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Artificial intelligence (AI) and machine learning (ML) continue to demonstrate substantial capabilities in solving a wide range of optical-network-related tasks such as fault management, resource allocation, and lightpath quality of transmission (QoT) estimation. However, the focus of the research community has been centered on ML models' predictive capabilities, neglecting aspects related to models' understanding, i.e., to interpret how the model reasons and makes its predictions. This lack of transparency hinders the understanding of a model's behavior and prevents operators from judging, and hence trusting, the model's decisions. To mitigate the lack of transparency and trust in ML, explainable AI (XAI) frameworks can be leveraged to explain how a model correlates input features to its outputs. In this paper, we focus on the application of XAI to lightpath QoT estimation. In particular, we exploit Shapley additive explanations (SHAP) as the XAI framework. Before presenting our analysis, we provide a brief overview of XAI and SHAP, then discuss the benefits of the application of XAI in networking and survey studies that apply XAI to networking tasks. Then, we model the lightpath QoT estimation problem as a supervised binary classification task to predict whether the value of the bit error rate associated with a lightpath is below or above a reference acceptability threshold and train an ML extreme gradient boosting model as the classifier. Finally, we demonstrate how to apply SHAP to extract insights about the model and to inspect misclassifications. [ABSTRACT FROM AUTHOR]

Increased global traffic puts tough requirements not just on fiber communications links but on the entire network. This manifests itself in multiple ways, including how to optimize wavelength routing around the network, how to maximize the benefits arising from fine-control DSP with increasingly accurate real-time monitoring, and how to best deploy multiband or multiple fiber connectivity. This article will summarize research into all these areas to present a full picture of how future optical networks will play their role in supporting the continuing traffic demands of broadband, 5G, and associated applications. [ABSTRACT FROM AUTHOR]

This Special Section (SI) features the latest research contributions regarding recent advances in the design and management of reliable communication networks. Communication networks are constantly increasing their complexity and scale to satisfy the requirements of network services. The current trend of convergence of networking and computing infrastructures (as in today’s cloud systems and softwarized networks) calls for novel advanced strategies and solutions to support reliable services, as the development of new data-driven solutions for reliable network automation and self-diagnostic tools to ensure resilient network management. [ABSTRACT FROM AUTHOR]

In presence of multiple failures affecting their network infrastructure, operators are faced with the Progressive Network Recovery (PNR) problem, i.e., deciding the best sequence of repairs during recovery. With incoming deployments of 5G networks, PNR must evolve to incorporate new recovery opportunities offered by network slicing. In this study, we introduce the new problem of Progressive Slice Recovery (PSR), which is addressed with eight different strategies, i.e., allowing or not to change slice embedding during the recovery, and/or by enforcing different versions of slice connectivity (i.e., network vs. content connectivity). We propose a comprehensive PSR scheme, which can be applied to all recovery strategies and achieves fast recovery of slices. We first prove the PSR’s NP-hardness and design an integer linear programming (ILP) model, which can obtain the best recovery sequence and is extensible for all the recovery strategies. Then, to address scalability issues of the ILP model, we devise an efficient two-phases progressive slice recovery (2-phase PSR) meta-heuristic algorithm, small optimality gap, consisting of two main steps: i) determination of recovery sequence, achieved through a linear-programming relaxation that works in polynomial time; and ii) slice-embedding recovery, for which we design an auxiliary-graph-based column generation to re-embed failed slice nodes/links to working substrate elements within a given number of actions. Numerical results compare the different strategies and validate that amount of recovered slices can be improved up to 50% if operators decide to reconfigure only few slice nodes and guarantee content connectivity. [ABSTRACT FROM AUTHOR]

Filterless Optical Networks (FONs) (i.e., optical networks where switching nodes are solely based on passive splitters and combiners) enjoy features that are highly appreciated by network operators, such as their low cost and their energy efficiency, posing them as an alternative solution to filtered Wavelength-Switched Optical Networks (WSON) based on active switching nodes. Due to FONs’ specific design criteria (the network topology must be divided into link-disjoint filterless fiber trees to avoid laser loops), traditional network problems, such as survivable virtual network mapping, shall be revisited and tackled adopting novel solutions with respect to state-of-the-art filtered WSONs. In this paper, we investigate the problem of survivable virtual network mapping (SVNM) in FONs with the aim of evaluating the cost of survivability when adopting FON technology. We first model the problem as an Integer Linear Program to establish fiber trees and provide survivable mapping of virtual networks, while minimizing cost of additional network equipment and spectrum with respect to WSON. We then propose multiple heuristic and meta-heuristic approaches to tackle large problem instances. In our numerical evaluations, we consider three scenarios: FON, WSON, and FON with pre-established fiber trees. Results show that in FON, where SVNM is jointly optimized with fiber tree establishment, the investment in additional network equipment can be largely minimized, and even avoided in some cases. In contrast, in FON with pre-established trees, amount of additional network equipment needed to guarantee survivability is significant (up to 60% with respect to WSON). [ABSTRACT FROM AUTHOR]