Revisión de técnicas de machine learning en redes ópticas: análisis en pon y machine learning en redes ópticas.

Abstract

There is no doubt that, during the last decade, optical networks have become "smart networks", software defined networks (SDN), software-defined optical networks (SDON), elastic optical networks (EON), are some emerging technologies that provide a basis for promising innovations in the functioning and operation of optical networks. Machine learning algorithms are providing the possibility to develop this promising study area. Since machine learning can learn from a large amount of data available from the elements of the network. They can find a suitable solution for any type of environment and thus create more dynamic and flexible networks that improve the user experience. This investigation carries out a systematic mapping that provides an overview about machine learning in optical networks, identifies its opportunities, and suggests future research lines. The study analyzed 96 papers from the 841 publications obtained on this topic, with the objective of finding information about the use of machine learning techniques to solve problems related to the functioning and operation of optical networks. It is concluded that supervised machine learning techniques are those that are mostly used for resource management, network monitoring, fault management and classification and traffic prediction of an optical network. However, certain challenges need to be solved for a successful deployment of this type of methods in real communication systems, since most of the research has been carried out in controlled experimental environments.