(3), 209–218 (2003)CrossRefMathSciNetGoogle Scholar
  3. 3.
    Snir, M., Otto, S., Huss-Lederman, S., Walker, D., Dongarra, J.: MPI: The Complete Reference. The MPI Core, vol. 1. The MIT Press, Cambridge (1998)Google Scholar
  4. 4.
    Khokhar, A., Prasanna, V.K., Shaaban, M., Wang, C.L.: Heterogeneous computing: Challenges and opportunities. IEEE Computer 26(6), 18–27 (1993)Google Scholar
  5. 5.
    Siegel, H.J., Antonio, J.K., Metzger, R.C., Tan, M., Li, Y.A.: Heterogeneous computing. In: Zomaya, A.Y. (ed.) Parallel and Distributed Computing Handbook, pp. 725–761. McGraw–Hill, New York (1996)Google Scholar
  6. 6.
    Foster, I.: Globus toolkit version 4: Software for service–oriented systems. In: Jin, H., Reed, D., Jiang, W. (eds.) NPC 2005. LNCS, vol. 3779, pp. 2–13. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  7. 7.
    Fernandez-Baca, D.: Allocating modules to processors in a distributed system. IEEE Transaction on Software Engineering 15(11), 1427–1436 (1989)CrossRefGoogle Scholar
  8. 8.
    Wang, L., Siegel, J.S., Roychowdhury, V.P., Maciejewski, A.A.: Task matching and scheduling in heterogeneous computing environments using a genetic–algorithm–based approach. Journal of Parallel and Distributed Computing 47, 8–22 (1997)CrossRefGoogle Scholar
  9. 9.
    Braun, T.D., Siegel, H.J., N.B.,, Bölöni, L.L., Maheswaran, M., Reuther, A.I., Robertson, J.P., Theys, M.D., Yao, B.: 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, 810–837 (2001)CrossRefGoogle Scholar
  10. 10.
    Kim, S., Weissman, J.B.: A genetic algorithm based approach for scheduling decomposable data grid applications. In: International Conference on Parallel Processing (ICPP 2004), Montreal, Quebec, Canada, pp. 406–413 (2004)Google Scholar
  11. 11.
    Song, S., Kwok, Y.K., Hwang, K.: Security–driven heuristics and a fast genetic algorithm for trusted grid job scheduling. In: IPDP 2005, Denver, Colorado (2005)Google Scholar
  12. 12.
    Price, K., Storn, R.: Differential evolution. Dr. Dobb’s Journal 22(4), 18–24 (1997)MathSciNetGoogle Scholar
  13. 13.
    Fonseca, C.M., Fleming, P.J.: An overview of evolutionary algorithms in multiobjective optimization. Evolutionary Computation 3(1), 1–16 (1995)CrossRefGoogle Scholar
  14. 14.
    Dong, F., Akl, S.G.: Scheduling algorithms for grid computing: State of the art and open problems. Technical Report2006–504, School of Computing, Queen (2006)Google Scholar
  15. 15.
    Fitzgerald, S., Foster, I., Kesselman, C., von Laszewski, G., Smith, W., Tuecke, S.: A directory service for configuring high-performance distributed computations. In: Sixth Symp. on High Performance Distributed Computing, Portland, OR, USA, pp. 365–375. IEEE Computer Society, Los Alamitos (1997)CrossRefGoogle Scholar
  16. 16.
    Czajkowski, K., Fitzgerald, S., Foster, I., Kesselman, C.: Grid information services for distributed resource sharing. In: Tenth Symp. on High Performance Distributed Computing, San Francisco, CA, USA, pp. 181–194. IEEE Computer Society, Los Alamitos (2001)CrossRefGoogle Scholar
  17. 17.
    Wolski, R., Spring, N., Hayes, J.: The network weather service: a distributed resource performance forecasting service for metacomputing. Future Generation Computer Systems 15(5–6), 757–768 (1999)CrossRefGoogle Scholar
  18. 18.
    Gong, L., Sun, X.H., Waston, E.: Performance modeling and prediction of non–dedicated network computing. IEEE Trans. on Computer 51(9), 1041–1055 (2002)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Ivanoe De Falco
    • 1
  • Antonio Della Cioppa
    • 2
  • Umberto Scafuri
    • 1
  • Ernesto Tarantino
    • 1
  1. 1.ICAR–CNRNaplesItaly
  2. 2.DIIIEUniversity of SalernoFisciano (SA)Italy

Personalised recommendations