In order to accomplish cost-efficient management of complex optical communication networks, operators are seeking automation of network diagnosis and management by means of Machine Learning (ML). To support these objectives, new functions are needed to enable cognitive, autonomous management of optical network security. This paper focuses on the challenges related to the performance of ML-based approaches for detection and localization of optical-layer attacks, and to their integration with standard Network Management Systems (NMSs). We propose a framework for cognitive security diagnostics that comprises an attack detection module with Supervised Learning (SL), Semi-Supervised Learning (SSL) and Unsupervised Learning (UL) approaches, and an attack localization module that deduces the location of a harmful connection and/or a breached link. The influence of false positives and false negatives is addressed by a newly proposed Window-based Attack Detection (WAD) approach. We provide practical implementation guidelines for the integration of the framework into the NMS and evaluate its performance in an experimental network testbed subjected to attacks, resulting with the largest optical-layer security experimental dataset reported to date.

Network slicing enables an infrastructure provider (InP) to support heterogeneous 5G services over a common platform (i.e., by creating a customized slice for each service). Once in operation, slices can be dynamically scaled up/down to match the variation of their service requirements. An InP generates revenue by accepting a slice request. If a slice cannot be scaled up when required, an InP has to also pay a penalty (proportional to the level of service degradation). It becomes then crucial for an InP to decide which slice requests should be accepted/rejected in order to increase its net profit. This paper presents a slice admission strategy based on reinforcement learning (RL) in the presence of services with different priorities. The use case considered is a 5G flexible radio access network (RAN), where slices of different mobile service providers are virtualized over the same RAN infrastructure. The proposed policy learns which are the services with the potential to bring high profit (i.e., high revenue with low degradation penalty), and hence should be accepted. The performance of the RL-based admission policy is compared against two deterministic heuristics. Results show that in the considered scenario, the proposed strategy outperforms the benchmark heuristics by at least 55%. Moreover, this paper shows how the policy is able to adapt to different conditions in terms of: (i) slice degradation penalty vs. slice revenue factors, and (ii) proportion of high vs. low priority services.

Bringing the cellular connectivity in rural zones is a big challenge, due to the large installation costs that are incurred when a legacy cellular network based on fixed Base Stations (BSs) is deployed. To tackle this aspect, we consider an alternative architecture composed of UAV-based BSs to provide cellular coverage, ground sites to connect the UAVs with the rest of the network, Solar Panels (SPs) and batteries to recharge the UAVs and to power the ground sites, and a ring of optical fiber links to connect the installed sites. We then target the minimization of the installation costs for the considered UAV-based cellular architecture, by taking into account the constraints of UAVs coverage, SPs energy consumption, levels of the batteries and the deployment of the optical ring. After providing the problem formulation, we derive an innovative methodology to ensure that a single ring of installed optical fibers is deployed. Moreover, we propose a new algorithm, called DIARIZE, to practically tackle the problem. Our results, obtained over a set of representative rural scenarios, show that DIARIZE performs very close to the optimal solution, and in general outperforms a reference design based on fixed BSs.

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Reinforcement Learning (RL) is leading to important breakthroughs in several areas (e.g., selfdriving vehicles, robotics, and network automation). Part of its success is due to the existence of toolkits (e.g., OpenAI Gym) to implement standard RL tasks. On the one hand, they allow for the quick implementation and testing of new ideas. On the other, these toolkits ensure easy reproducibility via quick and fair benchmarking. RL is also gaining traction in the optical networks research community, showing promising results while solving several use cases. However, there are many scenarios where the benefits of RL-based solutions remain still unclear. A possible reason for this is the steep learning curve required to tailor RL-based frameworks to each specific use case. This, in turn, might delay or even prevent the development of new ideas. This paper introduces the Optical Network Reinforcement-Learning-Gym (Optical RL-Gym) 1 , an open-source toolkit that can be used to apply RL to problems related to optical networks. The Optical RL-Gym follows the principles established by the OpenAI Gym, the de-facto standard for RL environments. Optical RL-Gym allows for the quick integration with existing RL agents, as well as the possibility to build upon several already available environments to implement and solve more elaborated use cases related to the optical networks research area. The capabilities and the benefits of the proposed toolkit are illustrated by using the Optical RL-Gym to solve two different service provisioning problems.

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