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 human-driven) 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.

Distributed Denial of Service (DDoS) attacks represent a major concern in modern Software Defined Networking (SDN), as SDN controllers are sensitive points of failures in the whole SDN architecture. Recently, research on DDoS attacks detection in SDN has focused on investigation of how to leverage data plane programmability, enabled by P4 language, to detect attacks directly in network switches, with marginal involvement of SDN controllers. In order to effectively address cybersecurity management in SDN architectures, we investigate the potential of Artificial Intelligence and Machine Learning (ML) algorithms to perform automated DDoS Attacks Detection (DAD), specifically focusing on Transmission Control Protocol SYN flood attacks. We compare two different DAD architectures, called Standalone and Correlated DAD, where traffic features collection and attack detection are performed locally at network switches or in a single entity (e.g., in SDN controller), respectively. We combine the capability of ML and P4-enabled data planes to implement real-time DAD. Illustrative numerical results show that, for all tested ML algorithms, accuracy, precision, recall and F1-score are above 98% in most cases, and classification time is in the order of few hundreds of $$\upmu \text {s}$$μ s in the worst case. Considering real-time DAD implementation, significant latency reduction is obtained when features are extracted at the data plane by using P4 language. Graphic

5G mobile access targets unprecedented performance, not only in terms of higher data rates per user and lower latency, but also in terms of network intelligence and capillarity. To achieve this, 5G networks will resort to solutions as small cell deployment, multipoint coordination (CoMP, ICIC) and centralized radio access network (C-RAN) with baseband units (BBUs) hotelling. As adopting such techniques requires a high-capacity low-latency access/aggregation network to support backhaul, radio coordination and fronthaul (i.e., digitized baseband signal) traffic, optical access/aggregation networks based on wavelength division multiplexing (WDM) are considered as an outstanding candidate for 5G-transport. By physically separating BBUs from the corresponding cell sites, BBU hotelling promises substantial savings in terms of cost and power consumption. However, this requires to insert additional high bit-rate traffic, i.e., the fronthaul, which also has very strict latency requirements. Therefore, a tradeoff between the number of BBU-hotels (BBU consolidation), the fronthaul latency and network-capacity utilization arises. We introduce the novel BBU-placement optimization problem for C-RAN deployment over a WDM aggregation network and formalize it by integer linear programming. Thus, we evaluate the impact of 1) jointly supporting converged fixed and mobile traffic, 2) different fronthaul-transport options (namely, OTN and Overlay) and 3) joint optimization of BBU and electronic switches placement, on the amount of BBU consolidation achievable on the aggregation network.

By predicting the traffic load on network links, a network operator can effectively pre-dispose resource-allocation strategies to early address, e.g., an incoming congestion event. Traffic loads on different links of a telecom is know to be subject to strong correlation, and this correlation, if properly represented, can be exploited to refine the prediction of future congestion events. Machine Learning (ML) represents nowadays the state-of-the-art methodology for discovering complex relations among data. However, ML has been traditionally applied to data represented in the Euclidean space (e.g., to images) and it may not be straightforward to effectively employ it to model graph-stuctured data (e.g., as the events that take place in telecom networks). Recently, several ML algorithms specifically designed to learn models of graph-structured data have appeared in the literature. The main novelty of these techniques relies on their ability to learn a representation of each node of the graph considering both its properties (e.g., features) and the structure of the network (e.g., the topology). In this paper, we employ a recently-proposed graph-based ML algorithm, the Diffusion Convolutional Recurrent Neural Network (DCRNN), to forecast traffic load on the links of a real backbone network. We evaluate DRCNN's ability to forecast the volume of expected traffic and to predict events of congestion, and we compare this approach to other existing approaches (as LSTM, and Fully-Connected Neural Networks). Results show that DCRN outperforms the other methods both in terms of its forecasting ability (e.g., MAPE is reduced from 210% to 43%) and in terms of the prediction of congestion events, and represent promising starting point for the application of DRCNN to other network management problems.

We develop and test several machine-learning methods to perform detection and identification of equipment failures in optical networks. Results, obtained over real BER traces, show above 98% accuracy in most cases with reasonable algorithm complexity.