000 | 04075nam a22005415i 4500 | ||
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001 | 978-3-031-79275-5 | ||
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007 | cr nn 008mamaa | ||
008 | 220601s2017 sz | s |||| 0|eng d | ||
020 |
_a9783031792755 _9978-3-031-79275-5 |
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024 | 7 |
_a10.1007/978-3-031-79275-5 _2doi |
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050 | 4 | _aQ334-342 | |
050 | 4 | _aTA347.A78 | |
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_aUYQ _2bicssc |
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_aUYQ _2thema |
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_a006.3 _223 |
100 | 1 |
_aLow, Steven H. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut _981699 |
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245 | 1 | 0 |
_aAnalytical Methods for Network Congestion Control _h[electronic resource] / _cby Steven H. Low. |
250 | _a1st ed. 2017. | ||
264 | 1 |
_aCham : _bSpringer International Publishing : _bImprint: Springer, _c2017. |
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300 |
_aXX, 193 p. _bonline resource. |
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336 |
_atext _btxt _2rdacontent |
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337 |
_acomputer _bc _2rdamedia |
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338 |
_aonline resource _bcr _2rdacarrier |
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347 |
_atext file _bPDF _2rda |
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490 | 1 |
_aSynthesis Lectures on Learning, Networks, and Algorithms, _x2690-4314 |
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505 | 0 | _aPreface -- Acknowledgments -- Notations -- Congestion Control Models -- Equilibrium Structure -- Global Stability: Lyapunov Method -- Global Stability: Passivity Method -- Global Stability: Gradient Projection Method -- Local Stability with Delay -- Bibliography -- Author's Biography. | |
520 | _aThe congestion control mechanism has been responsible for maintaining stability as the Internet scaled up by many orders of magnitude in size, speed, traffic volume, coverage, and complexity over the last three decades. In this book, we develop a coherent theory of congestion control from the ground up to help understand and design these algorithms. We model network traffic as fluids that flow from sources to destinations and model congestion control algorithms as feedback dynamical systems. We show that the model is well defined. We characterize its equilibrium points and prove their stability. We will use several real protocols for illustration but the emphasis will be on various mathematical techniques for algorithm analysis. Specifically we are interested in four questions: 1. How are congestion control algorithms modelled? 2. Are the models well defined? 3. How are the equilibrium points of a congestion control model characterized? 4. How are the stability of these equilibrium points analyzed? For each topic, we first present analytical tools, from convex optimization, to control and dynamical systems, Lyapunov and Nyquist stability theorems, and to projection and contraction theorems. We then apply these basic tools to congestion control algorithms and rigorously prove their equilibrium and stability properties. A notable feature of this book is the careful treatment of projected dynamics that introduces discontinuity in our differential equations. Even though our development is carried out in the context of congestion control, the set of system theoretic tools employed and the process of understanding a physical system, building mathematical models, and analyzing these models for insights have a much wider applicability than to congestion control. | ||
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_aArtificial intelligence. _93407 |
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_aCooperating objects (Computer systems). _96195 |
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_aProgramming languages (Electronic computers). _97503 |
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_aTelecommunication. _910437 |
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650 | 1 | 4 |
_aArtificial Intelligence. _93407 |
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_aCyber-Physical Systems. _932475 |
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_aProgramming Language. _939403 |
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_aCommunications Engineering, Networks. _931570 |
710 | 2 |
_aSpringerLink (Online service) _981700 |
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773 | 0 | _tSpringer Nature eBook | |
776 | 0 | 8 |
_iPrinted edition: _z9783031792748 |
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_iPrinted edition: _z9783031792762 |
830 | 0 |
_aSynthesis Lectures on Learning, Networks, and Algorithms, _x2690-4314 _981701 |
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856 | 4 | 0 | _uhttps://doi.org/10.1007/978-3-031-79275-5 |
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