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.
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Editorial Reviews
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"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
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
Book Description
Power grids, flexible manufacturing, cellular communications: interconnectedness has consequences. This remarkable book gives the tools and philosophy you need to model and control complex networks. Core chapters assume only exposure to stochastic processes and linear algebra at undergraduate level; later chapters are for more advanced graduate students and researchers/practitioners.
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1 of 1 people found the following review helpful:
5.0 out of 5 stars
Excellent book,
This review is from: Control Techniques for Complex Networks (Hardcover)
This book is one of the most exciting additions to the literature on queuing networks. The book illustrates the basic ideas underlying control of complex queueing networks and at the same time presents a lot of the underlying theory that you won't find elsewhere. The mathematical results are presented in detail. Chapter 9 (optimization) is truly a treasure of very useful results and insights. The book is bound to become "the" source for results on queueing-network control for many, many years.
2 of 3 people found the following review helpful:
5.0 out of 5 stars
Review for CTCN,
This review is from: Control Techniques for Complex Networks (Hardcover)
This book is invaluable for anyone trying to study, model/control networks (for example the internet, biological networks, power grids to name a few). The author has given extensive and very clear coverage to numerous issues related to networked systems e.g. scheduling, optimization, routing and resource pooling, hedging and workload etc. Significant time is also spent on developing theory for stochastic and Markov models. The book also has numerous illustrative examples to demonstrate the theoretical framework developed. A "must have" for any applied mathematician.
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Inside This Book
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Key Phrases - Statistically Improbable Phrases (SIPs):
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simple routing model, workload relaxation, pathwise optimal solution, buffer priority policy, nonidling policy, pathwise optimality, idleness process, fluid limit model, workload vectors, diffusion heuristic, ignoring future arrivals, perturbed value function, constituency matrix, unrelaxed model, deficit buffer, simple inventory model, monotone region, nth relaxation, optimal switching curve, processor sharing model, reflected random walk, affine enlargement, randomized stationary policy, fluid model equations, relative value function Key Phrases - Capitalized Phrases (CAPs): (learn more)
Monte Carlo, Comparison Theorem, Foster-Lyapunov Techniques, Strong Law of Large Numbers, Dominated Convergence Theorem, Optimization Proof, Kac's Theorem, Applying Proposition, Azuma's Inequality, Applying Theorem, Min-Cut Max-Flow, Demand Figure, Bellman-Gronwall Lemma, Arzelą-Ascoli Theorem, The Single Server Queue Proof, Markov Decision Process, Fatou's Lemma, Central Limit Theorem, Consider the Klimov, Matrix Inversion Lemma, Last Buffer-First Served, Los Angeles Browse Sample Pages:
Front Cover | Table of Contents | First Pages | Index | Surprise Me!
simple routing model, workload relaxation, pathwise optimal solution, buffer priority policy, nonidling policy, pathwise optimality, idleness process, fluid limit model, workload vectors, diffusion heuristic, ignoring future arrivals, perturbed value function, constituency matrix, unrelaxed model, deficit buffer, simple inventory model, monotone region, nth relaxation, optimal switching curve, processor sharing model, reflected random walk, affine enlargement, randomized stationary policy, fluid model equations, relative value function Key Phrases - Capitalized Phrases (CAPs): (learn more)
Monte Carlo, Comparison Theorem, Foster-Lyapunov Techniques, Strong Law of Large Numbers, Dominated Convergence Theorem, Optimization Proof, Kac's Theorem, Applying Proposition, Azuma's Inequality, Applying Theorem, Min-Cut Max-Flow, Demand Figure, Bellman-Gronwall Lemma, Arzelą-Ascoli Theorem, The Single Server Queue Proof, Markov Decision Process, Fatou's Lemma, Central Limit Theorem, Consider the Klimov, Matrix Inversion Lemma, Last Buffer-First Served, Los Angeles Browse Sample Pages:
Front Cover | Table of Contents | First Pages | Index | Surprise Me!
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