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

A survey on OFDM-based elastic core optical networking

Orthogonal frequency-division multiplexing (OFDM) is a modulation technology that has been widely adopted in many new and emerging broadband wireless and wireline communication systems. Due to its capability to transmit a high-speed data stream using multiple spectral-overlapped lower-speed subcarriers, OFDM technology offers superior advantages of high spectrum efficiency, robustness against inter-carrier and inter-symbol interference, adaptability to server channel conditions, etc. In recent years, there have been intensive studies on optical OFDM (O-OFDM) transmission technologies, and it is considered a promising technology for future ultra-high-speed optical transmission. Based on O-OFDM technology, a novel elastic optical network architecture with immense flexibility and scalability in spectrum allocation and data rate accommodation could be built to support diverse services and the rapid growth of Internet traffic in the future. In this paper, we present a comprehensive survey on OFDM-based elastic optical network technologies, including basic principles of OFDM, O-OFDM technologies, the architectures of OFDM-based elastic core optical networks, and related key enabling technologies. The main advantages and issues of OFDM-based elastic core optical networks that are under research are also discussed

Performance Evaluation of Non-Hitless Spectrum Defragmentation Algorithms in Elastic Optical Networks

Fragmentation in Elastic Optical Networks is an issue caused by isolated, non-aligned, and non-contiguous frequency slots that can not be used to allocate new connection request to the network, due to the optical layer restrictions imposed to the Routing and Spectrum Assignment (RSA) algorithms. To deal with this issue, several studies about Spectrum Defragmentation have been presented. In this work, we analyze the most important Non-Hitless Defragmentation Algorithms found in the literature, with proactive and reactive approaches that include rerouting and non-rerouting schemes, and compare their performance in terms of Blocking Probability, Entropy, and Bandwidth Fragmentation Ratio. Simulations results showed that the Fragmentation Aware schemes outperformed the other algorithms in low traffic load, but the Reactive schemes got better results in high traffic load.Sociedad Argentina de Informática e Investigación Operativa (SADIO

Combination of Advanced Reservation and Resource Periodic Arrangement for RMSA in EON with Deep Reinforcement Learning

The Elastic Optical Networks (EON) provide a solution to the massive demand for connections and extremely high data traffic with the Routing Modulation and Spectrum Assignment (RMSA) as a challenge. In previous RMSA research, there was a high blocking probability because the route to be passed by the K-SP method with a deep neural network approach used the First Fit policy, and the modulation problem was solved with Modulation Format Identification (MFI) or BPSK using Deep Reinforcement Learning. The issue might be apparent in spectrum assignment because of the influence of Advanced Reservation (AR) and Resource Periodic Arrangement (RPA), which is a decision block on a connection request path with both idle and active data traffic. The study’s limitation begins with determining the modulation of m = 1 and m = 4, followed by the placement of frequencies, namely 13 with a combination of standard block frequencies 41224–24412, so that the simulation results are less than 0.0199, due to the combination of block frequency slices with spectrum allocation rule techniques.