Automation of Photonic Networks Using Machine Learning: Case Studies and Future Works

Although a “Self-Driving” photonic network is still a long way to go, many time-consuming complex tasks and decision making in photonic networks can be automated using machine learning, and other data-driven solutions. This study explores recent contributions towards photonic network automation, such as alarm prediction, fault localization, resource auto-scaling, quality of transmission prediction, dynamic controller placement, automated service restoration, resource allocation and optimization, minimization of electricity and power supply cost, user data analysis, etc. The studies explored in this letter, provide solution approaches to these problems using wide range of data-driven methods, machine learning, AI, and deep learning based methods. This study discusses different challenges involving these interesting research areas and provides directions for future research opportunities as well.