復(fù)雜網(wǎng)絡(luò)控制技術(shù)

內(nèi)容概要

電力網(wǎng)絡(luò)、柔性制造、移動通信等等互連都形成了復(fù)雜網(wǎng)絡(luò)。本書著眼于復(fù)雜網(wǎng)絡(luò)系統(tǒng)中的共性問題，綜合了作者多年來在該方向深入、系統(tǒng)的研究成果，給出了建立網(wǎng)絡(luò)模型所需要的工具和哲學(xué)思想，詳細(xì)具體地把握了其動力學(xué)本質(zhì)，同時簡明地揭示了有效控制的解決方案及其分析。本書內(nèi)容分為三個部分：第一部分為建模與控制，第二部分為負(fù)荷調(diào)度，第三部分為穩(wěn)定性及性能分析。    本書內(nèi)容循序漸進(jìn)，每章均附有習(xí)題并提供習(xí)題解答?；A(chǔ)章節(jié)部分要求讀者具有隨機過程和線性代數(shù)知識，適用于信息類、電子類等專業(yè)高年級本科生，高級部分則適用于研究生、研究人員和從業(yè)者。

作者簡介

Sean Meyn，伊利諾斯大學(xué)電子與計算機工程系教授，IEEE Fellow。擔(dān)任系統(tǒng)與控制、應(yīng)用概率等領(lǐng)域多個期刊的編委。與他人合著的圖書Markov Chains and Stochastic Stability獲1994年ORSA／TIMS最佳著作獎。在MIT4 UTRC等世界各地多個大學(xué)擔(dān)任客座教授。他的研究興趣包括隨機過程、最優(yōu)化、復(fù)雜網(wǎng)絡(luò)以及信息論等。

書籍目錄

List of IllustrationsPrefaceDedication1  Introduction  1.1  Networks in practice  1.2  Mathematical models  1.3  What do you need to know to read this book?  1.4  NotesPart I: Modeling and Control2  Examples  2.1  Modeling the single server queue  2.2  Klimov model  2.3  Capacity and queueing in communication systems  2.4  Multiple-access communication  2.5  Processor sharing model  2.6  Inventory model  2.7  Power transmission network  2.8  Optimization in a simple re-entrant line  2.9  Contention for resources and instability  2.10 Routing model  2.11  Braess' paradox  2.12 Notes3  The Single Server Queue  3.1  Representations  3.2  Approximations  3.3  Stability  3.4  Invariance equations  3.5  Big queues  3.6  Model selection  3.7  Notes  Exercises4  Scheduling  4.1  Controlled random-walk model  4.2  Fluid model  4.3  Control techniques for the fluid model  4.4  Comparing fluid and stochastic models  4.5  Structure of optimal policies  4.6  Safety-stocks  4.7  Discrete review  4.8  MaxWeight and MinDrift  4.9  Perturbed value function  4.10 Notes  Exercises~Part II: Workload5  Workload and Scheduling  5.1  Single server queue  5.2  Workload for the CRW scheduling model  5.3  Relaxations for the fluid model  5.4  Stochastic workload models  5.5  Pathwise optimality and workload  5.6  Hedging in networks  5.7  Notes  Exercises6  Routing and Resource Pooling  6.1  Workload in general models  6.2  Resource pooling  6.3  Routing and workload  6.4  MaxWeight for routing and scheduling  6.5  Simultaneous resource possession  6.6  Workload relaxations  6.7  Relaxations and policy synthesis for stochastic models  6.8  Notes  Exercises7  Demand  7.1  Network models  7.2  Transients  7.3  Workload relaxations  7.4  Hedging in a simple inventory model  7.5  Hedging in networks  7.6  Summary of steady-state control techniques  7.7  Notes  ExercisesPart III: Stability and Performance8  Foster-Lyapunov Techniques  8.1  Lyapunov functions  8.2  Lyapunov functions for networks  8.3  Discrete review  8.4  MaxWeight  8.5  MaxWeight and the average-cost optimality equation  8.6  Linear programs for performance bounds  8.7  Brownian workload model  8.8  Notes  Exercises9  Optimization  9.1  Reachability and decomposibility  9.2  Linear programming formulations  9.3  Multiobjective optimization  9.4  Optimality equations  9.5  Algorithms  9.6  Optimization in networks  9.7  One-dimensional inventory model  9.8  Hedging and workload  9.9  Notes  Exercises10  ODE Methods  10.1  Examples  10.2  Mathematical preliminaries  10.3  Fluid limit model  10.4  Fluid-scale stability  10.5  Safety stocks and trajectory tracking  10.6  Fluid-scale asymptotic optimality  10.7  Brownian workload model  10.8  Notes  Exercises11  Simulation and Learning  11.1  Deciding when to stop  11.2  Asymptotic theory for Markov models  11.3  The single-server queue  11.4  Control variates and shadow functions  11.5  Estimating a value function  11.6  Notes  ExercisesAppendix  Markov ModelsBibliographyIndex

章節(jié)摘錄

插圖：More history on Brownian models is contained in the Notes for Chapter 5.   This book provides foundations for resource allocation and perfonnance evaluation, but can- not go too deeply into specific issues in each possible application. A notable example is the area of Internet congestion control where there are many constraints due to the reliance on ar- chitecture and algorithms designed in the 1970s. Srikant's monograph [459] treats this problem in-depth using a range of techniques, including variants of methods described in this book.   Although much of this book concerns the construction and analysis of algorithms to con- struct feedback laws for control, to bound performance, or to improve simulation, this book does not contain any theory of algorithms. In particular, we do not touch upon complexity the- ory for algorithms as described in [390, 391,392, 115, 42, 194], although this theory is the most important motivation for the approximation techniques developed in the book.   The optimal control problems posed in this book are primarily centralized in the sense that there is a centralized decision maker that possesses complete information. A decentralized con- tro[ solution is one that can be implemented based on local information, such as nearby con- gested links.   For a physical network such as the Internet, or the North American power grid, a centralized control framework is absurd. For example, in a power distribution system generators may be owned by different companies, who supply power to various utilities, using power lines man- aged by different system operators. Methods from game theory can be applied to study the consequences of potential outcomes in a decentralized noncooperative setting [31,412]. We do not address any of these game-theoretic issues. However, the centralized optimal policy can be used as a benchmark against which the performance of a decentralized system is evaluated.Moreover, we do consider classes of policies that can be implemented using only local in- formation. One example is the class of MaxWeight policies introduced in Section 4.8. These are a subset of myopic: policies. In some cases it can be shown that a myopic policy is approx- imately optimal if the network is congested, or the network load is high （see Chapter 9 and Theorem 10.0.2）.

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