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Advances in Design and Control Ser.: AIMD Dynamics and Distributed Resource Allocation by R. Shorten, C. King, F. Wirth and M. Corless (2016, Trade Paperback)
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The authors cover the origins and application of AIMD, Linear AIMD, stochastic linear AIMD, nonlinear AIMD, and the advanced applications of AIMD algorithms over th’s sixteen chapters. Annotation ©2016 Ringgold, Inc., Portland, OR ().
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About this product
Product Identifiers
PublisherSociety for Industrial AND Applied Mathematics
ISBN-101611974216
ISBN-139781611974218
eBay Product ID (ePID)219632556
Product Key Features
Number of Pages249 Pages
LanguageEnglish
Publication NameAimd Dynamics and Distributed Resource Allocation
Publication Year2016
SubjectProbability & Statistics / Stochastic Processes, General, Algebra / General
TypeTextbook
Subject AreaMathematics
AuthorR. Shorten, C. King, F. Wirth, M. Corless
SeriesAdvances in Design and Control Ser.
FormatTrade Paperback
Dimensions
Item Height0.5 in
Item Weight16.8 Oz
Item Length9.9 in
Item Width7 in
Additional Product Features
Intended AudienceScholarly & Professional
LCCN2015-038343
Dewey Edition23
Series Volume Number29
IllustratedYes
Dewey Decimal519.8/2
Table Of ContentChapter 1: Origins and Applications of AIMD Part I: Linear AIMD Chapter 2: Synchronized Homogeneous AIMD Chapter 3: Nonsynchronized Nonhomogeneous AIMD Chapter 4: The IID AIMD Model Chapter 5: Mathematical Background for Part I Part II: Stochastic Linear AIMD;Chapter 6: IID AIMD and Ergodicity Chapter 7: AIMD with State-Dependent Transition Probabilities Chapter 8: A Markov Chain Model for Capacity Events Chapter 9: Mathematical Background for Part II Part III: Nonlinear AIMD Chapter 10: A Primer on Nonlinear AIMD Chapter 11: Synchronized Homogeneous Nonlinear AIMD Chapter 12: Nonsynchronized Nonhomogeneous NAIMD Chapter 13: Nonsynchronized Algorithms with Stochastic State-Dependent Growth Rates Part IV: Applications of AIMD Algorithms Chapter 14: Three Sample Applications of AIMD Chapter 15: Another Application: Network Utility Optimization Chapter 16: Mathematical Background for Part IV Bibliography Index.
SynopsisThe first comprehensive book on the AIMD algorithm. The authors offer a new approach that is based on positive switched linear systems. It is used to develop most of the main results found in the book, and fundamental results on stochastic switched nonnegative and consensus systems are derived to obtain these results., The AIMD algorithm is the most widely used method for allocating a limited resource among competing agents without centralised control. In this first comprehensive book on the topic, the authors offer a new approach based on positive switched linear systems, which is used to develop most of the main results in the book. Students and researchers alike will benefit from details of several variants of the algorithm, provided in order of increasing complexity, including deterministic, random, linear, and nonlinear versions. In each case, stability and convergence results are derived based on unifying principles. Basic and fundamental properties of the algorithm are described, examples are used to illustrate the richness of the resulting dynamical systems, and applications are provided to show how the algorithm can be used in the context of smart cities, intelligent transportation systems, and the smart grid., This is the first comprehensive book on the AIMD algorithm, the most widely used method for allocating a limited resource among competing agents without centralized control. The authors offer a new approach that is based on positive switched linear systems. It is used to develop most of the main results found in the book, and fundamental results on stochastic switched nonnegative and consensus systems are derived to obtain these results. The original and best known application of the algorithm is in the context of congestion control and resource allocation on the Internet, and readers will find details of several variants of the algorithm in order of increasing complexity, including deterministic, random, linear, and nonlinear versions. In each case, stability and convergence results are derived based on unifying principles. Basic and fundamental properties of the algorithm are described, examples are used to illustrate the richness of the resulting dynamical systems, and applications are provided to show how the algorithm can be used in the context of smart cities, intelligent transportation systems, and the smart grid.
LC Classification NumberT57.9.C675 2016
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