As the fundamental infrastructure of the Internet, the optical network carries a great amount of Internet traffic. There would be great financial losses if some faults happen. Therefore, fault location is very important for the operation and maintenance in optical networks. Due to complex relationships among each network element in topology level, each board in network element level, and each component in board level, the concrete fault location is hard for traditional method. In recent years, machine learning, especially deep learning, has been applied to many complex problems, because machine learning can find potential non-linear mapping from some inputs to the output. In this paper, we introduce supervised machine learning to propose a complete process for fault location. Firstly, we use data preprocessing, data annotation, and data augmentation in order to process original collected data to build a high-quality dataset. Then, two machine learning algorithms (convolutional neural networks and deep neural networks) are applied on the dataset. The evaluation on commercial optical networks shows that this process helps improve the quality of dataset, and two algorithms perform well on fault location.

Due to the vulnerability of fibers in optical networks, physical-layer attacks targeting photon splitting, such as eavesdropping, can potentially lead to large information and revenue loss. To enhance the existing security approaches of optical networks, a new promising technology, quantum key distribution (QKD), can securely encrypt services in optical networks, which has been a hotspot of research in recent years for its characteristic that can let clients know whether information transmission has been eavesdropped or not. In this paper, we apply QKD to provide secret keys for optical networks and then introduce the architecture of QKD based optical network. As for the secret keys generated by QKD in optical networks, we propose a re-transmission mechanism by analyzing the security risks in QKD-based optical networks. Numerical results indicate that the proposed re-transmission mechanism can provide strong protection degree with enhanced attack protection. Finally, we illustrated some future challenges in QKD-based optical networks.

Software defined optical networks (SDONs) integrate software defined technology with optical communication networks and represent the promising development trend of future optical networks. The key technologies for SDONs include software-defined optical transmission, switching, and networking. The main features include control and transport separation, hardware universalization, protocol standardization, controllable optical network, and flexible optical network applications. This paper introduces software defined optical networks and its innovation environment, in terms of network architecture, protocol extension solution, experiment platform and typical applications. Batch testing has been conducted to evaluate the performance of this SDON testbed. The results show that the SDON testbed has good scalability in different sizes. Meanwhile, we notice that controller output bandwidth has great influence on lightpath setup delay.