Experiences on Grid Resource Selection Considering Resource Proximity

  • Eduardo Huedo
  • Rubén S. Montero
  • Ignacio M. Llorente
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2970)


Grids are by nature highly dynamic and heterogeneous environments, and this is specially the case for the performance of the interconnection links between grid resources. Therefore, grid resource selection should take into account the proximity of the computational resources to the needed data in order to reduce the cost of file staging. This fact is specially relevant in the case of adaptive job execution, since job migration requires the transfer of large restart files between the compute hosts. In this paper, we discuss the extension of the GridWay framework to also consider dynamic resource proximity to select grid resources, and to decide if job migration is feasible and worthwhile. The benefits of the new resource selector will be demonstrated for the adaptive execution of a computational fluid dynamics (CFD) code.


Grid Resource Resource Selection Resource Discovery Remote Host Submission Time 
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  1. 1.
    Foster, I., Kesselman, C.: Globus: A Metacomputing Infrastructure Toolkit. Intl. J. of Supercomputer Applications 11, 115–128 (1997)CrossRefGoogle Scholar
  2. 2.
    Schopf, J.M.: Ten Actions when Superscheduling. Technical Report GFD-I.4, The Global Grid Forum: Scheduling Working Group (2001)Google Scholar
  3. 3.
    Huedo, E., Montero, R.S., Llorente, I.M.: A Framework for Adaptive Execution on Grids. Intl. J. of Software – Practice and Experience (2004) (in press)Google Scholar
  4. 4.
    Allcock, W., et al.: Globus Toolkit Support for Distributed Data-Intensive Science. In: Proc. of Computing in High Energy Physics (2001)Google Scholar
  5. 5.
    Wolski, R., Spring, N., Hayes, J.: The Network Weather Service: A Distributed Resource Performance Forecasting Service for Metacomputing. J. of Future Generation Computing Systems 15, 757–768 (1999)CrossRefGoogle Scholar
  6. 6.
    Vazhkudai, S., Schopf, J., Foster, I.: Predicting the Performance of Wide-Area Data Transfers. In: Proc. of Intl. Parallel and Distributed Processing Symp. (2002)Google Scholar
  7. 7.
    Montero, R.S., Llorente, I.M., Salas, M.D.: Robust Multigrid Algorithms for the Navier-Stokes Equations. Journal of Computational Physics 173, 412–432 (2001)zbMATHCrossRefGoogle Scholar
  8. 8.
    Liu, C., Yang, L., Foster, I., Angulo, D.: Design and Evaluation of a Resource Selection Framework for Grid Applications. In: Proc. of the Symp. on High-Performance Distributed Computing (2002)Google Scholar
  9. 9.
    Vadhiyar, S., Dongarra, J.: A Performance Oriented Migration Framework for the Grid. In: Proc. of the Intl. Symp. on Cluster Computing and the Grid (2003)Google Scholar
  10. 10.
    Vazhkudai, S., Tuecke, S., Foster, I.: Replica Selection in the Globus Data Grid. In: Intl. Workshop on Data Models and Databases on Clusters and the Grid (2001)Google Scholar
  11. 11.
    Lamehamedi, H., Szymanski, B.K., Deelman, E.: Data Replication Strategies in Grid Environments. In: Proc. of 5th Intl. Conf. on Algorithms and Architectures for Parallel Processing (2002)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Eduardo Huedo
    • 1
  • Rubén S. Montero
    • 2
  • Ignacio M. Llorente
    • 1
    • 2
  1. 1.Laboratorio de Computación AvanzadaCentro de Astrobiología (CSIC-INTA)Torrejón de ArdozSpain
  2. 2.Departamento de Arquitectura de Computadores y AutomáticaUniversidad ComplutenseMadridSpain

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