Advertisement

Dynamic Objective and Advance Scheduling in Federated Grids

  • Katia Leal
  • Eduardo Huedo
  • Ignacio M. Llorente
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5331)

Abstract

In this paper we present a dynamic mapping strategy for scheduling independent tasks in Federated Grids. This strategy is performed in two steps: first we calculate a new objective, and then we apply advance scheduling to meet the new objective. The results obtained by simulation show that the combination of these two steps reduces the makespan and increases the throughput. Thus, the mapping strategy proposed meets two of the most common objective functions of tasks scheduling problems: makespan and performance of the resources. The presented algorithm is easy to implement, unlike Genetic Algorithms is fast enough to be used in a realistic scheduling, and is efficient. In addition, the information the strategy needs can be provided by any Grid Information Service, and its does not require the deployment of complex prediction services or service level agreement: it can work in any Grid.

Keywords

Federated Grids Planning Scheduling Independent Tasks 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Ullman, J.D.: NP-Complete Scheduling Problems. Journal of Computer and System Sciences 10(3), 384–393 (1975)MathSciNetCrossRefzbMATHGoogle Scholar
  2. 2.
    Berman, F.: High-Performance Schedulers. In: The Grid: Blueprint for a Future Computing Infrastructure. Morgan Kaufmann, San Francisco (1998)Google Scholar
  3. 3.
    Dong, F., Akl, S.G.: Scheduling Algorithms for Grid Computing: State of the Art and Open Problems. Technical Report 2006-504, Ontario Queenâs University (January 2006)Google Scholar
  4. 4.
    Andrieux, A., Berry, D., Garibaldi, J., Jarvis, S., MacLaren, J., Ouelhadj, D., Snelling, D.: Open Issues in Grid Scheduling. Technical Report ISSN 1751-5971, UK e-Science Institute (October 2003)Google Scholar
  5. 5.
    de Assuncao, M.D., Buyya, R., Venugopal, S.: InterGrid: A Case for Internetworking Islands of Grids. Concurrency and Computation: Practice and Experience (CCPE) (2007)Google Scholar
  6. 6.
    Leal, K., Huedo, E., Montero, R.S., Llorente, I.M.: Scheduling Strategies in Federated Grids. In: Proceedings of the 2008 High Performance Computing & Simulation Conference (HPCS 2008) (2008)Google Scholar
  7. 7.
    Montero, R.S., Huedo, E., Llorente, I.M.: Benchmarking of High Throughput Computing Applications on Grids. Parallel Computing 32(4), 267–269 (2006)CrossRefGoogle Scholar
  8. 8.
    Hockney, R., Jesshope, C.: Parallel Computers 2: Architecture, Programming, and Algorithms. Adam Hilger Ltd (1988)Google Scholar
  9. 9.
    Huedo, E., Montero, R.S., Llorente, I.M.: A Framework for Adaptive Execution on Grids. Software – Practice and Experience 34(7), 631–651 (2004)CrossRefGoogle Scholar
  10. 10.
    Casavant, T.L., Kuhl, J.G.: A taxonomy of scheduling in general-purpose distributed computing systems. IEEE Transactions on Software Engineering 14(2), 141–154 (1988)CrossRefGoogle Scholar
  11. 11.
    Braun, T.D., Siegel, H.J., Beck, N., Blni, L., Maheswaran, M., Reuther, A.I., Robertson, J.P., Theys, M.D., Yao, B.: A taxonomy for describing matching and scheduling heuristics for mixed-machine heterogeneous computing systems. In: The 17th IEEE Symposium on Reliable Distributed Systems (1998)Google Scholar
  12. 12.
    Yin, H., Wu, H., Zhou, J.: An Improved Genetic Algorithm with Limited Iteration for Grid Scheduling. In: Proceedings of the Sixth International Conference on Grid and Cooperative Computing (GCC 2007) (2007)Google Scholar
  13. 13.
    Freund, R.F., Gherrity, M., Ambrosius, S., Campbelly, M., Halderman, M., Hensgenz, D., Keithy, E., Kiddz, T., Kussowy, M., Limay, J.D., Mirabilea, F., Moorex, L., Rusty, B., Siegel, H.J.: Scheduling Resources in Multi-User, Heterogeneous, Computing Environments with SmartNet. In: Proceedings of the 7th International IEEE Heterogeneous Computing Workshop (HCW 1998) (1998)Google Scholar
  14. 14.
    Fujimoto, N., Hagihara, K.: A Comparison among Grid Scheduling Algorithms for Independent Coarse-Grained Tasks. In: Proceedings of the 2004 International Symposium and the Internet Workshops (SAINTW 2004) (2004)Google Scholar
  15. 15.
    Llorente, I.M., Montero, R.S., Huedo, E., Leal, K.: A Grid Infrastructure for Utility Computing. In: Proceedings of the Third International Workshop on Emerging Technologies for Next-generation GRID (ETNGRID 2006). IEEE Computer Society Press, Los Alamitos (2006)Google Scholar
  16. 16.
    Vázquez, C., Fontán, J., Huedo, E., Montero, R.S., Llorente, I.M.: A Performance Model for Federated Grid Infrastructures. In: Proceedings of the 16th Euromicro International Conference on Parallel, Distributed and network-based Processing (PDP 2008), pp. 188–192 (2008)Google Scholar
  17. 17.
    Vázquez, C., Huedo, E., Montero, R.S., Llorente, I.M.: Evaluation of A Utility Computing Mode based on Federation of Grid Infrastructures. In: Kermarrec, A.-M., Bougé, L., Priol, T. (eds.) Euro-Par 2007. LNCS, vol. 4641, pp. 372–381. Springer, Heidelberg (2007)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Katia Leal
    • 1
  • Eduardo Huedo
    • 2
  • Ignacio M. Llorente
    • 2
  1. 1.Departamento de Sistemas Telemáticos y Computación Escuela Superior de Ciencias Experimentales y Tecnología Tulipán SN, MóstelesUniversidad Rey Juan CarlosMadridSpain
  2. 2.Departamento de Arquitectura de Computadores y Automática Facultad de InformáticaUniversidad Complutense de MadridSpain

Personalised recommendations