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

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

Cross-layer static resource provisioning for dynamic traffic in flexible grid optical networks

Flexible grid networks need rigorous resource planning to avoid network over-dimensioning and resource over-provisioning. The network must provision the hardware and spectrum resources statically, even for dynamic random bandwidth demands, due to the infrastructure of flexible grid networks, hardware limitations, and reconfiguration speed of the control plane. We propose a flexible online–offline probabilistic (FOOP) algorithm for the static spectrum assignment of random bandwidth demands. The FOOP algorithm considers the probabilistic nature of random bandwidth demands and balances hardware and control plane pressures with spectrum assignment efficiency. The FOOP algorithm uses the probabilistic spectrum Gaussian noise (PSGN) model to estimate the physical-layer impairment (PLI) for random bandwidth traffic. Compared to a benchmark spectrum assignment algorithm and a widely applied PLI estimation model, the proposed FOOP algorithm using the PSGN model saves up to 49% of network resources

Management of Spectral Resources in Elastic Optical Networks

Recent developments in the area of mobile technologies, data center networks, cloud computing and social networks have triggered the growth of a wide range of network applications. The data rate of these applications also vary from a few megabits per second (Mbps) to several Gigabits per second (Gbps), thereby increasing the burden on the Inter- net. To support this growth in Internet data traffic, one foremost solution is to utilize the advancements in optical networks. With technology such as wavelength division multiplexing (WDM) networks, bandwidth upto 100 Gbps can be exploited from the optical fiber in an energy efficient manner. However, WDM networks are not efficient when the traffic demands vary frequently. Elastic Optical Networks (EONs) or Spectrum Sliced Elastic Optical Path Networks (SLICE) or Flex-Grid has been recently proposed as a long-term solution to handle the ever-increasing data traffic and the diverse demand range. EONs provide abundant bandwidth by managing the spectrum resources as fine-granular orthogonal sub-carriers that makes it suitable to accommodate varying traffic demands. However, the Routing and Spectrum Allocation (RSA) algorithm in EONs has to follow additional constraints while allocating sub-carriers to demands. These constraints increase the complexity of RSA in EONs and also, make EONs prone to the fragmentation of spectral resources, thereby decreasing the spectral efficiency. The major objective of this dissertation is to study the problem of spectrum allocation in EONs under various network conditions. With this objective, this dissertation presents the author\u27s study and research on multiple aspects of spectrum allocation in EONs: how to allocate sub-carriers to the traffic demands, how to accommodate traffic demands that varies with time, how to minimize the fragmentation of spectral resources and how to efficiently integrate the predictability of user demands for spectrum assignment. Another important contribution of this dissertation is the application of EONs as one of the substrate technologies for network virtualization

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

Static resource allocation for dynamic traffic

A flexible offline probabilistic (FOP) algorithm is designed to aggressively accommodate random bandwidth traffic demands in long-haul networks. Compared to algorithms that configure demands according to their maximum bandwidth, the FOP algorithm can save 15% of the spectrum used, accommodating over 99% of the throughput demand