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

Fault diagnosis is a critical task for operators in the context of e-TOM (enhanced Telecom Operations Map) assurance process, in order to reduce network maintenance costs and improve availability, reliability, and performance of network services. This operation are complex, requiring significant human involvement. In our work, we first examine the fundamental properties of fault diagnosis in VPN and argue that this complexity is due to a lack of intelligence in the diagnosis process. In this paper, we present a new hybrid approach that combines Bayesian Networks and Case-Based Reasoning to overcome the usual limits of fault diagnosis techniques and reduce human intervention in this process. The proposed mechanism allows identifying the root cause failure with a finer precision and high reliability while reducing the process computation time and taking into account the network dynamicity.