| 书目名称 | Stochastic Network Optimization with Application to Communication and Queueing Systems | | 编辑 | Michael J. Neely | | 视频video | | | 丛书名称 | Synthesis Lectures on Learning, Networks, and Algorithms | | 图书封面 |  | | 描述 | This text presents a modern theory of analysis, control, and optimization for dynamic networks. Mathematical techniques of Lyapunov drift and Lyapunov optimization are developed and shown to enable constrained optimization of time averages in general stochastic systems. The focus is on communication and queueing systems, including wireless networks with time-varying channels, mobility, and randomly arriving traffic. A simple drift-plus-penalty framework is used to optimize time averages such as throughput, throughput-utility, power, and distortion. Explicit performance-delay tradeoffs are provided to illustrate the cost of approaching optimality. This theory is also applicable to problems in operations research and economics, where energy-efficient and profit-maximizing decisions must be made without knowing the future. Topics in the text include the following: - Queue stability theory - Backpressure, max-weight, and virtual queue methods - Primal-dual methods for non-convex stochasticutility maximization - Universal scheduling theory for arbitrary sample paths - Approximate and randomized scheduling theory - Optimization of renewal systems and Markov decision systems Detailed exam | | 出版日期 | Book 2010 | | 版次 | 1 | | doi | https://doi.org/10.1007/978-3-031-79995-2 | | isbn_softcover | 978-3-031-79994-5 | | isbn_ebook | 978-3-031-79995-2Series ISSN 2690-4306 Series E-ISSN 2690-4314 | | issn_series | 2690-4306 | | copyright | Springer Nature Switzerland AG 2010 |
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发表于 2025-3-21 16:10:09
