Towards Scalable Cost-Effective Service and Survivability Provisioning in Ultra High Speed Networks

Optical transport networks based on wavelength division multiplexing (WDM) are considered to be the most appropriate choice for future Internet backbone. On the other hand, future DOE networks are expected to have the ability to dynamically provision on-demand survivable services to suit the needs of various high performance scientific applications and remote collaboration. Since a failure in aWDMnetwork such as a cable cut may result in a tremendous amount of data loss, efficient protection of data transport in WDM networks is therefore essential. As the backbone network is moving towards GMPLS/WDM optical networks, the unique requirement to support DOE’s science mission results in challenging issues that are not directly addressed by existing networking techniques and methodologies. The objectives of this project were to develop cost effective protection and restoration mechanisms based on dedicated path, shared path, preconfigured cycle (p-cycle), and so on, to deal with single failure, dual failure, and shared risk link group (SRLG) failure, under different traffic and resource requirement models; to devise efficient service provisioning algorithms that deal with application specific network resource requirements for both unicast and multicast; to study various aspects of traffic grooming in WDM ring and mesh networks to derive cost effective solutions while meeting application resource and QoS requirements; to design various diverse routing and multi-constrained routing algorithms, considering different traffic models and failure models, for protection and restoration, as well as for service provisioning; to propose and study new optical burst switched architectures and mechanisms for effectively supporting dynamic services; and to integrate research with graduate and undergraduate education. All objectives have been successfully met. This report summarizes the major accomplishments of this project. The impact of the project manifests in many aspects: First, the project addressed many essential problems that arisen in current and future WDM optical networks, and provided a host of innovative solutions though there was no invention or patent filing. This project resulted in more than 2 dozens publications in major journals and conferences (including papers in IEEE Transactions and journals, as well as a book chapter). Our publications have been cited by many peer researchers. In particular, one of our conference papers was nominated for the best paper award of IEEE/Create-Net Broadnets (International Conference on Broadband Communications, Networks, and Systems) 2006. Second, the results and solutions of this project were well received by DOE Labs where presentations were given by the PI. We hope to continue the collaboration with DOE Labs in the future. Third, the project was the first to propose and extensively study multicast traffic grooming, new traffic models such as sliding scheduled traffic model and scheduled traffic model. Our research has sparkled a flurry of recent studies and publications by the research community in these areas. Fourth, the project has benefited a diverse population of students by motivating, engaging, enhancing their learning and skills. The project has been conducted in a manner conducive to the training of students both at graduate and undergraduate levels. As a result, one Ph.D., Dr. Abdur Billah, was graduated. Another Ph.D. student, Tianjian Li, will graduate in January 2007. In addition, four MS students were graduated. One undergraduate student, Jeffrey Alan Shininger, completed his university honors project. Fifth, thanks to the support of this ECPI project, the PI has obtained additional funding from the National Science Foundation, the Air Force Research Lab, and other sources. A few other proposals are pending. Finally, this project has also significantly impacted the curricula and resulted in the enhancement of courses at the graduate and undergraduate levels, therefore strengthening the bond between research and education

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

Call admission and routing in telecommunication networks.

by Kit-man Chan.Thesis (M.Phil.)--Chinese University of Hong Kong, 1994.Includes bibliographical references (leaves 82-86).Chapter 1 --- Introduction --- p.1Chapter 1.1 --- Overview of Integrated Service Digital Networks --- p.1Chapter 1.2 --- Multirate Loss Networks --- p.5Chapter 1.3 --- Previous Work --- p.7Chapter 1.4 --- Organization --- p.11Chapter 1.5 --- Publications --- p.12Chapter 2 --- Call Admission in Multirate Loss Networks --- p.13Chapter 2.1 --- Introduction --- p.13Chapter 2.2 --- Two Adaptive Routing Rules --- p.15Chapter 2.3 --- Call Admission Policies --- p.17Chapter 2.4 --- Analysis of Call Admission Policies --- p.25Chapter 2.4.1 --- "The CS, LO, GB and the EB Policies" --- p.25Chapter 2.4.2 --- The DP Policy --- p.29Chapter 2.5 --- Performance Comparisons --- p.32Chapter 2.6 --- Concluding Remarks --- p.35Chapter 3 --- Least Congestion Routing in Multirate Loss Networks --- p.41Chapter 3.1 --- Introduction --- p.41Chapter 3.2 --- The M2 and MTB Routings --- p.42Chapter 3.2.1 --- M2 Routing --- p.43Chapter 3.2.2 --- MTB Routing --- p.43Chapter 3.3 --- Bandwidth Sharing Policies and State Aggregation --- p.45Chapter 3.4 --- Analysis of M2 Routing --- p.47Chapter 3.5 --- Analysis of MTB Routing --- p.50Chapter 3.6 --- Numerical Results and Discussions --- p.53Chapter 3.7 --- Concluding Remarks --- p.56Chapter 4 --- The Least Congestion Routing in WDM Lightwave Networks --- p.60Chapter 4.1 --- Introduction --- p.60Chapter 4.2 --- Architecture and Some Design Issues --- p.62Chapter 4.3 --- The Routing Rule --- p.66Chapter 4.4 --- Analysis of the LC Routing Rule --- p.67Chapter 4.4.1 --- Fixed Point Model --- p.67Chapter 4.4.2 --- Without Direct-link Priority --- p.68Chapter 4.4.3 --- With Direct-link Priority --- p.72Chapter 4.5 --- Performance Comparisons --- p.73Chapter 4.6 --- Concluding Remarks --- p.75Chapter 5 --- Conclusions and Future Work --- p.79Chapter 5.1 --- Future Work --- p.8

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

Journal of Telecommunications and Information Technology, 2014, nr 3

kwartalni

A Connection Admission Control Framework for UMTS based Satellite Systems.An Adaptive Admission Control algorithm with pre-emption control mechanism for unicast and multicast communications in satellite UMTS.

In recent years, there has been an exponential growth in the use of multimedia applications. A satellite system offers great potential for multimedia applications with its ability to broadcast and multicast a large amount of data over a very large area as compared to a terrestrial system. However, the limited transmission capacity along with the dynamically varying channel conditions impedes the delivery of good quality multimedia service in a satellite system which has resulted in research efforts for deriving efficient radio resource management techniques. This issue is addressed in this thesis, where the main emphasis is to design a CAC framework which maximizes the utilization of the scarce radio resources available in the satellite and at the same time increases the performance of the system for a UMTS based satellite system supporting unicast and multicast traffic. The design of the system architecture for a UMTS based satellite system is presented. Based on this architecture, a CAC framework is designed consisting of three different functionalities: the admission control procedure, the retune procedure and the pre-emption procedure. The joint use of these functionalities is proposed to allow the performance of the system to be maintained under congestion. Different algorithms are proposed for different functionalities; an adaptive admission control algorithm, a greedy retune algorithm and three pre-emption algorithms (Greedy, SubSetSum, and Fuzzy). A MATLAB simulation model is developed to study the performance of the proposed CAC framework. A GUI is created to provide the user with the flexibility to configure the system settings before starting a simulation. The configuration settings allow the system to be analysed under different conditions. The performance of the system is measured under different simulation settings such as enabling and disabling of the two functionalities of the CAC framework; retune procedure and the pre-emption procedure. The simulation results indicate the CAC framework as a whole with all the functionalities performs better than the other simulation settings

Mobile Ad Hoc Networks

Guiding readers through the basics of these rapidly emerging networks to more advanced concepts and future expectations, Mobile Ad hoc Networks: Current Status and Future Trends identifies and examines the most pressing research issues in Mobile Ad hoc Networks (MANETs). Containing the contributions of leading researchers, industry professionals, and academics, this forward-looking reference provides an authoritative perspective of the state of the art in MANETs. The book includes surveys of recent publications that investigate key areas of interest such as limited resources and the mobility of mobile nodes. It considers routing, multicast, energy, security, channel assignment, and ensuring quality of service. Also suitable as a text for graduate students, the book is organized into three sections: Fundamentals of MANET Modeling and Simulation—Describes how MANETs operate and perform through simulations and models Communication Protocols of MANETs—Presents cutting-edge research on key issues, including MAC layer issues and routing in high mobility Future Networks Inspired By MANETs—Tackles open research issues and emerging trends Illustrating the role MANETs are likely to play in future networks, this book supplies the foundation and insight you will need to make your own contributions to the field. It includes coverage of routing protocols, modeling and simulations tools, intelligent optimization techniques to multicriteria routing, security issues in FHAMIPv6, connecting moving smart objects to the Internet, underwater sensor networks, wireless mesh network architecture and protocols, adaptive routing provision using Bayesian inference, and adaptive flow control in transport layer using genetic algorithms

Mobile Ad Hoc Networks

Guiding readers through the basics of these rapidly emerging networks to more advanced concepts and future expectations, Mobile Ad hoc Networks: Current Status and Future Trends identifies and examines the most pressing research issues in Mobile Ad hoc Networks (MANETs). Containing the contributions of leading researchers, industry professionals, and academics, this forward-looking reference provides an authoritative perspective of the state of the art in MANETs. The book includes surveys of recent publications that investigate key areas of interest such as limited resources and the mobility of mobile nodes. It considers routing, multicast, energy, security, channel assignment, and ensuring quality of service. Also suitable as a text for graduate students, the book is organized into three sections: Fundamentals of MANET Modeling and Simulation—Describes how MANETs operate and perform through simulations and models Communication Protocols of MANETs—Presents cutting-edge research on key issues, including MAC layer issues and routing in high mobility Future Networks Inspired By MANETs—Tackles open research issues and emerging trends Illustrating the role MANETs are likely to play in future networks, this book supplies the foundation and insight you will need to make your own contributions to the field. It includes coverage of routing protocols, modeling and simulations tools, intelligent optimization techniques to multicriteria routing, security issues in FHAMIPv6, connecting moving smart objects to the Internet, underwater sensor networks, wireless mesh network architecture and protocols, adaptive routing provision using Bayesian inference, and adaptive flow control in transport layer using genetic algorithms