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Probabilistic Inference for Network Management

  • Jianguo Ding
  • Bernd J. Krämer
  • Yingcai Bai
  • Hansheng Chen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3262)

Abstract

As networks grow in size, heterogeneity, and complexity of applications and network services, an efficient network management system needs to work effectively even in face of incomplete management information, uncertain situations and dynamic changes. We use Bayesian networks to model the network management and consider the probabilistic backward inference between the managed entities, which can track the strongest causes and trace the strongest routes between particular effects and its causes. This is the foundation for further intelligent decision of management in networks.

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References

  1. 1.
    Wang, C., Schwartz, M.: Fault detection with multiple observers. IEEE/ACM transactions on Networking 1, 48–55 (1993)CrossRefGoogle Scholar
  2. 2.
    Frontini, M., Griffin, J., Towers, S.: A knowledge-based system for fault localization in wide area networks. In: Integrated Network Management, II, pp. 519–530. North-Holland, Amsterdam (1990)Google Scholar
  3. 3.
    Yemini, S.A., Kliger, S., Mozes, E., Yemini, Y., Ohsie, D.: High speed and robust event correlation. IEEE Communications Magazine 34(5), 82–90 (1996)CrossRefGoogle Scholar
  4. 4.
    Lewis, L.: A case-based reasoning approach to the resolution of faults in communication networks. In: Integrated Network Management, III, pp. 671–682. Elsevier Science Publishers B.V, Amsterdam (1993)Google Scholar
  5. 5.
    Deng, R.H., Lazar, A.A., Wang, W.: A probabilistic Approach to Fault Diagnosis in Linear Lightwave Networks. IEEE Journal on Selected Areas in Communications 11(9), 1438–1448 (1993)CrossRefGoogle Scholar
  6. 6.
    Steinder, M., Sethi, A.S.: Non-deterministic diagnosis of end-to-end service failures in a multi-layer communication system. In: Proc. of ICCCN, Scottsdale, AR, pp. 374-379 (2001)Google Scholar
  7. 7.
    The International Engineering Consortium. Highly available embedded computer platforms become reality , http://www.iec.org/online/tutorials/acrobat/haembed.pdf
  8. 8.
    Pearl, J.: Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann, San Mateo (1988)Google Scholar
  9. 9.
    Cowell, R.G., Dawid, A.P., Lauritzen, S.L., Spiegelhalter, D.J.: Probabilistic Networks and Expert Systems. Springer, Heidelberg (1999)zbMATHGoogle Scholar
  10. 10.
    Nikovski, D.: Constructing Bayesian networks for medical diagnosis from incomplete and partially correct statistics. IEEE Transactions on Knowledge and Data Engineering 12(4), 509–516 (2000)CrossRefGoogle Scholar
  11. 11.
    Basye, K., Dean, T., Vitter, J.S.: Coping with Uncertainty in Map Learning. Machine Learning 29(1), 65–88 (1997)zbMATHCrossRefGoogle Scholar
  12. 12.
    Charniak, E., Goldman, R.P.: A Semantics for Probabilistic Quantifier-Free First- Order Languages, with Particular Application to Story Understanding. In: Proceedings of IJCAI 1989, pp. 1074–1079. Morgan-Kaufmann, San Francisco (1989)Google Scholar
  13. 13.
    Katzela, I., Schwarz, M.: Schemes for fault identification in communication networks. IEEE Transactions on Networking 3(6), 733–764 (1995)CrossRefGoogle Scholar
  14. 14.
    Klinger, S., Yemini, S., Yemini, Y., Ohsie, D., Stolfo, S.: A coding approach to event correlation. In:Proceedings of the fourth international symposium on Integrated network management IV, pp.266-277 (January 1995)Google Scholar
  15. 15.
    Heckerman, D., Wellman, M.P.: Bayesian networks. Communications of the ACM 38(3), 27–30 (1995)CrossRefGoogle Scholar
  16. 16.
    Keller, U., Blumenthal, G.: Kar. Classification and Computation of Dependencies for Distributed Management. In: Pro. of 5th IEEE Symposium on Computers and Communications. Antibes-Juan-les-Pins, France (July 2000)Google Scholar
  17. 17.
    Gupta, M., Neogi, A., Agarwal, M.K., Kar, G.: Discovering Dynamic Dependencies in Enterprise Environments for Problem Determination. In: Brunner, M., Keller, A. (eds.) DSOM 2003. LNCS, vol. 2867, pp. 221–233. Springer, Heidelberg (2003) ISBN 3-540-20314-1CrossRefGoogle Scholar
  18. 18.
    Pearl, J.: A constraint-propagation approach to probabilistic reasoning. Uncertainty in Artificial Intelligence. North-Holland, Amsterdam, pp.357-369 (1986)Google Scholar
  19. 19.
    Neal, R.M.: Probabilistic inference using Markov chain Monte Carlo methods. Tech. Rep. CRG-TR93-1, University of Toronto (1993)Google Scholar
  20. 20.
    Cooper, G.: Computational complexity of probabilistic inference using Bayesian belief networks. Artificial Intelligence 42, 393–405 (1990)zbMATHCrossRefMathSciNetGoogle Scholar
  21. 21.
    Suermondt, H.J., Cooper, G.F.: Probabilistic inference in multiply connected belief network using loop cutsets. International Journal of Approximate Reasoning 4, 283–306 (1990)zbMATHCrossRefMathSciNetGoogle Scholar
  22. 22.
    Wang, C.: Bayesian Belief Network Simulation. Tech-Reprort, Department of Computer Science, Florida State University (2003)Google Scholar
  23. 23.
    Weigend, S., Gershenfeld, N.A.: Time Series Prediction. Addison-Wesley, Reading (1994)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Jianguo Ding
    • 1
    • 2
  • Bernd J. Krämer
    • 2
  • Yingcai Bai
    • 1
  • Hansheng Chen
    • 3
  1. 1.Department of Computer Science and EngineeringShanghai Jiao Tong UniversityShanghaiP. R. China
  2. 2.Department of Electrical Engineering and Information EngineeringFernUniversität HagenHagenGermany
  3. 3.East-china Institute of Computer TechnologyShanghaiP. R. China

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