Experimental Validation of Time-Synchronized Operations for Software-Defined Elastic Optical Networks

Elastic optical networks (EON) have been proposed as a solution to efficiently exploit the spectrum resources in the physical layer of optical networks. Moreover, by centralizing legacy generalized multiprotocol label switching control-plane functionalities and providing a global network view, software-defined networking (SDN) enables advanced network programmability valuable to control and configure the technological breakthroughs of EON. In this paper, we review our recent proposal [Optical Fiber Communication Conf., Los Angeles, California, 2017] of time-synchronized operations (TSO) to minimize disruption time during lightpath reassignment in EON. TSO has been recently standardized in SDN, and here we discuss its implementation using NETCONF and OpenFlow in optical networks. Subsequently, we update our analytical model considering an experimental characterization of the WSS operation time. Then, we extend our previous work with an experimental validation of TSO for lightpath reassignment in a five-node metropolitan optical network test-bed. Results validate the convenience of our TSO-based approach against a traditional asynchronous technique given its reduction of disruption time, while both techniques maintain a similar network performance in terms of optical signal-to-noise ratio and optical power budget

On the Impact of Optimal Modulation and FEC Overhead on Future Optical Networks

The potential of optimum selection of modulation and forward error correction (FEC) overhead (OH) in future transparent nonlinear optical mesh networks is studied from an information theory perspective. Different network topologies are studied as well as both ideal soft-decision (SD) and hard-decision (HD) FEC based on demap-and-decode (bit-wise) receivers. When compared to the de-facto QPSK with 7% OH, our results show large gains in network throughput. When compared to SD-FEC, HD-FEC is shown to cause network throughput losses of 12%, 15%, and 20% for a country, continental, and global network topology, respectively. Furthermore, it is shown that most of the theoretically possible gains can be achieved by using one modulation format and only two OHs. This is in contrast to the infinite number of OHs required in the ideal case. The obtained optimal OHs are between 5% and 80%, which highlights the potential advantage of using FEC with high OHs.Comment: Some minor typos were correcte

IDEALIST control and service management solutions for dynamic and adaptive flexi-grid DWDM networks

Wavelength Switched Optical Networks (WSON) were designed with the premise that all channels in a network have the same spectrum needs, based on the ITU-T DWDM grid. However, this rigid grid-based approach is not adapted to the spectrum requirements of the signals that are best candidates for long-reach transmission and high-speed data rates of 400Gbps and beyond. An innovative approach is to evolve the fixed DWDM grid to a flexible grid, in which the optical spectrum is partitioned into fixed-sized spectrum slices. This allows facilitating the required amount of optical bandwidth and spectrum for an elastic optical connection to be dynamically and adaptively allocated by assigning the necessary number of slices of spectrum. The ICT IDEALIST project will provide the architectural design, protocol specification, implementation, evaluation and standardization of a control plane and a network and service management system. This architecture and tools are necessary to introduce dynamicity, elasticity and adaptation in flexi-grid DWDM networks. This paper provides an overview of the objectives, framework, functional requirements and use cases of the elastic control plane and the adaptive network and service management system targeted in the ICT IDEALIST project

An Overview on Application of Machine Learning Techniques in Optical Networks

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

RECODIS : Resilient communication services protecting end-user applications from disaster-based failures

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