Abstract
Innovations in optical networks created new technological challenges as
routing and spectrum allocation (RSA) problem, fragmented spectrum, the
need for rapid and efficient channel restoration, and put pressure on
the operation and maintenance. With a lot of lots of variables or knobs
to adjust, it is crucial to improve the automation as traditional
algorithms are not able to handle the network efficiently. So, this
requires sensors, network abstraction, actuators and SDN (Software
Defined Networking) in order to run algorithms on top, control, manage
the network and make decisions. In addition to this, there are the
requirements for low-margin systems and probabilistic shaping. So,
machine learning (ML) provides a collection of techniques to adapt to
this dynamic and flexible environment and provide a network that learn
from experience, optimize and make the networks agile, robust, dynamic,
and smarter. At the same time, network automation together with machine
learning may cause an explosion in power consumption, making this
solution costly, inefficient and not sustainable.