Energy management in communication networks: a journey through modelling and optimization glasses

The widespread proliferation of Internet and wireless applications has produced a significant increase of ICT energy footprint. As a response, in the last five years, significant efforts have been undertaken to include energy-awareness into network management. Several green networking frameworks have been proposed by carefully managing the network routing and the power state of network devices. Even though approaches proposed differ based on network technologies and sleep modes of nodes and interfaces, they all aim at tailoring the active network resources to the varying traffic needs in order to minimize energy consumption. From a modeling point of view, this has several commonalities with classical network design and routing problems, even if with different objectives and in a dynamic context. With most researchers focused on addressing the complex and crucial technological aspects of green networking schemes, there has been so far little attention on understanding the modeling similarities and differences of proposed solutions. This paper fills the gap surveying the literature with optimization modeling glasses, following a tutorial approach that guides through the different components of the models with a unified symbolism. A detailed classification of the previous work based on the modeling issues included is also proposed

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

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

Traffic engineering in dynamic optical networks

Traffic Engineering (TE) refers to all the techniques a Service Provider employs to improve the efficiency and reliability of network operations. In IP over Optical (IPO) networks, traffic coming from upper layers is carried over the logical topology defined by the set of established lightpaths. Within this framework then, TE techniques allow to optimize the configuration of optical resources with respect to an highly dynamic traffic demand. TE can be performed with two main methods: if the demand is known only in terms of an aggregated traffic matrix, the problem of automatically updating the configuration of an optical network to accommodate traffic changes is called Virtual Topology Reconfiguration (VTR). If instead the traffic demand is known in terms of data-level connection requests with sub-wavelength granularity, arriving dynamically from some source node to any destination node, the problem is called Dynamic Traffic Grooming (DTG). In this dissertation new VTR algorithms for load balancing in optical networks based on Local Search (LS) techniques are presented. The main advantage of using LS is the minimization of network disruption, since the reconfiguration involves only a small part of the network. A comparison between the proposed schemes and the optimal solutions found via an ILP solver shows calculation time savings for comparable results of network congestion. A similar load balancing technique has been applied to alleviate congestion in an MPLS network, based on the efficient rerouting of Label-Switched Paths (LSP) from the most congested links to allow a better usage of network resources. Many algorithms have been developed to deal with DTG in IPO networks, where most of the attention is focused on optimizing the physical resources utilization by considering specific constraints on the optical node architecture, while very few attention has been put so far on the Quality of Service (QoS) guarantees for the carried traffic. In this thesis a novel Traffic Engineering scheme is proposed to guarantee QoS from both the viewpoint of service differentiation and transmission quality. Another contribution in this thesis is a formal framework for the definition of dynamic grooming policies in IPO networks. The framework is then specialized for an overlay architecture, where the control plane of the IP and optical level are separated, and no information is shared between the two. A family of grooming policies based on constraints on the number of hops and on the bandwidth sharing degree at the IP level is defined, and its performance analyzed in both regular and irregular topologies. While most of the literature on DTG problem implicitly considers the grooming of low-speed connections onto optical channels using a TDM approach, the proposed grooming policies are evaluated here by considering a realistic traffic model which consider a Dynamic Statistical Multiplexing (DSM) approach, i.e. a single wavelength channel is shared between multiple IP elastic traffic flows