A Probabilistic Approach for Failure Localization

This work considers the problem of fault localization
in transparent optical networks. The aim is to localize singlelink
failures by utilizing statistical machine learning techniques
trained on data that describe the network state upon current
and past failure incidents. In particular, a Gaussian Process
(GP) classifier is trained on historical data extracted from the
examined network, with the goal of modeling and predicting
the failure probability of each link therein. To limit the set of
suspect links for every failure incident, the proposed approach is
complemented with the utilization of a Graph-Based Correlation
heuristic. The proposed approach is tested on a dataset generated
for an OFDM-based optical network, demonstrating that it
achieves a high localization accuracy. The proposed scheme can
be used by service providers for reducing the Mean-Time-To-
Repair of the failure.