Processing Networks

Fluid Models and Stability

Processing Networks
J G Dai, J Michael Harrison
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NZ$ 91.95
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Hardback
h234 x 156mm - 404pg
15 Oct 2020 UK
International import eta 7-19 days
9781108488891
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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.
' The deep and rich theory of stochastic processing networks has served as the analytical foundation for the study of communication networks, cloud computing systems, and manufacturing networks. This book by two of the pioneers of the theory presents an authoritative and comprehensive treatment of the topic, and will serve as an important reference to researchers in the area. ' R. Srikant, University of Illinois at Urbana-Champaign
Jim Dai received his PhD in mathematics from Stanford University. He is currently Presidential Chair Professor in the Institute for Data and Decision Analytics at The Chinese University of Hong Kong, Shenzhen. He is also the Leon C. Welch Professor of Engineering in the School of Operations Research and Information Engineering at Cornell University. He was honored by the Applied Probability Society of INFORMS with its Erlang Prize (1998) and with two Best Publication Awards (1997 and 2017). In 2018 he received The Achievement Award from ACM SIGMETRICS. Professor Dai served as Editor-In-Chief of Mathematics of Operations Research from 2012 to 2018. J. Michael Harrison earned degrees in industrial engineering and operations research before joining the faculty of Stanford University' s Graduate School of Business, where he served for 43 years. His research concerns stochastic models in business and engineering, including mathematical finance and processing network theory. His previous books include Brownian Models of Performance and Control (2013). Professor Harrison has been honored by INFORMS with its Expository Writing Award (1998), the Lanchester Prize for best research publication (2001), and the John von Neumann Theory Prize (2004); he was elected to the U. S. National Academy of Engineering in 2008.
Status: Active
ISBN-13: 9781108488891
Published: 15 Oct 2020
Published In: United Kingdom
Imprint: Cambridge University Press
Publisher: Cambridge University Press
Format: Hardback
Height: 234mm
Width: 156mm
Spine: 23mm
Weight: 730g
Pages: 404

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