Control Techniques for Complex Networks

'Sean Meyn's text is a wonderful piece of work ... It progresses through a series of important topics, running the gamut from modern control techniques for queuing system analysis, to optimization of deterministic network models, to computer simulation methods; and all the while, it provides rigorous mathematical foundations alongside a variety of clever, practical applications. The lively writing style and apt examples keep everything interesting, and I believe that readers will greatly appreciate and benefit from this unique book.' David M. Goldsman, Georgia Institute of Technology

'Sean Meyn's earlier book with Tweedie is the bible for economists who use Markov models to do everything from formulating asset pricing models to constructing Bayesian posteriors for dynamic models. This book is a gold mine of useful new ideas. I predict that the ideas in chapter 11 alone will have a big impact on the way we think about computing rational expectations equilibria.' Thomas Sargent, New York University; Winner of the 2011 Nobel Prize in Economic Sciences

'The first comprehensive account of some major strands of research in modeling, approximation, stability analysis and optimization of stochastic networks, from a leader in the field ... Notable among these are its coverage of deterministic fluid limits, controlled random walk models, approximation via workload relaxation, and implications of these to stability and optimization of networks. Several important special instances are worked out in detail. A valuable resource for both researchers and practitioners.' Vivek S. Borkar, Tata Institute of Fundamental Research

'In my opinion this book is written primarily for seasoned researchers in the field who need a nice source of existing results and ideas. In this vein the book is outstanding and it should become an indispensable aid to researchers and practitioners. ... All in all this is an excellent book ...' Mathematical Reviews

Sean Meyn is professor of electrical and computer engineering at the University of Illinois, and a fellow of the IEEE. He is co-author with Richard Tweedie of Markov Chains and Stochastic Stability, which received the 1994 ORSA/TIMS Best Publication in Applied Probability Award.

Preface; 1. Introduction; Part I. Modeling and Control: 2. Examples; 3. The single-server queue; 4. Scheduling; Part II. Workload: 5. Workload and scheduling; 6. Routing and resource pooling; 7. Demand; Part III. Stability and Performance: 8. Foster-Lyapunov techniques; 9. Optimization; 10. ODE methods; 11. Simulation and learning; Appendix. Markov models; References; Index.