Stochastic network optimization with application to communication and queueing systems [electronic resource] / Michael J. Neely.

Mode of access: World Wide Web.

System requirements: Adobe Acrobat Reader.

Part of: Synthesis digital library of engineering and computer science.

Series from website.

Includes bibliographical references (p. 181-198).

1. Introduction -- Example opportunistic scheduling problem -- General stochastic optimization problems -- Lyapunov drift and Lyapunov optimization -- Differences from our earlier text -- Alternative approaches -- On general Markov decision problems -- On network delay -- Preliminaries --

2. Introduction to queues -- Rate stability -- Stronger forms of stability -- Randomized scheduling for rate stability -- Exercises --

3. Dynamic scheduling example -- Scheduling for stability -- Stability and average power minimization -- Generalizations --

4. Optimizing time averages -- Lyapunov drift and Lyapunov optimization -- General system model -- Optimality via [omega]-only policies -- Virtual queues -- The min drift-plus-penalty algorithm -- Examples -- Variable V algorithms -- Place-holder backlog -- Non-i.i.d. models and universal scheduling -- Exercises -- Appendix 4.A, proving theorem 4.5 --

5. Optimizing functions of time averages -- Solving the transformed problem -- A flow-based network model -- Multi-hop queueing networks -- General optimization of convex functions of time averages -- Non-convex stochastic optimization -- Worst case delay -- Alternative fairness metrics -- Exercises --

6. Approximate scheduling -- Time-invariant interference networks -- Multiplicative factor approximations --

7. Optimization of renewal systems -- The renewal system model -- Drift-plus-penalty for renewal systems -- Minimizing the drift-plus-penalty ratio -- Task processing example -- Utility optimization for renewal systems -- Dynamic programming examples -- Exercises --

8. Conclusions -- Bibliography -- Author's biography.

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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.

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Title from PDF t.p. (viewed on October 13, 2010).