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Machine learning models for alarm classification and failure localization in optical transport networks

The increase in computing capacity and the huge amount of available data have significantly accelerated the use of artificial intelligence and machine learning algorithms to solve complex real-life problems. In this context, optical networks can benefit from the power of machine learning algorithms to find innovative solutions to complex problems, in particular, by improving failure management. Failure management is an essential functionality of a network management system (NMS) to avoid degradation of signal quality or even the interruption of service. Existing failure management solutions are based on simple static rules, not suitable for modern optical networks characterized by a complex design and a large number of management parameters. In this paper, we use machine learning algorithms with data extracted from an NMS connected to an experimental setup to perform a rapid diagnosis and locate failures. We compare several machine learning models to identify, in the first step, correlated alarms that are related to the same type of failure—unexpected power attenuation—and to localize, in the second step, the position of this failure in the network. Using the four datasets generated by the experimental setup, results show that for both alarm classification and failure localization steps, the best performance is obtained by the light gradient boosting machine classifier with an F1-score greater than 90% and average precision and area under the curve scores close to 1.