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.
The technological development of smart devices and Internet of Things (IoT) has brought ever-larger bandwidth and fluctuating traffic to existing networks. The analysis of network capital expenditure (CAPEX) is extremely important and plays a fundamental role in further network optimizing. In this paper, an adaptability analysis is raised for IP switching and optical transport network (OTN) switching in CAPEX when the service bandwidth is fluctuating violently. This paper establishes a multi-layer network architecture through Clos network model and discusses impacts of maximum allowable blocking rate and service bandwidth standard deviation on CAPEX of IP network and OTN network to find CAPEX demarcation point in different situations. As simulation results show, when the bandwidth deviation mean rate is 0.3 and the maximum allowable blocking rate is 0.01, the hardware cost of OTN switching will exceed IP switching as the average bandwidth is greater than 6 100 Mbit/s. When the service bandwidth fluctuation is severe, the hardware cost of OTN switching will increase and exceed IP switching as the single port rate is allowed in optical switching. The increasing of maximum allowable blocking rate can decrease hardware cost of OTN switching. Finally, it is found that Flex Ethernet (FlexE) can be used to decrease CAPEX of OTN switching greatly at this time.
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.