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. 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. Note to Practitioners-This paper was motivated by the problem of self-diagnosis in communication networks. The root cause identification process used currently consists of testing all of the network metrics without any prior knowledge of the dependency model. This process ignores the causal relationships which may exist between the network components. This paper suggests an approach based on prior modeling dependencies between metrics that compose a network (i.e., packet loss or jitter). The dependency model is achieved through a graph theory technique known as "Bayesian Network." Upon the detection of a failure, the approach considers only the most relevant metrics for the detected fault. The diagnosis consists of inferring the root cause using an algorithm based on the combination of Bayesian networks and case-based reasoning techniques. For the evaluation, observations collected from concrete network monitoring were used. This evaluation shows the efficiency of the proposed approach in terms of automation, speed, accuracy and reliability.