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

Observing and Modeling the Physical Layer Phenomena in Open Optical Systems for Network planning and management

L'abstract è presente nell'allegato / the abstract is in the attachmen

Physical layer aware open optical networking

L'abstract è presente nell'allegato / the abstract is in the attachmen

Cross-layer modeling and optimization of next-generation internet networks

Scaling traditional telecommunication networks so that they are able to cope with the volume of future traffic demands and the stringent European Commission (EC) regulations on emissions would entail unaffordable investments. For this very reason, the design of an innovative ultra-high bandwidth power-efficient network architecture is nowadays a bold topic within the research community. So far, the independent evolution of network layers has resulted in isolated, and hence, far-from-optimal contributions, which have eventually led to the issues today's networks are facing such as inefficient energy strategy, limited network scalability and flexibility, reduced network manageability and increased overall network and customer services costs. Consequently, there is currently large consensus among network operators and the research community that cross-layer interaction and coordination is fundamental for the proper architectural design of next-generation Internet networks. This thesis actively contributes to the this goal by addressing the modeling, optimization and performance analysis of a set of potential technologies to be deployed in future cross-layer network architectures. By applying a transversal design approach (i.e., joint consideration of several network layers), we aim for achieving the maximization of the integration of the different network layers involved in each specific problem. To this end, Part I provides a comprehensive evaluation of optical transport networks (OTNs) based on layer 2 (L2) sub-wavelength switching (SWS) technologies, also taking into consideration the impact of physical layer impairments (PLIs) (L0 phenomena). Indeed, the recent and relevant advances in optical technologies have dramatically increased the impact that PLIs have on the optical signal quality, particularly in the context of SWS networks. Then, in Part II of the thesis, we present a set of case studies where it is shown that the application of operations research (OR) methodologies in the desing/planning stage of future cross-layer Internet network architectures leads to the successful joint optimization of key network performance indicators (KPIs) such as cost (i.e., CAPEX/OPEX), resources usage and energy consumption. OR can definitely play an important role by allowing network designers/architects to obtain good near-optimal solutions to real-sized problems within practical running times

Impairment -Aware Static Route and Wavelength Assignment in WDM Networks

Routing and Wavelength Assignment (RWA) is a fundamentally important aspect of WDM optical network design. RWA is performed to determine a route and wavelength for each demand requesting resources between a given source and destination node. Classic RWA has only been concerned with determining a route while only taking into account network layer wavelength availability constraints. In recent years the size of WDM optical communication networks has exponentially increased in size. Resulting in the use of very long fibers for interconnecting nodes. On these modern WDM networks, researchers have identified at the physical layer, linear and non-linear impairments. Impairment occurs during the propagation of optical signals across a fiber cable and within the optical switching fabric of routing equipment. These impairments have the potential to either, greatly reduce the efficiency of WDM optical networks, or to completely render lightpaths unusable. Impairment-aware routing and wavelength assignment (IA-RWA) takes different types of impairments of lightpaths into account, while performing the RWA. The use of IA-RWA improves the quality of transmission among lightpaths as well as reduce the blocking ratio. A new heuristic for IA-RWA has been reported in this thesis for use in WDM optical network planning and design. This heuristic takes both linear and non-linear impairments into account during the RWA process. The heuristic uses existing techniques from graph theory, operations research, and optical network design, to determine an IA-RWA in an efficient manner

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 this paper proposing new possible research directions

All optical multicasting in wavelength routing mesh networks with power considerations: design and operation

Wavelength routing Wavelength Division Multiplexing (WDM) are optical networks that support all-optical services. They have become the most appealing candidate for wide area backbone networks. Their huge available bandwidth provides the solution for the exponential growth in trayc demands that is due to the increase in the number of users and the surge of more bandwidth intensive network applications and services. A sizable fraction of these applications and services are of multi-point nature. Therefore, supporting multicast service in this network environment is very critical and unique. The all-optical support of various services has advantages, which includes achieving the signal transparency to its content. Nevertheless, the all-optical operational support comes with an associated cost and new issues that make this problem very challenging. In this thesis, we investigate the power-related issues for supporting multicast service in the optical domain, referred to as All-Optical Multicasting (AOM). Our study treats these issues from two networking contexts, namely, Network Provisioning and Connection Provisioning. We propose a number of optimal and heuristic solutions with a unique objective function for each context. In this regard, the objective function for the network provisioning problem is to reduce the network cost, while the solutions for the connection provisioning problem aim to reduce the connection blocking ratio. The optimal formulations are inherently non-linear. However, we introduce novel methods for linearizing them and formulate the problems as Mixed Integer Linear Programs. Also, the design of the heuristic solutions takes into account various optimization factors which results in efficient heuristics that can produce fast solutions that are relatively close to their optimal counterparts, as shown in the numerical results we present

Wavelengths switching and allocation algorithms in multicast technology using m-arity tree networks topology

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University London.In this thesis, the m-arity tree networks have been investigated to derive equations for their nodes, links and required wavelengths. The relationship among all parameters such as leaves nodes, destinations, paths and wavelengths has been found. Three situations have been explored, firstly when just one server and the leaves nodes are destinations, secondly when just one server and all other nodes are destinations, thirdly when all nodes are sources and destinations in the same time. The investigation has included binary, ternary, quaternary and finalized by general equations for all m-arity tree networks. Moreover, a multicast technology is analysed in this thesis to transmit data carried by specific wavelengths to several clients. Wavelengths multicast switching is well examined to propose split-convert-split-convert (S-C-S-C) multicast switch which consists of light splitters and wavelengths converters. It has reduced group delay by 13% and 29% compared with split-convert (S-C) and split-convert-split (S-C-S) multicast switches respectively. The proposed switch has also increased the received signal power by a significant value which reaches 28% and 26.92% compared with S-C-S and S-C respectively. In addition, wavelengths allocation algorithms in multicast technology are proposed in this thesis using tree networks topology. Distributed scheme is adopted by placing wavelength assignment controller in all parents’ nodes. Two distributed algorithms proposed shortest wavelength assignment (SWA) and highest number of destinations with shortest wavelength assignment (HND-SWA) algorithms to increase the received signal power, decrease group delay and reduce dispersion. The performance of the SWA algorithm was almost better or same as HND-SWA related to the power, dispersion and group delay but they are always better than other two algorithms. The required numbers of wavelengths and their utilised converters have been examined and calculated for the researched algorithms. The HND-SWA has recorded the superior performance compared with other algorithms. It has reduced number of utilised wavelengths up to about 19% and minimized number of the used wavelengths converters up to about 29%. Finally, the centralised scheme is discussed and researched and proposed a centralised highest number of destinations (CHND) algorithm with static and dynamic scenarios to reduce network capacity decreasing (Cd) after each wavelengths allocation. The CDHND has reduced (Cd) by about 16.7% compared with the other algorithms