Control Techniques for Complex Networks

From foundations to state-of-the-art; the tools and philosophy you need to build network models.

Sean Meyn (Author)

9780521884419, Cambridge University Press

Hardback, published 10 December 2007

582 pages, 98 exercises
26 x 18.3 x 3.3 cm, 1.166 kg

'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

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. Core chapters assume only exposure to stochastic processes and linear algebra at 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 at the heart 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 are available on a companion website.

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.

Subject Areas: Optimization [PBU], Probability & statistics [PBT]