Abstract

In this paper, we first review how the main machine learning concepts can apply to communication networks. Then we present results from a concrete application using unsupervised machine learning in a real network. We show how the application can detect anomalies at multiple network layers, including the optical layer, how it can be trained to anticipate anomalies before they become a problem, and how it can be trained to identify the root cause of each anomaly. Finally, we elaborate on the importance of this work and speculate about the future of intelligent adaptive networks.

© 2018 Optical Society of America

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