Control Techniques for Complex Networks
Power grids, flexible manufacturing, cellular communications: interconnectedness has consequences. This remarkable book gives the tools and philosophy you need to build network models detailed enough to capture essential dynamics but simple enough to expose the structure of effective control solutions and to clarify analysis.
Core chapters assume only exposure to stochastic processes and linear algebra at the undergraduate level; later chapters are for advanced graduate students and researchers/practitioners. This gradual development bridges classical theory with the state-of-the-art. The workload model that is the basis of traditional analysis of the single queue becomes a foundation for workload relaxations used in the treatment of complex networks. Lyapunov functions and dynamic programming equations lead to the celebrated MaxWeight policy along with many generalizations. Other topics include methods for synthesizing hedging and safety stocks, stability theory for networks, and techniques for accelerated simulation.
Examples and figures throughout make ideas concrete. Solutions to end-of-chapter exercises available on a companion website.
- Control perspective supports development of simplified analysis for robust control solutions
- Emphasis on visualization techniques builds intuition and understanding
- Range of exercises enables practice with simple concepts through to exploration of theory
Reviews & endorsements
"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 queueing 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
"...outstanding and it should become an indispensable aid to researchers and practitioners... All in all this is an excellent book useful primarily to researchers
in the field."
Yannis A. Phillis, Mathematical Reviews
"The main goal of the book is to describe how simpler, more parsimonious descriptions of a complex network may be used to reach conclusions on the stability, control and design of the original network. The book does a superb job in bringing to light the whole gamut of issues, ranging from modeling and control to simulation, that must be considered... A particularly pleasing aspect of the book is how self-contained it is... This book will no doubt become an invaluable resource for graduate students, researchers and practitioners in this field."
Kavita Ramanan, Journal of the American Statistical Association
Product details
December 2007Hardback
9780521884419
582 pages
260 × 183 × 33 mm
1.166kg
98 exercises
Available
Table of Contents
- 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.
