Today's telecommunication networks have become sources of enormous amounts of widely heterogeneous data. This information can be retrieved from network traffic traces, network alarms, signal quality indicators, users' behavioral data, etc. Advanced mathematical tools are required to extract meaningful information from these data and take decisions pertaining to the proper functioning of the networks from the network-generated data. Among these mathematical tools, Machine Learning (ML) is regarded as one of the most promising methodological approaches to perform network-data analysis and enable automated network self-configuration and fault management.The adoption of ML techniques in the field of optical communication networks is motivated by the unprecedented growth of network complexity faced by optical networks in the last few years. Such complexity increase is due to the introduction of a huge number of adjustable and interdependent system parameters (e.g., routing configurations, modulation format, symbol rate, coding schemes, etc.) that are enabled by the usage of coherent transmission/reception technologies, advanced digital signal processing and compensation of nonlinear effects in optical fiber propagation.In this paper we provide an overview of the application of ML to optical communications and networking. We classify and survey relevant literature dealing with the topic, and we also provide an introductory tutorial on ML for researchers and practitioners interested in this field. Although a good number of research papers have recently appeared, the application of ML to optical networks is still in its infancy: to stimulate further work in this area, we conclude the paper proposing new possible research directions.

Predicting the Quality of Transmission (QoT) of a lightpath prior to its deployment is a step of capital importance for an optimized design of optical networks. Due to the continuous advances in optical transmission, the number of design parameters available to system engineers (say, e.g., modulation formats, baud rate, code rate, etc.) is growing dramatically, thus significantly increasing the alternative scenarios for lightpath deployment. As of today, existing (pre-deployment) estimation techniques for lightpath QoT belong to two categories: "exact" analytical models estimating physical layer impairments, which provide accurate results but incur heavy computational requirements, and margined formulas which are computationally faster, but typically introduce high link margins that lead to underutilization of network resources. In this paper we explore a third option, i.e., Machine Learning (ML), as ML techniques have been already successfully applied for optimization and performance prediction of complex systems where analytical models are hard to derive and/or numerical procedures impose high computational burden. We investigate a ML classifier that predicts whether the bit-error rate of unestablished lightpaths meets the required system threshold, based on traffic volume, desired route and modulation format. The classifier is trained and tested on synthetic data and its performance is assessed over different network topologies and for various combinations of classification features. Results in terms of classifier accuracy are promising and motivate further investigation over real field data.

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Abstract-The widespread deployment of Automatic Metering Infrastructures in Smart Grid scenarios rises great concerns about privacy preservation of user-related data, from which detailed information about customer's habits and behaviors can be deduced. Therefore, the users' individual measurements should be aggregated before being provided to External Entities such as utilities, grid managers and third parties.This paper proposes a security architecture for distributed aggregation of additive data, in particular energy consumption metering data, relying on Gateways placed at the customers' premises, which collect the data generated by local Meters and provide communication and cryptographic capabilities. The Gateways communicate with one another and with the External Entities by means of a public data network. We propose a secure communication protocol aimed at preventing Gateways and External Entities from inferring information about individual data, in which privacy-preserving aggregation is performed by means of a cryptographic homomorphic scheme. The routing of information flows can be centralized or it can be performed in a distributed fashion using a protocol inspired by Chord. We compare the performance of both approaches to the optimal solution minimizing the data aggregation delay.

Abstract:The Smart Meter (SM) is an essential tool for successful balancing the demand-offer energy curve. It allows the linking of the consumption and production measurements with the time information and the customer's identity, enabling the substitution of flat-price billing with smarter solutions, such as Time-of-Use or Real-Time Pricing. In addition to sending data to the energy operators for billing and monitoring purposes, Smart Meters must be able to send the same data to customer devices in near-real-time conditions, enabling new services such as instant energy awareness and home automation. In this article, we review the ongoing situation in Europe regarding real-time services for the final customers. Then, we review the architectural and technological options that have been considered for the roll-out phase of the Italian second generation of Smart Meters. Finally, we identify a collection of use cases, along with their functional and performance requirements, and discuss what architectures and communications technologies can meet these requirements.

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Failure management plays a role of capital importance in optical networks to avoid service disruptions and to satisfy customers' service level agreements. Machine Learning (ML) promises to revolutionize the (mostly manual and humandriven) approaches in which failure management in optical networks has been traditionally managed, by introducing automated methods for failure prediction, detection, localization and identification. This tutorial provides a gentle introduction to some ML techniques that have been recently applied in the field of optical-network failure management. It then introduces a taxonomy to classify failure-management tasks and discusses possible applications of ML for these failure management tasks. Finally, for a reader interested in more implementative details, we provide a step-by-step description of how to solve a representative example of a practical failure-management task.

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