Terms and Conditions:
Bruce Hajek wrote:Permission is hereby given to freely print and circulate copies of these notes so long as the notes are left intact and not reproduced for commercial purposes.
This is the latest draft of notes written for the graduate course Communication Network Analysis
, offered by the Department of Electrical and Computer Engineering
at the University of Illinois at Urbana-Champaign
. The notes describe many of the most popular analytical techniques for design and analysis of computer communication networks, with an emphasis on performance issues such as delay, blocking, and resource allocation. Topics that are not covered in the notes include the Internet protocols (at least not explicitly), simulation techniques and simulation packages, and some of the mathematical proofs. These are covered in other books and courses.
The topics of these notes form a basis for understanding the literature on performance issues in networks, including the Internet. Specific topics include:
- The basic and intermediate theory of queueing systems
, along with stability criteria based on drift analysis and fluid models
- The notion of effective bandwidth, in which a constant bit rate equivalent is given for a bursty data stream in a given context
- The use of penalty and barrier functions in optimization, and the natural extension to the use of utility functions and prices in the formulation of dynamic routing and congestion control problems
- Some topics related to performance analysis in wireless networks, including coverage of basic multiple access techniques, and transmission scheduling
- The basics of dynamic programming
, introduced in the context of a simple queueing control problem
- The analysis of blocking and the reduced load fixed point approximation for circuit switched networks.
Students are assumed to have already had a course on computer communication networks, although the material in such a course is more to provide motivation for the material in these notes, than to provide understanding of the mathematics. In addition, since probability is used extensively, students in the class are assumed to have previously had two courses in probability. Some prior exposure to the theory of Lagrange multipliers
for constrained optimization and nonlinear optimization algorithms is desirable, but not necessary.