THE ROLE OF GOVERNMENT EXTERNAL DEBT IN THE FORMATION OF THE FEDERAL BUDGET

The article considers the role of the public external debt of the budget of the Russian Federation. Also attention is paid to the influence of the level of public debt on the financial position of the country. Substantiated proposals to increase the external public debt management in the Russian Federation, resulting in proposed variant of increase of efficiency of public debt management Russia, optimization of the structure and minimize the consequences of its growth

THE ROLE OF GOVERNMENT EXTERNAL DEBT IN THE FORMATION OF THE FEDERAL BUDGET

The article considers the role of the public external debt of the budget of the Russian Federation. Also attention is paid to the influence of the level of public debt on the financial position of the country. Substantiated proposals to increase the external public debt management in the Russian Federation, resulting in proposed variant of increase of efficiency of public debt management Russia, optimization of the structure and minimize the consequences of its growth

Low-Margin Optical-Network Design with Multiple Physical-Layer Parameter Uncertainties

Analytical QoT models require safety margins to account for uncertain knowledge of input parameters. We propose and evaluate a design procedure that gradually decreases these margins in presence of multiple physical-layer uncertainties, by leveraging monitoring data to build a ML-based QoT regressor

If Not Here, There. Explaining Machine Learning Models for Fault Localization in Optical Networks

Machine Learning (ML) is being widely investigated to automate safety-critical tasks in optical-network management. However, in some cases, decisions taken by ML models are hard to interpret, motivate and trust, and this lack of explainability complicates ML adoption in network management. The rising field of Explainable Artificial Intelligence (XAI) tries to uncover the reasoning behind the decision-making of complex ML models, offering end-users a stronger sense of trust towards ML-Automated decisions. In this paper we showcase an application of XAI, focusing on fault localization, and analyze the reasoning of the ML model, trained on real Optical Signal-To-Noise Ratio measurements, in two scenarios. In the first scenario we use measurements from a single monitor at the receiver, while in the second we also use measurements from multiple monitors along the path. With XAI, we show that additional monitors allow network operators to better understand model's behavior, making ML model more trustable and, hence, more practically adoptable

Minimum-Cost Optical Amplifier Placement in Metro Networks

Emerging 5G services are revolutionizing the way operators manage and optimize their optical metro networks, and the metro network design process must be rethought accordingly. In particular, minimizing network cost is crucial to curb operators' investment. Taking advantage of relatively-short distances in metro networks, operators have the opportunity to optimize the placement of optical amplifiers (OAs) with the goal of minimizing amplifiers' cost (and hence decrease network cost) without significantly affecting the quality of transmitted optical signals. Minimizing OA cost translates not only in minimizing the cost of equipment (i.e., boosters, pre-amplifiers and inline amplifiers), but also in minimizing deployment and maintenance costs of active amplifier sites. In this article, we propose a heuristic algorithm for OA placement and for the Routing and Spectrum Assignment (RSA) in metro networks, with the objective of minimizing the total cost of OAs while guaranteeing sufficient optical signal-to-noise ratio (OSNR) of established lightpaths. In our approach, we consider different cost for the deployed OAs, according to their location and type, i.e., inline amplifiers (ILAs), boosters and pre-amplifiers, and compare our optimized placement against benchmark strategies where OAs are pre-deployed at network nodes and at a fixed distance one from the other along optical fiber links. We also evaluate the impact of different routing strategies on the total cost and utilized spectrum. Simulative results, performed over realistic metro network topologies, show that our strategy provides up to 47% OAs cost savings while satisfying minimum OSNR constraints

On Deep Reinforcement Learning for Static Routing and Wavelength Assigment

Deep Reinforcement Learning (DRL) is rising as a promising tool for solving optimization problems in optical networks. Though studies employing DRL for solving static optimization problems in optical networks are appearing, assessing strengths and weaknesses of DRL with respect to state-of-the-art solution methods is still an open research question. In this work, we focus on Routing and Wavelength Assignment (RWA), a well-studied problem for which fast and scalable algorithms leading to better optimality gaps are always sought for. We develop two different DRL-based methods to assess the impact of different design choices on DRL performance. In addition, we propose a Multi-Start approach that can improve the average DRL performance, and we engineer a shaped reward that allows efficient learning in networks with high link capacities. With Multi-Start, DRL gets competitive results with respect to a state-of-the-art Genetic Algorithm with significant savings in computational times. Moreover, we assess the generalization capabilities of DRL to traffic matrices unseen during training, in terms of total connection requests and traffic distribution, showing that DRL can generalize on small to moderate deviations with respect to the training traffic matrices. Finally, we assess DRL scalability with respect to topology size and link capacity

Quantifying Resource Savings from Low-Margin Design in Optical Networks with Probabilistic Constellation Shaping

We estimate resource savings from low-margin optical-network design considering (i) different transmission modes (including PCS), (ii) full vs. actual load for interference modelling and (iii) greedy heuristic vs. evolutionary metaheuristic for routing. Numerical results, mimicking multi-year-traffic evolution, allow to quantify extent of these savings

Tutorial on filterless optical networks [Invited]

The recent acceleration in fiber-to-the-home deployment worldwide along with the emerging 5G communications are pressuring network operators to enhance their networks to serve these new deployments. Hence, operators are seeking new high-capacity optical-network architectures, while averting excessive capital and operational expenditures. Filterless optical networks (FONs), by replacing costly wavelength selective switches in switching nodes with passive optical power splitters/combiners, currently represent a prominent candidate for cost-effective optical-network deployment. In this tutorial, we provide an overview of the architecture and the design issues of FONs when deployed in core and in metro networks. We also perform a techno-economic study to quantify the economic benefits of FONs, comparing their cost to that of state-of-the-art filtered optical networks, and we discuss how several networking problems such as resource allocation, network slicing, and protection are tackled in the context of FONs. Finally, we present our vision of how research on FONs will evolve in the coming years