Programmable Multilayer INT: An Enabler for AIAssisted Network Automation Recently, the fast development of backbone networks has made the traffic, services, and infrastructure of packetoveroptical networks increasingly complicated. This stimulates research and development on finegrained and realtime performance monitoring and troubleshooting. In this article, we propose a ProMLINT system to oversee packetoveroptical networks in real time and enable customized performance monitoring and troubleshooting. We introduce the system design in detail, and explain how to control the overhead of multilayer INT MLINT by inserting INT fields in packets selectively. Experiments demonstrate the ProMLINT system, a smallscale packetoveroptical network testbed. The experimental results confirm that our proposal can monitor packet and optical layers jointly in real time, and the homemade data analyzer in it can leverage artificial intelligence to identify the root causes of exceptions in packetoveroptical networks correctly and promptly.

Machine learning for intelligent optical networks: A comprehensive survey Abstract   With the rapid development of Internet and communication systems, both in the aspect of services and technologies, communication networks have been suffering increasing complexity. It is imperative to improve intelligence in communication networks, and several aspects have been incorporating with Artificial Intelligence (AI) and Machine Learning (ML). The optical network, which plays an important role both in core and access network in communication networks, also faces great challenges of system complexity and the requirement of manual operations. To overcome the current limitations and address the issues of future optical networks, it is essential to deploy more intelligence capability to enable autonomous and flexible network operations. ML techniques are proved to have superiority on solving complex problems, and thus recently, ML techniques have been used for many optical network applications.  In this paper, a detailed survey of existing applications of ML for intelligent optical networks is presented. The applications of ML are classified in terms of their use cases, which are categorized into optical network control and resource management, and optical network monitoring and survivability. These applications are analyzed and compared according to the used ML techniques. Besides, a tutorial for ML applications is provided from the aspects of the introduction of common ML algorithms, paradigms of ML, and motivations of applying ML. Lastly, challenges and possible solutions of ML application in intelligent optical networks are also discussed, which intends to inspire future innovations in leveraging ML to build intelligent optical networks.