On the suitability, requisites, and challenges of machine learning Invited

The introduction of 5G, the increasing number of connected devices, and the exponential growth of services relying on connectivity are pressuring multilayer networks to improve their scaling, efficiency, and controlling capabilities. However, enhancing those features consistently results in a significant amount of complexity in operating the resources available across heterogeneous vendors and technology domains. Thus, multilayer networks should become more intelligent in order to be efficiently managed, maintained, and optimized. In this context, we are witnessing an increasing interest in the adoption of artificial intelligence (AI) and machine learning (ML) in the design and operation of multilayer optical transport networks. This paper provides a brief introduction to key concepts in AI/ML, highlighting the conditions under which the use of ML is justified, on the requisites to deploy a data-driven system, and on the challenges faced when moving toward a production environment. As far as possible, some key concepts are illustrated using two realistic use-cases applied to multilayer optical networks: cognitive service provisioning and quality of transmission estimation.