With the rapid development of Internet and communication systems, both in services and technologies, communication networks have been suffering increasing complexity. It is imperative to improve intelligence in communication network, and several aspects have been incorporating with Artificial Intelligence (AI) and Machine Learning (ML).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 networks monitoring and survivability. The use cases 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 optical networks are also discussed, which intends to inspire future innovations in leveraging ML to build intelligent optical networks.

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Network virtualization is meant to improve the efficiency of network infrastructure by sharing a physical substrate network among multiple virtual networks. Virtual network embedding (VNE) determines how to map a virtual network request onto a physical substrate. In this paper, we first overview three possible underlying substrates for interdatacenter networks, namely an electrical-layer-based substrate, an optical-layer-based substrate, and a multilayer-based (optical and electrical layer) substrate. Then, the corresponding VNE problems for the three physical substrates are discussed. The work presented focuses on VNE over a multilayer optical network; a key problem is how to map a virtual network request onto either an electrical or optical substrate. We propose an auxiliary graph model to address multilayer virtual network mapping in a dynamic traffic scenario. Different node-mapping and link-mapping policies can be achieved by adjusting weights of the edges of the auxiliary graph, which depends on the purposes of the network operators.

Increasingly, more people are suffering from the effects of air pollution. This study took Beijing as an example and proposed an attention-based air quality predictor (AAQP) that could better protect people from air pollution. The AAQP is a seq2seq model, and it exploits historical air quality data and weather data to predict future air quality indexes. Although existing research has promoted seq2seq for air quality prediction, there are still two problems. First, the seq2seq has a slow training speed so the original RNN in the encoder was replaced with a fully connected encoder to accelerate the training process. Position embedding was also introduced to help the fully connected encoder find the sequential relationships among source sequences. Another problem is error accumulation caused by recurrent prediction. Accordingly, the n-step recurrent prediction was proposed to solve this problem. The experimental results validated that the AAQP with n-step recurrent prediction had better performance than the related arts since the error accumulation was reduced, and the training time was significantly decreased compared with the original seq2seq attention model.

The wide coverage of satellite networks and the large bandwidth of terrestrial networks have led to an increasing research on the integration of the two networks (ISTN) for complementary advantages. However, most researches on routing mainly focus on the internal routing of the satellite network. Due to the point-to-area coverage of the channel characteristics between the satellites and the ground stations, the heterogeneity of ISTN has increased, which makes that the routing algorithm of the traditional satellite networks cannot be applied to the end-to-end routing of ISTN. Meanwhile, the data flows with elastic quality of service attribute make the routing pre-assign and resource allocation in ISTN much more complicated, which has been rarely researched before. In this paper, we first describe the unified network architecture based on software-defined network and model the data flow. Then, based on the considerations of latency, capacity, wavelength fragmentation, and load balancing, a heuristic service-oriented path computation algorithm for elastic data flows is proposed for the complex heterogeneity of the ISTN. The simulation shows that, the end-to-end routing mechanism can reduce the blocking rate of the ISTN, and our proposed algorithm greatly reduces the wavelength fragmentation and bandwidth consumption, and has a better performance on load balancing, with a slight disadvantage in latency when the network load is high. INDEX TERMS Low earth orbit satellites, quality of service, wavelength routing, optical fiber networks. I. INTRODUCTION