J. G. Dai

Processing Networks (eBook, PDF)

Fluid Models and Stability

• Format: PDF

Produktbeschreibung

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.

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Produktdetails

• Verlag: Cambridge University Press
• Erscheinungstermin: 15. Oktober 2020
• Englisch
• ISBN-13: 9781108805995
• Artikelnr.: 66181367

Autorenporträt

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.

Inhaltsangabe

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
Appendix A. Selected topics in real analysis
Appendix B. Selected topics in probability
Appendix C. Discrete-time Markov chains
Appendix D. Continuous-time Markov chains and phase-type distributions
Appendix E. Markovian arrival processes
Appendix F. Convergent square matrices.