Adaptable Distance-Based Decision-Making Support in Dynamic Cross-Grid Environment

  • Julien Gossa
  • Jean-Marc Pierson
  • Lionel Brunie
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4641)


The grid environment presents numerous opportunities for business applications as well as for scientific ones. Nevertheless the current trends seem to lead to several independent specialized grids in opposition to the early visions of one generic world wide grid. In such a cross-grid context, the environment might be harder to manipulate whereas more decisions must be handled from user-side. Our proposal is a distance-based decision-making support designed to be usable, adaptable and accurate. Our main contribution is to ensure the profitability of classical monitoring solutions by improving their usability. Our approach is illustrated and validated with experiments in a real grid environment.


Grid Environment Network Distance Signature Computation Grid Middlewares Network Weather 
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.


  1. 1.
    de Assuncao, M.D., Buyya, R.: A case for the world wide grid. Technical Report GRIDS-TR-2006-1, GRIDS Laboratory, Mel bourne University, Australia (February 2006)Google Scholar
  2. 2.
    Lowekamp, B., Tierney, B., Cottrell, L., Hughes-Jones, R., Kielmann, T., Swany, M.: A hierarchy of network performance characteristics for grid applications and services. In: Global Grid Forum (June 2004)Google Scholar
  3. 3.
    Globus_Alliance: Monitoring and discovery service,
  4. 4.
    Cooke, A., Gray, A., et al.: The relational grid monitoring architecture: Mediating information about the grid (2004)Google Scholar
  5. 5.
    Truong, H.L., Fahringer, T.: Scalea-g: a unified monitoring and performance analysis system for the grid, 12(4), 225–237 (2004)Google Scholar
  6. 6.
    Wolski, R., Spring, N.T., 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
  7. 7.
    Francis, P., Jamin, S., Jin, C., Jin, Y., Raz, D., Shavitt, Y., Zhang, L.: IDMaps: A global Internet host distance estimation service. IEEE/ACM Transactions on Networking 9(5), 525–540 (2001)CrossRefGoogle Scholar
  8. 8.
    Ng, T.S.E., Zhang, H.: Predicting internet network distance with coordinates-based approaches. In: Proceedings IEEE INFOCOM 2002, vol. 1, pp. 170–179. IEEE, Los Alamitos (2002)Google Scholar
  9. 9.
    Faerman, M., Su, A., Wolski, R., Berman, F.: Adaptive performance prediction for distributed data-intensive applications. In: ACM/IEEE SC99 Conference on High Performance Networking and Computing, Portland, OR, USA, IEEE Computer Society Press, Los Alamitos (1999)Google Scholar
  10. 10.
  11. 11.
    JEP - Java Math Expression Parser. Singular Systems,
  12. 12.
    Gossa, J.: Evaluation of network distances properties by nds, the network distance service. In: 3th International Workshop on Networks for Grid Applications (GRIDNETS 2006), IEEE/Create-Net (October 2006)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Julien Gossa
    • 1
  • Jean-Marc Pierson
    • 2
  • Lionel Brunie
    • 1
  1. 1.LIRIS INSA-Lyon, UMR5205 F-69621France
  2. 2.IRIT University Paul Sabatier UMR5505 F-31062, France 

Personalised recommendations