A Tutorial on Machine Learning for Failure Management in Optical Networks

doi:10.1109/JLT.2019.2922586

A Tutorial on Machine Learning for Failure Management in Optical Networks

2019-08-15

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DOI：10.1109/JLT.2019.2922586

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Failure management plays a role of capital importance in optical networks to avoid service disruptions and to satisfy customers' service level agreements. Machine learning (ML) promises to revolutionize the (mostly manual and human-driven) approaches in which failure management in optical networks has been traditionallymanaged, by introducing automated methods for failure prediction, detection, localization, and identification. This tutorial provides a gentle introduction to some ML techniques that have been recently applied in the field of the optical-network failure management. It then introduces a taxonomy to classify failure-management tasks and discusses possible applications of ML for these failure management tasks. Finally, for a reader interested in more implementative details, we provide a step-by-step description of how to solve a representative example of a practical failuremanagement task.

Keywords:

Failure management

machine learning

A Tutorial on Machine Learning for Failure Management in Optical Networks

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