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Processing networks :fluid models and stability /

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Processing networks :J. G. Dai, J. Michael Harrison.
Reminder of title:	fluid models and stability /
Author:	Dai, J. G.
other author:	Harrison, J. Michael,
Published:	Cambridge ;Cambridge University Press,2020.
Description:	xx, 384 p. :ill. (some col.) ;24 cm.
Subject:	Stochastic processes.
ISBN:	9781108488891 :

Processing networks :fluid models and stability /
Dai, J. G.1962-

Processing networks :fluid models and stability /J. G. Dai, J. Michael Harrison. - Cambridge ;Cambridge University Press,2020. - xx, 384 p. :ill. (some col.) ;24 cm.

Includes bibliographical references and index.

Stochastic processing networks -- Markov representations -- Extensions and complements -- Is stability achievable? -- Fluid limits, fluid equations and positive recurrence -- Fluid equations that characterize specific policies -- Proving fluid model stability using Lyapunov functions -- Max-weight and back-pressure control -- Proportionally fair resource allocation -- Task allocation in server farms -- Multi-hop packet networks.

"This state-of-the-art account unifies material developed in journal articles over the last 35 years, with two central thrusts : It describes a broad class of system models that the authors call "stochastic processing networks" (SPNs), which include queueing networks and bandwidth sharing networks as prominent special cases; and in that context it explains and illustrates a method for stability analysis based on fluid models. The central mathematical result is a theorem that can be paraphrased as follows: If the fluid model derived from an SPN is stable, then the SPN itself is stable. Two topics discussed in detail are (a) the derivation of fluid models by means of fluid limit analysis, and (b) stability analysis for fluid models using Lyapunov functions. With regard to applications, there are chapters devoted to max-weight and back-pressure control, proportionally fair resource allocation, data center operations, and flow management in packet networks. Geared toward researchers and graduate students in engineering and applied mathematics, especially in electrical engineering and computer science, this compact text gives readers full command of the methods"--

ISBN: 9781108488891 :NT$1874

LCCN: 2020012552Subjects--Topical Terms:

181874
Stochastic processes.

LC Class. No.: QA274.A1 / D35 2020

Dewey Class. No.: 519.2/3

Processing networks :fluid models and stability /
LDR:02552cam a2200289 a 4500 001 676206
003 OCoLC
005 20250620022454.0
008 250827s2020 enka b 001 0 eng c
010 $a 2020012552
019 $a 1191235518
020 $a 9781108488891 : $c NT$1874
020 $a 1108488897
020 $z 9781108772662 (epub)
020 $z 9781108805995 (ebook)
035 $a (OCoLC)1159648913 $z (OCoLC)1191235518
035 $a on1159648913
040 $a LBSOR/DLC $b eng $c DLC $d OCLCF $d OCLCO $d YDX $d UKMGB $d OCLCO $d OCLCL
042 $a pcc
050 0 0 $a QA274.A1 $b D35 2020
082 0 0 $a 519.2/3 $2 23
100 1 $a Dai, J. G. $q (Jiangang), $d 1962- $e author. $3 989931
245 1 0 $a Processing networks : $b fluid models and stability / $c J. G. Dai, J. Michael Harrison.
260 $a Cambridge ; $a New York, NY : $b Cambridge University Press, $c 2020.
300 $a xx, 384 p. : $b ill. (some col.) ; $c 24 cm.
504 $a Includes bibliographical references and index.
505 0 $a Stochastic processing networks -- Markov representations -- Extensions and complements -- Is stability achievable? -- Fluid limits, fluid equations and positive recurrence -- Fluid equations that characterize specific policies -- Proving fluid model stability using Lyapunov functions -- Max-weight and back-pressure control -- Proportionally fair resource allocation -- Task allocation in server farms -- Multi-hop packet networks.
520 $a "This state-of-the-art account unifies material developed in journal articles over the last 35 years, with two central thrusts : It describes a broad class of system models that the authors call "stochastic processing networks" (SPNs), which include queueing networks and bandwidth sharing networks as prominent special cases; and in that context it explains and illustrates a method for stability analysis based on fluid models. The central mathematical result is a theorem that can be paraphrased as follows: If the fluid model derived from an SPN is stable, then the SPN itself is stable. Two topics discussed in detail are (a) the derivation of fluid models by means of fluid limit analysis, and (b) stability analysis for fluid models using Lyapunov functions. With regard to applications, there are chapters devoted to max-weight and back-pressure control, proportionally fair resource allocation, data center operations, and flow management in packet networks. Geared toward researchers and graduate students in engineering and applied mathematics, especially in electrical engineering and computer science, this compact text gives readers full command of the methods"-- $c Provided by publisher.
650 0 $a Stochastic processes. $3 181874
650 0 $a Linear programming. $3 185299
650 0 $a Queuing networks (Data transmission) $v Congresses. $3 446900
650 0 $a Fluid dynamics. $3 186085
650 0 $a Stability. $3 191051
700 1 $a Harrison, J. Michael, $d 1944- $3 709828
776 0 8 $i ebook version : $z 9781108805995