Artificial intelligence (AI) is an extensive scientific discipline which enables computer systems to solve problems by emulating complex biological processes such as learning, reasoning and self-correction. This paper presents a comprehensive review of the application of AI techniques for improving performance of optical communication systems and networks. The use of AI-based techniques is first studied in applications related to optical transmission, ranging from the characterization and operation of network components to performance monitoring, mitigation of nonlinearities, and quality of transmission estimation. Then, applications related to optical network control and management are also reviewed, including topics like optical network planning and operation in both transport and access networks. Finally, the paper also presents a summary of opportunities and challenges in optical networking where AI is expected to play a key role in the near future.

5G technologies promise to bring new network and service capacities and are expected to introduce significant architectural and service deployment transformations. The Cloud-Radio Access Networks (C-RAN) architecture, enabled by the combination of Software Defined Networking (SDN), Network Function Virtualization (NFV) and Mobile Edge Computing (MEC) technologies, play a key role in the development of 5G. In this context, this paper addresses the problems of Virtual Network Functions (VNF) provisioning (VNF-placement and service chain allocation) in a 5G network. In order to solve that problem, we propose a genetic algorithm that, considering both computing resources and optical network capacity, minimizes both the service blocking rate and CPU usage. In addition, we present an algorithm extension that adds a learning stage and evaluate the algorithm performance benefits in those scenarios where VNF allocations can be reconfigured. Results reveal and quantify the advantages of reconfiguring the VNF mapping depending on the current demands. Our methods outperform previous proposals in the literature, reducing the service blocking ratio while saving energy by reducing the number of active core CPUs.

We propose a cognitive Quality of Transmission (QoT) estimator for classifying lightpaths into high or low quality categories in impairment-aware wavelength-routed optical networks. The technique is based on Case-Based Reasoning (CBR), an artificial intelligence technique which solves new problems by exploiting previous experiences, which are stored on a knowledge base. We also show that by including learning and forgetting techniques, the underlying knowledge base can be optimized, thus leading to a significant reduction on the computing time for on-line operation. The performance of the cognitive estimator is evaluated in a long haul and in an ultra-long haul network, and we demonstrate that it achieves more than 98% successful classifications, and that it is up to four orders of magnitude faster when compared with a non-cognitive QoT estimator, the Q-Tool.Index Terms-Case-based reasoning (CBR), cognitive networks, impairment-aware networking, quality of transmission (QoT), wavelength-routed optical network (WRON).

5G technology will provide networks with high-bandwidth, low latency and multitenancy. The integration of computing and storage resources in the edge of the fronthaul network, i.e., multi-access edge computing (MEC), will allow to instantiate some virtual network functions (VNF) in those computing resources. The backhaul of 5G networks will be based on optical technology, in particular WDM, due to its high capacity and flexibility. In this paper, we analyse the problem of VNF-provisioning in a metro ring-topology network equipped with MEC resources and with a WDM network connecting the edge nodes. In contrast to previous proposals, the method decides where VNFs must be instantiated but also the design of the virtual topology for the WDM metro network in order to reduce the service blocking ratio and the number of resources in operation.

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