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1 ADOPNET - Advanced technologies for operated networks IRISA-D2 - RÉSEAUX, TÉLÉCOMMUNICATION ET SERVICES, Télécom Bretagne, UR1 - Université de Rennes 1 2 INFO - Département informatique 3 IRISA - Institut de Recherche en Informatique et Systèmes Aléatoires 4 Orange Labs Lannion

Abstract : The dynamic and distributed nature of telecommunication networks makes complex the design of model-based approaches for network fault diagnosis. Most model-based approaches assume the prior existence of the model which is reduced to a static image of the network. Such models become rapidly obsolete when the network changes. We propose in this paper a 3-layered self-reconfigurable generic model of fault diagnosis in telecommunication networks. The Layer 1 of the model is an undirected graph which models the network topology. Network behavior, also called fault propagation, is modeled in Layer 2 using a set of directed acyclic graphs interconnected via the Layer 1. We handle uncertainties of fault propagation by quantifying strengths of dependencies between Layer 2 nodes with conditional probability distributions estimated from network generated data. Layer 3 is the junction tree representation of the loopy obtained Layer 2 Bayesian networks. The junction tree is the diagnosis computational layer since exact inference algorithms fail on loopy bayesian networks. This generic model embeds intelligent self-reconfiguration capabilities in order to track some changes in network topology and network behavior. These self-reconfiguration capabilities are highlighted through some example scenarios that we describe. We apply this 3-layered generic model to carry out active self-diagnosis of the GPON-FTTH access network.We present and analyze some experimental diagnosis results carried out by running a Python implementation of the generic model.

Keywords : FTTH Self-diagnosis Self-reconfiguration Fault propagation Bayesian networks Probabilistic inference GPON

Autor: Serge Romaric Tembo Mouafo - Jean-Luc Courant - Sandrine Vaton -

Fuente: https://hal.archives-ouvertes.fr/


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