Control and Performance in Packet, Circuit, and ATM Networks

Overview

Higher transmission speeds, larger traffic volumes and the unpredictable characteristics of new applications, coupled with new end-user functions and features and variability of bandwidth, demand new networking methods, technologies and algorithms.
These new directions are explored in Control and Performance in Packet, Circuit, and ATM Networks, which includes discussions of static flow, fluid flow, dynamic flow, learning automata, neural networks, fuzzy logic and much more. ...

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Hardcover (1995)
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Overview

Higher transmission speeds, larger traffic volumes and the unpredictable characteristics of new applications, coupled with new end-user functions and features and variability of bandwidth, demand new networking methods, technologies and algorithms.
These new directions are explored in Control and Performance in Packet, Circuit, and ATM Networks, which includes discussions of static flow, fluid flow, dynamic flow, learning automata, neural networks, fuzzy logic and much more. These techniques are used in performance evaluation, control and optimization of communications networks. Each of the methods achieves a different goal, and understanding them helps in the evaluation of different aspects of control and performance in communication networks.
The book combines queuing theory with static-flow models and static optimization methods in order to analyze the behavior of communication networks. In addition, it reviews queuing theory and static optimization methods for the study of control problems in telecommunication networks.
A valuable reference that can also be used as a text for an advanced course on the subject.

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Editorial Reviews

Booknews
Explains how well known analytic and control techniques are used for performance evaluation, control, and optimization of the new computer communication networks such as circuit and packet networks, and in high-speed networking such as broadband ISDN. The techniques described include static, fluid, and dynamic flow; learning automata; neural networks; and fuzzy logic. Shows how each technique evaluates a different aspect of the networks. The appendices cover simple queuing systems and the Lagrangian multiplier method. Suitable as a text for a one-semester graduate course on network modeling and analysis. Annotation c. Book News, Inc., Portland, OR (booknews.com)
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Product Details

Table of Contents

Preface. 1. Components of a telecommunication network. 2. Networking. 3. Network performance measures. 4. Static flow model. 5. Dynamic flow models. 6. Learning automata. Appendix A: Simple queuing systems. Appendix B: Lagrangian multiplier method. Index.

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