Titre

Model-based probabilistic reasoning for self-diagnosis of telecommunication networks: application to a GPON-FTTH access network

Auteur(s)

TEMBO MOUAFO Serge Romaric1,2,3, VATON Sandrine1,2, COURANT Jean-Luc3, GOSSELIN Stephane3, BEUVELOT Michel3

Type de document

Article de revue avec comité de lecture

Source

Journal of network and systems management, july 2017, vol. 25, n° 3, pp. 558-590

Année

2017

Résumé

Carrying out self-diagnosis of telecommunication networks requires an understanding of the phenomenon of fault propagation on these networks. This understanding makes it possible to acquire relevant knowledge in order to automatically solve the problem of reverse fault propagation. Two main types of methods can be used to understand fault propagation in order to guess or approximate as much as possible the root causes of observed alarms. Expert systems formulate laws or rules that best describe the phenomenon. Artificial intelligence methods consider that a phenomenon is understood if it can be reproduced by modeling. We propose in this paper, a generic probabilistic modeling method which facilitates fault propagation modeling on large-scale telecommunication networks. A Bayesian network (BN) model of fault propagation on gigabit-capable passive optical network-fiber to the home (GPON-FTTH) access network is designed according to the generic model. GPON-FTTH network skills are used to build structure and approximatively determine parameters of the BN model so-called expert BN model of the GPON-FTTH network. This BN model is confronted with reality by carrying out self-diagnosis of real malfunctions encountered on a commercial GPON-FTTH network. Obtained self-diagnosis results are very satisfying and we show how and why these results of the probabilistic model are more consistent with the behaviour of the GPON-FTTH network, and more reasonable on a representative sample of diagnosis cases, than a rule-based expert system. With the main goal to improve diagnostic performances of the BN model, we study and apply expectation maximization algorithm in order to automatically fine-tune parameters of the BN model from real data generated by a commercial GPON-FTTH network. We show that the new BN model with optimized parameters reasonably improves self-diagnosis previously carried out by the expert Bayesian network model of the GPON-FTTH access network.

Labos

1 : INFO(TB) - Dépt. Informatique (Institut Mines-Télécom-Télécom Bretagne-UEB)
2 : IRISA(TB) - Institut de recherche en informatique et systèmes aléatoires (UMR CNRS 6074 - Université de Rennes 1 - INRIA - INSA de Rennes - ENS de Cachan - Télécom Bretagne - Université de Bretagne Sud)
3 : Orange Labs - Lannion (France Télécom)

Mots clés

Network management, Optical network, Fault management, Fault propagation, Model-based approach, Bayesian network, Statistical inference, Parameter estimation, Expectation Maximization

Référence

17444

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  • Institut Carnot Télécom & Société numérique
  • Université Bretagne Loire
  • Institut Mines-Télécom