Serge Romaric Tembo

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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(More)
The distributed iterative solution of numerical simulation problems on Infiniband or Ethernet Clusters via the P2PDC environment is studied. The P2PDC decentralized environment is dedicated to task parallel applications. It has been designed for the solution of large scale numerical simulation problems via distributed iterative algorithms. The P2PDC(More)
Model-based approaches for self-diagnosis of telecommunication networks develop reasonings based on formal and explicit representation of network structure and network behavior. Network behavior modeling is a central issue for these methods. In a recent work, we have proposed a model of architecture and fault propagation of the FTTH (Fiber To The Home)(More)
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(More)
In this paper we propose a distributed algorithm to solve a discrete trajectory optimization problem that occurs in a micro-electro-mechanical based modular surface context. The method computes the shortest path between two points of the modular surface using a strategy based on minimum hop count. Our scalable approach is based on distributed asynchronous(More)
Network behavior modelling is a central issue for model-based approaches of self-diagnosis of telecommunication networks. There are two methods to build such models. The model can be built from expert knowledge acquired from network standards and/or the model can be learnt from data generated by network components by data mining algorithms. In a recent(More)
This paper presents insights on the promises of probabilistic modeling and machine learning for fault diagnosis in optical access networks. A Bayesian inference engine, called Probabilistic tool for GPON-FTTH Access Network self-DiAgnosis (PANDA), is applied to fault diagnosis of Gigabit capable Passive Optical Networks (GPON). PANDA approach has been(More)
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