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

Traffic Engineering in G-MPLS networks with QoS guarantees

In this paper a new Traffic Engineering (TE) scheme to efficiently route sub-wavelength requests with different QoS requirements is proposed for G-MPLS networks. In most previous studies on TE based on dynamic traffic grooming, the objectives were to minimize the rejection probability by respecting the constraints of the optical node architecture, but without considering service differentiation. In practice, some high-priority (HP) connections can instead be characterized by specific constraints on the maximum tolerable end-to-end delay and packet-loss ratio. The proposed solution consists of a distributed two-stage scheme: each time a new request arrives, an on-line dynamic grooming scheme finds a route which fulfills the QoS requirements. If a HP request is blocked at the ingress router, a preemption algorithm is executed locally in order to create room for this traffic. The proposed preemption mechanism minimizes the network disruption, both in term of number of rerouted low-priority connections and new set-up lightpaths, and the signaling complexity. Extensive simulation experiments are performed to demonstrate the efficiency of our scheme

Joint dimensioning of server and network infrastructure for resilient optical grids/clouds

We address the dimensioning of infrastructure, comprising both network and server resources, for large-scale decentralized distributed systems such as grids or clouds. We design the resulting grid/cloud to be resilient against network link or server failures. To this end, we exploit relocation: Under failure conditions, a grid job or cloud virtual machine may be served at an alternate destination (i.e., different from the one under failure-free conditions). We thus consider grid/cloud requests to have a known origin, but assume a degree of freedom as to where they end up being served, which is the case for grid applications of the bag-of-tasks (BoT) type or hosted virtual machines in the cloud case. We present a generic methodology based on integer linear programming (ILP) that: 1) chooses a given number of sites in a given network topology where to install server infrastructure; and 2) determines the amount of both network and server capacity to cater for both the failure-free scenario and failures of links or nodes. For the latter, we consider either failure-independent (FID) or failure-dependent (FD) recovery. Case studies on European-scale networks show that relocation allows considerable reduction of the total amount of network and server resources, especially in sparse topologies and for higher numbers of server sites. Adopting a failure-dependent backup routing strategy does lead to lower resource dimensions, but only when we adopt relocation (especially for a high number of server sites): Without exploiting relocation, potential savings of FD versus FID are not meaningful

Considering Transmission Impairments in Wavelength Routed Networks

Abstract — We consider dynamically reconfigurable wavelength routed networks in which lightpaths carrying IP traffic are on demand established. We face the Routing and Wavelength Assignment problem considering as constraints the physical impairments that arise in all-optical wavelength routed networks. In particular, we study the impact of the physical layer when establishing a lightpath in transparent optical network. Because no signal transformation and regeneration at intermediate nodes occurs, noise and signal distortions due to non-ideal transmission devices are accumulated along the physical path, and they degrade the quality of the received signal. We propose a simple yet accurate model for the physical layer which consider both static and dynamic impairments, i.e., nonlinear effects depending on the actual wavelength/lightpath allocation. We then propose a novel algorithm to solve the RWA problem that explicitly considers the physical impairments. Simulation results show the effectiveness of our approach. Indeed, when the transmission impairments come into play, an accurate selection of paths and wavelengths which is driven by physical consideration is mandatory. I

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