A 3-Layered Self-Reconfigurable Generic Model For Self-Diagnosis of Telecommunication Networks

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 GPONFTTH access network. We present and analyze some experimental diagnosis results carried out by running a Python implementation of the generic model.