Regenerator placement and fault management in multi-wavelength optical networks.

Shen, Dong.Thesis (M.Phil.)--Chinese University of Hong Kong, 2011.Includes bibliographical references (p. 98-106).Abstracts in English and Chinese.Abstract --- p.i摘要 --- p.ivAcknowledgements --- p.vTable of Contents --- p.viChapter Chapter 1 --- Background --- p.1Chapter 1.1 --- Translucent Optical Networks --- p.1Chapter 1.1.1 --- The Way Towards Translucent --- p.1Chapter 1.1.2 --- Translucent Optical Network Architecture Design and Planning --- p.3Chapter 1.1.3 --- Other Research Topics in Translucent Optical Networks --- p.6Chapter 1.2 --- Fault Monitoring in All-Optical Networks --- p.12Chapter 1.2.1 --- Fault Monitoring in Network Layer's Perspective --- p.12Chapter 1.2.2 --- Passive Optical Monitoring --- p.14Chapter 1.2.3 --- Proactive Optical Monitoring --- p.16Chapter 1.3 --- Contributions --- p.17Chapter 1.3.1 --- Translucent Optical Network Planning with Heterogeneous Modulation Formats --- p.17Chapter 1.3.2 --- Multiplexing Optimization in Translucent Optical Networks --- p.19Chapter 1.3.3 --- An Efficient Regenerator Placement and Wavelength Assignment Scheme in Translucent Optical Networks --- p.20Chapter 1.3.4 --- Adaptive Fault Monitoring in All-Optical Networks Utilizing Real-Time Data Traffic --- p.20Chapter 1.4 --- Organization of Thesis --- p.22Chapter Chapter 2 --- Regenerator Placement and Resource Allocation Optimization in Translucent Optical Networks --- p.23Chapter 2.1 --- Introduction --- p.23Chapter 2.2 --- Translucent Optical Network Planning with Heterogeneous Modulation Formats --- p.25Chapter 2.2.1 --- Motivation and Problem Statements --- p.25Chapter 2.2.2 --- A Two-Step Planning Algorithm Using Two Modulation Formats to Realize Any-to-Any Topology Connectivity --- p.28Chapter 2.2.3 --- Illustrative Examples --- p.30Chapter 2.2.3 --- ILP Formulation of Minimizing Translucent Optical Network Cost with Two Modulation Formats under Static Traffic Demands --- p.34Chapter 2.2.4 --- Illustrative Numeric Examples --- p.42Chapter 2.3 --- Resource Allocation Optimization in Translucent Optical Networks --- p.45Chapter 2.3.1 --- Multiplexing Optimization with Auxiliary Graph --- p.45Chapter 2.3.2 --- Simulation Study of Proposed Algorithm --- p.51Chapter 2.3.3 --- An Efficient Regenerator Placement and Wavelength Assignment Solution --- p.55Chapter 2.3.4 --- Simulation Study of Proposed Algorithm --- p.60Chapter 2.4 --- Summary --- p.64Chapter Chapter 3 --- Adaptive Fault Monitoring in All-Optical Networks Utilizing Real-Time Data Traffic --- p.65Chapter 3.1 --- Introduction --- p.65Chapter 3.2 --- Adaptive Fault Monitoring --- p.68Chapter 3.2.1 --- System Framework --- p.68Chapter 3.2.2 --- Phase 1: Passive Monitoring --- p.70Chapter 3.2.3 --- Phase 2: Proactive Probing --- p.71Chapter 3.2.4 --- Control Plane Design and Analysis --- p.80Chapter 3.2.5 --- Physical Layer Implementation and Suggestions --- p.83Chapter 3.3 --- Placement of Label Monitors --- p.83Chapter 3.3.1 --- ILP Formulation --- p.84Chapter 3.3.2 --- Simulation Studies --- p.86Chapter 3.3.3 --- Discussion of Topology Evolution Adaptiveness --- p.93Chapter 3.4 --- Summary --- p.95Chapter Chapter 4 --- Conclusions and Future Work --- p.95Chapter 4.1 --- Conclusions --- p.96Chapter 4.2 --- Future Work --- p.97Bibliography --- p.98Publications during M.Phil Study --- p.10

A Survey on Communication Networks for Electric System Automation

Published in Computer Networks 50 (2006) 877–897, an Elsevier journal. The definitive version of this publication is available from Science Direct. Digital Object Identifier:10.1016/j.comnet.2006.01.005In today’s competitive electric utility marketplace, reliable and real-time information become the key factor for reliable delivery of power to the end-users, profitability of the electric utility and customer satisfaction. The operational and commercial demands of electric utilities require a high-performance data communication network that supports both existing functionalities and future operational requirements. In this respect, since such a communication network constitutes the core of the electric system automation applications, the design of a cost-effective and reliable network architecture is crucial. In this paper, the opportunities and challenges of a hybrid network architecture are discussed for electric system automation. More specifically, Internet based Virtual Private Networks, power line communications, satellite communications and wireless communications (wireless sensor networks, WiMAX and wireless mesh networks) are described in detail. The motivation of this paper is to provide a better understanding of the hybrid network architecture that can provide heterogeneous electric system automation application requirements. In this regard, our aim is to present a structured framework for electric utilities who plan to utilize new communication technologies for automation and hence, to make the decision making process more effective and direct.This work was supported by NEETRAC under Project #04-157

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

Unattended network operations technology assessment study. Technical support for defining advanced satellite systems concepts

The results are summarized of an unattended network operations technology assessment study for the Space Exploration Initiative (SEI). The scope of the work included: (1) identified possible enhancements due to the proposed Mars communications network; (2) identified network operations on Mars; (3) performed a technology assessment of possible supporting technologies based on current and future approaches to network operations; and (4) developed a plan for the testing and development of these technologies. The most important results obtained are as follows: (1) addition of a third Mars Relay Satellite (MRS) and MRS cross link capabilities will enhance the network's fault tolerance capabilities through improved connectivity; (2) network functions can be divided into the six basic ISO network functional groups; (3) distributed artificial intelligence technologies will augment more traditional network management technologies to form the technological infrastructure of a virtually unattended network; and (4) a great effort is required to bring the current network technology levels for manned space communications up to the level needed for an automated fault tolerance Mars communications network

Artificial intelligence (AI) methods in optical networks: A comprehensive survey

Producción CientíficaArtificial intelligence (AI) is an extensive scientific discipline which enables computer systems to solve problems by emulating complex biological processes such as learning, reasoning and self-correction. This paper presents a comprehensive review of the application of AI techniques for improving performance of optical communication systems and networks. The use of AI-based techniques is first studied in applications related to optical transmission, ranging from the characterization and operation of network components to performance monitoring, mitigation of nonlinearities, and quality of transmission estimation. Then, applications related to optical network control and management are also reviewed, including topics like optical network planning and operation in both transport and access networks. Finally, the paper also presents a summary of opportunities and challenges in optical networking where AI is expected to play a key role in the near future.Ministerio de Economía, Industria y Competitividad (Project EC2014-53071-C3-2-P, TEC2015-71932-REDT