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001 978-3-031-79275-5
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008 220601s2017 sz | s |||| 0|eng d
020 _a9783031792755
_9978-3-031-79275-5
024 7 _a10.1007/978-3-031-79275-5
_2doi
050 4 _aQ334-342
050 4 _aTA347.A78
072 7 _aUYQ
_2bicssc
072 7 _aCOM004000
_2bisacsh
072 7 _aUYQ
_2thema
082 0 4 _a006.3
_223
100 1 _aLow, Steven H.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_981699
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.
300 _aXX, 193 p.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aSynthesis Lectures on Learning, Networks, and Algorithms,
_x2690-4314
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.
650 0 _aArtificial intelligence.
_93407
650 0 _aCooperating objects (Computer systems).
_96195
650 0 _aProgramming languages (Electronic computers).
_97503
650 0 _aTelecommunication.
_910437
650 1 4 _aArtificial Intelligence.
_93407
650 2 4 _aCyber-Physical Systems.
_932475
650 2 4 _aProgramming Language.
_939403
650 2 4 _aCommunications Engineering, Networks.
_931570
710 2 _aSpringerLink (Online service)
_981700
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783031792748
776 0 8 _iPrinted edition:
_z9783031792762
830 0 _aSynthesis Lectures on Learning, Networks, and Algorithms,
_x2690-4314
_981701
856 4 0 _uhttps://doi.org/10.1007/978-3-031-79275-5
912 _aZDB-2-SXSC
942 _cEBK
999 _c85227
_d85227