| 000 | 05089nam a2200757 i 4500 | ||
|---|---|---|---|
| 001 | 6813406 | ||
| 003 | IEEE | ||
| 005 | 20200413152859.0 | ||
| 006 | m eo d | ||
| 007 | cr cn |||m|||a | ||
| 008 | 101013s2010 caua foab 000 0 eng d | ||
| 020 | _a9781608454563 (electronic bk.) | ||
| 020 | _z9781608454556 (pbk.) | ||
| 024 | 7 |
_a10.2200/S00271ED1V01Y201006CNT007 _2doi |
|
| 035 | _a(CaBNVSL)gtp00544199 | ||
| 035 | _a(OCoLC)669066856 | ||
| 040 |
_aCaBNVSL _cCaBNVSL _dCaBNVSL |
||
| 050 | 4 |
_aT57.83 _b.N447 2010 |
|
| 082 | 0 | 4 |
_a519.703 _222 |
| 100 | 1 | _aNeely, Michael J. | |
| 245 | 1 | 0 |
_aStochastic network optimization with application to communication and queueing systems _h[electronic resource] / _cMichael J. Neely. |
| 260 |
_aSan Rafael, Calif. (1537 Fourth Street, San Rafael, CA 94901 USA) : _bMorgan & Claypool, _cc2010. |
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| 300 |
_a1 electronic text (xii, 199 p. : ill.) : _bdigital file. |
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| 490 | 1 |
_aSynthesis lectures on communication networks, _x1935-4193 ; _v# 7 |
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| 538 | _aMode of access: World Wide Web. | ||
| 538 | _aSystem requirements: Adobe Acrobat Reader. | ||
| 500 | _aPart of: Synthesis digital library of engineering and computer science. | ||
| 500 | _aSeries from website. | ||
| 504 | _aIncludes bibliographical references (p. 181-198). | ||
| 505 | 0 | _a1. 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 -- | |
| 505 | 0 | _a2. Introduction to queues -- Rate stability -- Stronger forms of stability -- Randomized scheduling for rate stability -- Exercises -- | |
| 505 | 0 | _a3. Dynamic scheduling example -- Scheduling for stability -- Stability and average power minimization -- Generalizations -- | |
| 505 | 0 | _a4. 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 -- | |
| 505 | 0 | _a5. 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 -- | |
| 505 | 0 | _a6. Approximate scheduling -- Time-invariant interference networks -- Multiplicative factor approximations -- | |
| 505 | 0 | _a7. 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 -- | |
| 505 | 0 | _a8. Conclusions -- Bibliography -- Author's biography. | |
| 506 | 1 | _aAbstract freely available; full-text restricted to subscribers or individual document purchasers. | |
| 510 | 0 | _aCompendex | |
| 510 | 0 | _aINSPEC | |
| 510 | 0 | _aGoogle scholar | |
| 510 | 0 | _aGoogle book search | |
| 520 | 3 | _aThis 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. | |
| 530 | _aAlso available in print. | ||
| 588 | _aTitle from PDF t.p. (viewed on October 13, 2010). | ||
| 650 | 0 | _aDynamic programming. | |
| 650 | 0 | _aQueuing networks (Data transmission) | |
| 650 | 0 | _aStochastic systems. | |
| 650 | 0 | _aLyapunov functions. | |
| 653 | _adynamic scheduling | ||
| 653 | _adecision theory | ||
| 653 | _awireless networks | ||
| 653 | _aLyapunov optimization | ||
| 653 | _acongestion control | ||
| 653 | _afairness | ||
| 653 | _anetwork utility maximization | ||
| 653 | _amulti-hop | ||
| 653 | _amobile networks | ||
| 653 | _arouting | ||
| 653 | _abackpressure | ||
| 653 | _amax-weight | ||
| 653 | _avirtual queues | ||
| 830 | 0 | _aSynthesis digital library of engineering and computer science. | |
| 830 | 0 |
_aSynthesis lectures on communication networks, _x1935-4193 ; _v# 7. |
|
| 856 | 4 | 2 |
_3Abstract with links to resource _uhttp://ieeexplore.ieee.org/servlet/opac?bknumber=6813406 |
| 999 |
_c561788 _d561788 |
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