Self-Diagnosis technique for Virtual Private Networks combining Bayesian Networks and Case-Based Reasoning

L. Bennacer Yacine Amirat 1 A. Chibani 1 A. Mellouk 2 L. Ciavaglia 3
1 SIRIUS
LISSI - Laboratoire Images, Signaux et Systèmes Intelligents
2 CIR
LISSI - Laboratoire Images, Signaux et Systèmes Intelligents
3 Alcatel Lucent Bell Labs
Abstract : Fault diagnosis is a critical task for operators in the context of e-TOM (enhanced Telecom Operations Map) assurance process. Its purpose is to reduce network maintenance costs and to improve availability, reliability and performance of network services. Although necessary, this operation is complex and requires significant involvement of human expertise. The study of the fundamental properties of fault diagnosis shows that the diagnosis process complexity needs to be addressed using more intelligent and efficient approaches. In this paper, we present a hybrid approach that combines Bayesian networks and case-based reasoning in order to overcome the usual limits of fault diagnosis techniques and to reduce human intervention in this process. The proposed mechanism allows the identification of the root cause with a finer precision and a higher reliability. At the same time, it helps to reduce computation time while taking into account the network dynamicity. Furthermore, a study case is presented to show the feasibility and performance of the proposed approach based on a real-world use case: a virtual private network topology.
Keywords : Bayesian network case-based reasoning message passing inference root cause analysis self-diagnosis
Type de document :
Article dans une revue
IEEE Transactions on Automation Science and Engineering, Institute of Electrical and Electronics Engineers, 2015, 12 (1), pp.354-366
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https://hal.archives-ouvertes.fr/hal-01538572
Contributeur : Lab Lissi <>
Soumis le : mardi 13 juin 2017 - 16:55:36
Dernière modification le : mercredi 20 février 2019 - 16:07:19

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L. Bennacer, Yacine Amirat, A. Chibani, A. Mellouk, L. Ciavaglia. Self-Diagnosis technique for Virtual Private Networks combining Bayesian Networks and Case-Based Reasoning. IEEE Transactions on Automation Science and Engineering, Institute of Electrical and Electronics Engineers, 2015, 12 (1), pp.354-366. 〈hal-01538572〉

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