J G Dai, J Michael Harrison

Processing Networks

Fluid Models and Stability

• Gebundenes Buch

Produktbeschreibung

The state of the art in fluid-based methods for stability analysis, giving researchers and graduate students command of the tools.

---

Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.

Produktdetails

• Verlag: Cambridge University Press
• Seitenzahl: 404
• Erscheinungstermin: 12. November 2020
• Englisch
• Abmessung: 232mm x 162mm x 23mm
• Gewicht: 726g
• ISBN-13: 9781108488891
• ISBN-10: 1108488897
• Artikelnr.: 59988302

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.