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Thursday 14 October 2010

Communication Network Analysis

Communication Network Analysis

Preface
This is the latest draft of notes I have used 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
• An introduction to the calculus of deterministic constraints on traffic flows
• 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.
I’m grateful to students and colleagues for suggestions and corrections, and am always eager
for more. Bruce Hajek, December 2006
to download this course click here

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