Today's telecommunication networks have become sources of enormous amounts of widely
heterogeneous data. This information can be retrieved from network traffic traces, network …

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) …

We review margins used in optical networks and review a formerly proposed margin
taxonomy. For each category of margins, we review techniques that the network designer …

Predicting the quality of transmission (QoT) of a lightpath prior to its deployment is a step of
capital importance for an optimized design of optical networks. Due to the continuous …

Optical connections support virtual links in MPLS-over-optical multilayer networks and
therefore, errors in the optical layer impact on the quality of the services deployed on such …

QoT Estimation for Unestablished Lighpaths using Machine Learning Page 1 Th1J.1.pdf
OFC 2017 © OSA 2017 QoT Estimation for Unestablished Lighpaths using Machine …

Estimating the quality of transmission (QoT) of a candidate lightpath prior to its establishment
is of pivotal importance for effective decision making in resource allocation for optical …

In optical transport networks the quality of transmission (QoT) is estimated before
provisioning new connections or upgrading existing ones. Traditionally, a physical layer …

The interconnection of all things is developing a new diagram of future information networks.
However, it is difficult to realize future applications with only one single technique …

Machine Learning (ML) is under intense investigation in optical networks as it promises to
lead to automation of a variety of management tasks, as amplifier gain equalization, fault …