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.

Machine Learning Assisted Optical Network Resource Scheduling in Data Center Networks Parallel computing allows us to process incredible amounts of data in a timely manner by distributing the workload across multiple nodes and executing computation simultaneously. However, the performance of this parallelism usually suffers from network bottleneck. In optical switching enabled data center networks (DCNs), to satisfy the complex and timevarying bandwidth demands from the parallel computing, it is critical to fully exploit the flexibility of optical networks and meanwhile reasonably schedule the optical resources. Considering that the traffic flows generated by different applications in DCNs usually exhibit different statistical or correlative features, it is promising to schedule the optical resources with the assistance of machine learning. In this paper, we introduce a framework called intelligent optical resources scheduling system, and discuss how this framework can assist resource scheduling based on machine learning approaches. We also present our recent simulation results to verify the performance of the framework.