A First Step Towards Automatically Building Network Representations

  • Lionel Eyraud-Dubois
  • Arnaud Legrand
  • Martin Quinson
  • Frédéric Vivien
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4641)

Abstract

To fully harness Grids, users or middlewares must have some knowledge on the topology of the platform interconnection network. As such knowledge is usually not available, one must uses tools which automatically build a topological network model through some measurements. In this article, we define a methodology to assess the quality of these network model building tools, and we apply this methodology to representatives of the main classes of model builders and to two new algorithms. We show that none of the main existing techniques build models that enable to accurately predict the running time of simple application kernels for actual platforms. However some of the new algorithms we propose give excellent results in a wide range of situations.

Keywords

Network model topology reconstruction Grids 

References

  1. 1.
    Foster, I.: The Grid 2: Blueprint for a New Computing Infrastructure. Morgan Kaufmann, San Francisco (2004)Google Scholar
  2. 2.
    Legrand, A., Renard, H., Robert, Y., Vivien, F.: Mapping and load-balancing iterative computations on heterogeneous clusters with shared links. IEEE Trans. Parallel Distributed Systems 15(6), 546–558 (2004)CrossRefGoogle Scholar
  3. 3.
    Chouhan, P.K., Dail, H., Caron, E., Vivien, F.: Automatic middleware deployment planning on clusters. IJHPCA 20(4), 517–530 (2006)Google Scholar
  4. 4.
    Kielmann, T., Hofman, R.F.H., Bal, H.E., Plaat, A., Bhoedjang, R.A.F.: MagPIe: MPI’s collective communication operations for clustered wide area systems. ACM SIGPLAN Notices 34(8), 131–140 (1999)CrossRefGoogle Scholar
  5. 5.
    Eyraud, L., Quinson, M.: Assessing the quality of automatically built network representations. In: 1st Workshop on Programming Models for Grid Computing (in Proceedings of CCGrid 2007 (2007)Google Scholar
  6. 6.
    Dinda, P., Gross, T., Karrer, R., Lowekamp, B., Miller, N., Steenkiste, P., Sutherland, D.: The architecture of the remos system. In: HPDC-10 (2001)Google Scholar
  7. 7.
    den Burger, M., Kielmann, T., Bal, H.E.: TOPOMON: A monitoring tool for grid network topology. In: Sloot, P.M.A., Tan, C.J.K., Dongarra, J.J., Hoekstra, A.G. (eds.) Computational Science - ICCS 2002. LNCS, vol. 2330, pp. 558–567. Springer, Heidelberg (2002)Google Scholar
  8. 8.
    Burch, H., Cheswick, B., Wool, A.: Internet mapping project, http://www.lumeta.com/mapping.html
  9. 9.
    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 (October 2001)Google Scholar
  10. 10.
    Ng, T., Zhang, H.: Predicting internet network distance with coordinates-based approaches. In: INFOCOM. 1, 170–179 (2002)Google Scholar
  11. 11.
    The cooperative association for internet data analysis, http://www.caida.org/
  12. 12.
    Downey, A.B.: Using pathchar to estimate internet link characteristics. In: Measurement and Modeling of Computer Systems, pp. 222–223 (1999)Google Scholar
  13. 13.
    Wolski, R., Spring, N.T., Hayes, J.: The Network Weather Service: A distributed resource performance forecasting service for metacomputing. Future Generation Computing Systems, Metacomputing Issue 15(5–6), 757–768 (1999)CrossRefGoogle Scholar
  14. 14.
    Foster, I., Kesselman, C.: Globus: A Metacomputing Infrastructure Toolkit. International Journal of Supercomputing Applications 11(2), 115–128 (1997)CrossRefGoogle Scholar
  15. 15.
    Caron, E., Desprez, F.: DIET: A scalable toolbox to build network enabled servers on the grid. IJHPCA 20(3), 335–352 (2006)Google Scholar
  16. 16.
    Shao, G., Berman, F., Wolski, R.: Using effective network views to promote distributed application performance. In: PDPTA (June 1999)Google Scholar
  17. 17.
    Lowekamp, B., Beguelin, A.: ECO: Efficient collective operations for communication on heterogeneous networks. In: IPDPS 1996 (1999)Google Scholar
  18. 18.
    Byers, J.W., Bestavros, A., Harfoush, K.: Inference and labeling of metric-induced network topologies. IEEE TPDS 16(11), 1053–1065 (2005)Google Scholar
  19. 19.
    Quinson, M.: GRAS: A research & development framework for grid and P2P infrastructures. In: PDCS 2006 (2006)Google Scholar
  20. 20.
    Legrand, A., Quinson, M., Fujiwara, K., Casanova, H.: The SimGrid project - simulation and deployment of distributed applications. In: HPDC-15, IEEE Computer Society Press, Los Alamitos (2006)Google Scholar
  21. 21.
    Casanova, H.: Modeling large-scale platforms for the analysis and the simulation of scheduling strategies. In: IPDPS (April 2004)Google Scholar
  22. 22.
    Tsafrir, D., Etsion, Y., Feitelson, D.G.: Backfilling using system-generated predictions rather than user runtime estimates. IEEE TPDS 18(6), 789–803 (2007)Google Scholar
  23. 23.
    Blackford, L.S., Choi, J., Cleary, A., D’Azevedo, E., Demmel, J., Dhillon, I., Dongarra, J., Hammarling, S., Henry, G., Petitet, A., Stanley, K., Walker, D., Whaley, R.C.: ScaLAPACK Users’ Guide. SIAM (1997)Google Scholar
  24. 24.
    Lu, D., Dinda, P.: Synthesizing realistic computational grids. In: Proceedings of ACM/IEEE Supercomputing 2003 (SC 2003) (November 2003)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Lionel Eyraud-Dubois
    • 1
  • Arnaud Legrand
    • 2
  • Martin Quinson
    • 3
  • Frédéric Vivien
    • 4
    • 1
  1. 1.LIP, UniversitÉ de Lyon - CNRS - INRIA, LyonFrance
  2. 2.LIG - MESCAL, UJF - INPG - CNRS - INRIA, GrenobleFrance
  3. 3.Nancy University - LORIA, NancyFrance
  4. 4.INRIA 

Personalised recommendations