AIMD Dynamics and Distributed Resource Allocation
AIMD Dynamics and Distributed Resource Allocation
C. King, Northeastern University, Boston
R. Shorten, University College Dublin
F. Wirth, University of Passau
$93.00
USDThe AIMD algorithm is the most widely used method for allocating a limited resource among competing agents without centralised control. In this first comprehensive book on the topic, the authors offer a new approach based on positive switched linear systems, which is used to develop most of the main results in the book. Students and researchers alike will benefit from details of several variants of the algorithm, provided in order of increasing complexity, including deterministic, random, linear, and nonlinear versions. In each case, stability and convergence results are derived based on unifying principles. Basic and fundamental properties of the algorithm are described, examples are used to illustrate the richness of the resulting dynamical systems, and applications are provided to show how the algorithm can be used in the context of smart cities, intelligent transportation systems, and the smart grid.
- The first book giving a comprehensive overview of the AIMD algorithm
- Offers a new approach, based on positive switched linear systems, which is used to develop many of the main results
- Fundamental results on stochastic switched nonnegative and consensus systems are derived to develop the main results of the book
Product details
October 2016Paperback
9781611974218
250 pages
253 × 177 × 13 mm
0.47kg
This item is not supplied by Cambridge University Press in your region. Please contact Soc for Industrial & Applied Mathematics for availability.
Table of Contents
- List of figures
- Notation
- Preface
- 1. Origins and applications of AIMD
- Part I. Linear AIMD:
- 2. Synchronized homogeneous AIMD
- 3. Nonsynchronized Nonhomogenous AIMD
- 4. The IID AIMD model
- 5. Mathematical background for part I
- Part II. Stochastic Linear AIMD:
- 6. IID AIMD and ergodicity
- 7. AIMD with state-dependent transition probabilities
- 8. A Markov chain model for capacity events
- 9. Mathematical background for part II
- Part III. Nonlinear AIMD:
- 10. A primer on nonlinear AIMD
- 11. Synchronized homogeneous AIMD
- 12. Nonsynchronized nonhomogeneous NAIMD
- 13. Nonsynchronized algorithms with stochastic state-dependent growth rates
- Part IV. Applications of AIMD Algorithms:
- 14. Three sample industrial applications of AIMD
- 15. Another application: network utility optimization
- 16. Mathematical background for part IV
- Bibliography
- Index.
