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Part of: Synthesis digital library of engineering and computer science.

Includes bibliographical references (pages 187-191).

1. Congestion control models -- 1.1 Network model -- 1.2 Classical TCP/AQM protocols -- 1.2.1 Window-based congestion control -- 1.2.2 TCP algorithms -- 1.2.3 AQM algorithms -- 1.3 Models of classical algorithms -- 1.3.1 Reno/RED -- 1.3.2 Vegas/DropTail -- 1.3.3 FAST/DropTail -- 1.4 A general setup -- 1.4.1 The basic models -- 1.4.2 Limitations and extensions -- 1.5 Solution of the basic models -- 1.5.1 Existence and uniqueness theorems -- 1.5.2 Application to TCP/AQM models -- 1.5.3 Appendix: proof of lemma 1.3 -- 1.5.4 Appendix: proof of theorem 1.10 -- 1.6 Bibliographical notes -- 1.7 Problems --

2. Equilibrium structure -- 2.1 Convex optimization -- 2.1.1 Convex program -- 2.1.2 KKT theorem and duality -- 2.2 Network utility maximization -- 2.2.1 Example: Reno/RED -- 2.2.2 Examples: Vegas/DropTail; FAST/DropTail -- 2.2.3 Equilibrium of dual algorithms -- 2.2.4 Equilibrium of primal-dual algorithms -- 2.3 Implications of network utility maximization -- 2.3.1 TCP/AQM protocols -- 2.3.2 Utility function, throughput, and fairness -- 2.4 Appendix: existence of utility functions -- 2.5 Bibliographical notes -- 2.6 Problems --

3. Global stability: Lyapunov method -- 3.1 Lyapunov stability theorems -- 3.2 Stability of dual algorithms -- 3.3 Stability of primal-dual algorithms -- 3.4 Appendix: proof of lemma 3.10 -- 3.5 Bibliographical notes -- 3.6 Problems --

4. Global stability: passivity method -- 4.1 Passive systems -- 4.2 Feedback systems -- 4.3 Stability of primal algorithms -- 4.4 Stability of primal-dual algorithms -- 4.5 Bibliographical notes --

5. Global stability: gradient projection method -- 5.1 Convergence theorems -- 5.2 Stability of dual algorithms -- 5.3 Appendix: proof of lemma 5.2 -- 5.4 Appendix: proof of lemma 5.4 -- 5.5 Bibliographical notes --

6. Local stability with delay -- 6.1 Linear model with feedback delay -- 6.2 Nyquist stability theory -- 6.2.1 LTI systems, transfer functions, and realizations -- 6.2.2 Stability of LTI systems -- 6.2.3 Feedback systems and loop functions -- 6.2.4 Stability of closed-loop systems -- 6.2.5 Generalized Nyquist stability criterion -- 6.2.6 Unity feedback systems -- 6.3 Stability of primal algorithms -- 6.4 Stability of dual algorithms -- 6.5 Appendix: proof of theorem 6.14 -- 6.6 Bibliographical notes -- 6.7 Problems --

Bibliography -- Author's biography.

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The congestion control mechanism has been responsible for maintaining stability as the Internet scaled up by many orders of magnitude in size, speed, traffic volume, coverage, and complexity over the last three decades. In this book, we develop a coherent theory of congestion control from the ground up to help understand and design these algorithms. We model network traffic as fluids that flow from sources to destinations and model congestion control algorithms as feedback dynamical systems. We show that the model is well defined. We characterize its equilibrium points and prove their stability. We will use several real protocols for illustration but the emphasis will be on various mathematical techniques for algorithm analysis. Specifically we are interested in four questions: 1. How are congestion control algorithms modelled? 2. Are the models well defined? 3. How are the equilibrium points of a congestion control model characterized? 4. How are the stability of these equilibrium points analyzed? For each topic, we first present analytical tools, from convex optimization, to control and dynamical systems, Lyapunov and Nyquist stability theorems, and to projection and contraction theorems. We then apply these basic tools to congestion control algorithms and rigorously prove their equilibrium and stability properties. A notable feature of this book is the careful treatment of projected dynamics that introduces discontinuity in our differential equations. Even though our development is carried out in the context of congestion control, the set of system theoretic tools employed and the process of understanding a physical system, building mathematical models, and analyzing these models for insights have a much wider applicability than to congestion control.

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Title from PDF title page (viewed on July 21, 2017).