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Grid Resource Selection for Opportunistic Job Migration

  • Rubén S. Montero
  • Eduardo Huedo
  • Ignacio M. Llorente
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2790)

Abstract

The ability to migrate running applications among different grid resources is generally accepted as the solution to adapt to dynamic resource load, availability and cost. In this paper we focus on opportunistic migration when a new resource becomes available in the Grid. In this situation the performance of the new host, the remaining execution time of the application, and also the proximity of the new resource to the needed data, become critical factors to decide if job migration is feasible and worthwhile. We discuss the extension of the GridWay framework to consider all the previous factors in the resource selection and migration stages in order to improve response times of individual applications. The benefits of the new resource selector will be demonstrated for the execution of a computational fluid dynamics (CFD) code.

Keywords

Grid Resource Resource Selection Computational Fluid Dynamic Code Candidate Resource Candidate Host 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Liu, C., Yang, L., Foster, I., Angulo, D.: Design and Evaluation of a Resource Selection Framework for Grid Applications. In: Proceedings of the 11th IEEE Symposium on High-Performance Distributed Computing (2002)Google Scholar
  2. 2.
    Kennedy, K., et al.: Toward a Framework for Preparing and Execution Adaptive Grid Applications. In: Proceedings of NSF Next Generation Systems Program Workshop, International Parallel and Distributed Processing Symposium (2002)Google Scholar
  3. 3.
    Allcock, W., Chervenak, A., Foster, I., Pearlman, L., Welch, V., Wilde, M.: Globus Toolkit Support for Distributed Data-Intensive Science. In: Proceedings of Computing in High Energy Physics, CHEP 2001 (2001)Google Scholar
  4. 4.
    Evers, X., de Jongh, J.F.C.M., Boontje, R., Epema, D.H.J., van Dantzig, R.: Condor Flocking: Load Sharing Between Pools of Workstations. Technical Report DUT-TWI-93-104, Delft, The Netherlands (1993)Google Scholar
  5. 5.
    Vadhiyar, S., Dongarra, J.: A Performance Oriented Migration Framework for the Grid. In: Proceedings of the 3rd IEEE/ACM Int’l Symposium on Cluster Computing and the Grid, CCGrid (2003)Google Scholar
  6. 6.
    Wolski, R., Shao, G., Berman, F.: Predicting the Cost of Redistribution in Schedulling. In: Proceedings of the 8th SIAM Conference on Parallel Processing for Scientific Applications (1997)Google Scholar
  7. 7.
    Allen, G., et al.: The Cactus Worm: Experiments with Dynamic Resource Discovery and Allocation in a Grid Environment. International Journal of High- Performance Computing Applications 15 (2001)Google Scholar
  8. 8.
    Huedo, E., Montero, R.S., Llorente, I.M.: An Experimental Framework for Executing Applications in Dynamic Grid Environments. Technical Report 2002-43, ICASE NASA Langley, submitted to Intl. J. Software Practice & Experience (2002)Google Scholar
  9. 9.
    Wolski, R., Spring, N., Hayes, J.: The Network Weather Service: A Distributed Resource Performance Forecasting Service for Metacomputing. Journal of Future Generation Computing Systems 15, 757–768 (1999)CrossRefGoogle Scholar
  10. 10.
    Vazhkudai, S., Schopf, J., Foster, I.: Predicting the Performance of Wide-Area Data Transfers. In: Proceedings of 16th Int’l Parallel and Distributed Processing Symposium, IPDPS 2002 (2002)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Rubén S. Montero
    • 1
  • Eduardo Huedo
    • 2
  • Ignacio M. Llorente
    • 1
    • 2
  1. 1.Departamento de Arquitectura de Computadores y AutomáticaUniversidad ComplutenseMadridSpain
  2. 2.Centro de Astrobiología (Associated to NASA Astrobiology Institute)CSIC-INTATorrejón de ArdozSpain

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