Processing Networks

Companion webpage to the book "Processing Networks: Fluid Models and Stability". Copyright 2019 by Jim Dai and J. Michael Harrison.

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Jim Dai J. Michael Harrison
The Chinese University of University
of Hong Kong, Shenzhen
Stanford University
Cornell University  

This book has two purposes. First, it describes a broad class of mathematical system models, called stochastic processing networks (SPNs), that are useful as representations of service systems, industrial processes, and digital systems for computing and com-munication. And second, it develops a fluid model methodology for proving SPN stability, by which we mean proving positive recurrence of the Markov chain describing the SPN.

We will keep PDFs of this book freely available after publication.

Download the PDF of the book

Table of Contents

  1. Introduction
  2. Stochastic processing networks
  3. Markov representations
  4. Extensions and complements
  5. Is stability achievable?
  6. Fluid limits, fluid equations and positive recurrence
  7. Fluid equations that characterize specific policies
  8. Proving fluid model stability using Lyapunov functions
  9. Max-weight and back-pressure control
  10. Proportionally fair resource allocation
  11. Task allocation in server farms
  12. Multi-hop packet networks

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We submitted the final draft for copy-editing. Therefore, any issues you raise now may not make it into the printed version.