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Probabilistic Fault Diagnosis in the MAGNETO Autonomic Control Loop

  • Pablo Arozarena
  • Raquel Toribio
  • Jesse Kielthy
  • Kevin Quinn
  • Martin Zach
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6155)

Abstract

Management of outer edge domains is a big challenge for service providers due to the diversity, heterogeneity and large amount of such networks, together with limited visibility on their status. This paper focuses on the probabilistic fault diagnosis functionality developed in the MAGNETO project, which enables finding the most probable cause of service problems and thus triggering appropriate repair actions. Moreover, its self-learning capabilities allow continuously enhancing the accuracy of the diagnostic process.

Keywords

Autonomic Home Area Networks (HAN) Probabilistic Management Bayesian Network Self-learning 

References

  1. 1.
    Kephart, J.O., Chess, D.M.: The Vision of Autonomic Computing. Computer 36(1), 41–50 (2003)CrossRefMathSciNetGoogle Scholar
  2. 2.
    Strassner, J., Agoulmine, N., Lehtihet, E.: Focale: A novel autonomic networking architecture. In: Latin American Autonomic Computing Symposium (LAACS), Campo Grande, MS, Brazil (2006)Google Scholar
  3. 3.
    Chaparadza, R., et al.: Creating a viable Evolution Path towards Self-Managing Future Internet via a Standardizable Reference Model for Autonomic Network Engineering. In: Towards the Future Internet - A European Research Perspective, pp. 136–147. IOS Press, Amsterdam (2009)Google Scholar
  4. 4.
    Lee, G.: Capri: A common architecture for distributed probabilistic internet fault diagnosis. Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science. Thesis, Ph. D. (2007)Google Scholar
  5. 5.
    Barco Moreno, R.: Bayesian modelling of fault diagnosis in mobile communication networks. Universidad de Málaga, Tech. Rep. (2007)Google Scholar
  6. 6.
    Hasslinger, G., et al.: 4WARD Deliverable D-4.2: In-Network Management Concept (2009), http://www.4ward-project.eu
  7. 7.
    MAGNETO: Management of the outer edge, http://projects.celtic-initiative.org/MAGNETO
  8. 8.
    Wooldridge, M.: An Introduction to Multi Agent Systems, 2nd edn. John Wiley & Sons, Chichester (2009)Google Scholar
  9. 9.
    Hastie, T., Tibshirani, R., Friedman, J.: The Elements of Statistical Learning, pp. 236–243. Springer, Heidelberg (2001)CrossRefzbMATHGoogle Scholar

Copyright information

© IFIP International Federation for Information Processing 2010

Authors and Affiliations

  • Pablo Arozarena
    • 1
  • Raquel Toribio
    • 1
  • Jesse Kielthy
    • 2
  • Kevin Quinn
    • 2
  • Martin Zach
    • 3
  1. 1.Telefónica Investigación y DesarrolloMadridSpain
  2. 2.Telecommunications Software and Systems Group (TSSG)WaterfordIreland
  3. 3.Siemens AG AustriaVienaAustria

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