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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.

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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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