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.
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.