To support bursty traffic on the Internet (and especially WWW) efficiently, optical burst switching (OBS) is proposed as a way to streamline both protocols and hardware in building the future gener...

Optical burst switching (OBS) - a new paradigm for an optical Internet

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 this paper proposing new possible research directions.

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An Overview on Application of Machine Learning Techniques in Optical Networks

Traditional power grids are currently being transformed into smart grids (SGs). SGs feature multi-way communication among energy generation, transmission, distribution, and usage facilities. The reliable, efficient, and intelligent management of complex power systems requires integration of high-speed, reliable, and secure data information and communication technology into the SGs to monitor and regulate power generation and usage. Despite several challenges, such as trade-offs between wireless coverage and capacity as well as limited spectral resources in SGs, wireless communication is a promising SG communications technology. Cognitive radio networks (CRNs) in particular are highly promising for providing timely SG wireless communications by utilizing all available spectrum resources. We provide in this paper a comprehensive survey on the CRN communication paradigm in SGs, including the system architecture, communication network compositions, applications, and CR-based communication technologies. We highlight potential applications of CR-based SG systems. We survey CR-based spectrum sensing approaches with their major classifications. We also provide a survey on CR-based routing and MAC protocols, and describe interference mitigation schemes. We furthermore present open issues and research challenges faced by CR-based SG networks along with future directions.

Cognitive Radio for Smart Grids: Survey of Architectures, Spectrum Sensing Mechanisms, and Networking Protocols

Based on the concept of infrastructure as a service, optical network virtualization can facilitate the sharing of physical infrastructure among different users and applications. In this paper, we design algorithms for both transparent and opaque virtual optical network embedding (VONE) over flexible-grid elastic optical networks. For transparent VONE, we first formulate an integer linear programming (ILP) model that leverages the all-or-nothing multi-commodity flow in graphs. Then, to consider the continuity and consecutiveness of substrate fiber links' (SFLs') optical spectra, we propose a layered-auxiliary-graph (LAG) approach that decomposes the physical infrastructure into several layered graphs according to the bandwidth requirement of a virtual optical network request. With LAG, we design two heuristic algorithms: one applies LAG to achieve integrated routing and spectrum assignment in link mapping (i.e., local resource capacity (LRC)-layered shortest-path routing LaSP), while the other realizes coordinated node and link mapping using LAG (i.e., layered local resource capacity(LaLRC)-LaSP). The simulation results from three different substrate topologies demonstrate that LaLRC-LaSP achieves better blocking performance than LRC-LaSP and an existing benchmark algorithm. For the opaque VONE, an ILP model is also formulated. We then design a LRC metric that considers the spectrum consecutiveness of SFLs. With this metric, a novel heuristic for opaque VONE, consecutiveness-aware LRC-K shortest-path-first fit (CaLRC-KSP-FF), is proposed. Simulation results show that compared with the existing algorithms, CaLRC-KSP-FF can reduce the request blocking probability significantly.

Virtual Optical Network Embedding (VONE) Over Elastic Optical Networks

https://www.cba.upc.edu/downloads/category/11-articles?download=355:[ARTICLE]A_survey_on_physical_layer_impairments_aware_routing_and_wavelength_assignment_algorithms_in_optical_networks.pdf

A survey on physical layer impairments aware routing and wavelength assignment algorithms in optical networks

We review margins used in optical networks and review a formerly proposed margin taxonomy. For each category of margins, we review techniques that the network designer can use in order to increase the capacity of optical networks, extend their life, and decrease deployment cost (CAPEX) or total cost of ownership over their life duration. Green field (new network deployments) and brown field techniques (used after initial network deployment) are discussed. The technology needed to leverage the margins and achieve the aforementioned gains are also reviewed, along with the associated challenges.

Design of Low-Margin Optical Networks

Core optical networks using reconfigurable optical switches and tunable lasers appear to be on the road towards widespread deployment and could evolve to all-optical mesh networks in the coming future. Considering the impact of physical layer impairments in the planning and operation of all-optical (and translucent) networks is the main focus of the Dynamic Impairment Constraint Optical Networking (DICONET) project. The impairment aware network planning and operation tool (NPOT) is the main outcome of DICONET project, which is explained in detail in this paper. The key building blocks of the NPOT, consisting of network description repositories, the physical layer performance evaluator, the impairment aware routing and wavelength assignment engines, the component placement modules, failure handling, and the integration of NPOT in the control plane are the main contributions of this study. Besides, the experimental result of DICONET proposal for centralized and distributed control plane integration schemes and the performance of the failure handling in terms of restoration time is presented in this study.

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Experimental Demonstration of an Impairment Aware Network Planning and Operation Tool for Transparent/Translucent Optical Networks

In this paper, we propose to lower the network design margins by improving the estimation of the signal-tonoise ratio (SNR) given by a quality of transmission (QoT) estimator, for new optical demands in a brownfield phase, based on a mathematical model of the physics of propagation During the greenfield phase and the network operation, we collect and correlate information on the QoT input parameters, issued from the established initial demands and available almost for free from the network elements: amplifiers output power and the SNR at the coherent receiver side Since we have some uncertainties on these input parameters of the QoT model, we use a machine learning algorithm to reduce them, improving the accuracy of the SNR estimation With this learning process and for a European backbone network (28 nodes, 41 links), we could reduce the QoT inaccuracy by several dBs for new demands whatever the amount of uncertainties of the initial parameters

Learning process for reducing uncertainties on network parameters and design margins

In this paper, we consider the problem of routing and wavelength assignment (RWA) in all-optical networks, where signals propagate in the optical domain (with no electrical regeneration) from end-to-end. In such networks, physical impairments are an issue and can cause calls to be blocked if a minimum quality of transmission (QoT) measured in terms of bit-error rate (BER) cannot be guaranteed for all calls, at all times. We focus on the routing component of RWA to incorporate QoT: we propose a novel RWA algorithm that finds a route based on both the network utilization and the physical impairments experienced over the tentative route. Our algorithm is fully decentralized and is shown using simulations to perform better than previously proposed algorithms on a regional-sized network.

QoT-Aware Routing in Impairment-Constrained Optical Networks

The estimation of the quality of transmission (QoT) in optical systems with machine learning (ML) has recently been the focus of a large body of research. We discuss the sources of inaccuracy in QoT estimation in general; we propose a taxonomy for ML-aided QoT estimation; we briefly review ML-aided optical performance monitoring, a tightly related topic; and we review and compare all recently published ML-aided QoT articles.

Yvan Pointurier

Papers

Design of Low-Margin Optical Networks

Experimental Demonstration of an Impairment Aware Network Planning and Operation Tool for Transparent/Translucent Optical Networks

Learning process for reducing uncertainties on network parameters and design margins

QoT-Aware Routing in Impairment-Constrained Optical Networks

Machine learning techniques for quality of transmission estimation in optical networks