Spring 2014 - EE 381K
Analysis & Design of Communication Networks
Meets MW 1:30-3pm in ENS 116
Unique No: 17240
Gustavo de Veciana
Office: ACES 3.120
Office Hours: may change, so see my web page
We introduce analytical tools need to construct and analyze models for
for communication and computer systems. The focus of the course
will be on discussing tools from queueing theory, optimization,
and control, as they apply to evaluating the the performance of
various types of systems. The course is intended to build upon an
introductory graduate course on probability and stochastic processes
and one on communication networks (i.e., EE 381J and EE382N ).
Course Topics (Will cover as many as I can)
Review Discrete Time Markov Chains:
Definition: construction, Markov property, stopping times and strong Markov property
Classification of states: recurrence, transience, positive recurrence and null recurrence, periodic.
Stationary distributions: Existence, uniqueness and convergence to a stationary distribution. .
Positive recurrence and stability: Lyapunov functions to show positive recurrence- Foster's criterion.
Continuous Time Markov Chains and Queues:
Counting processes, Poisson processes equivalent definitions and their properties. Nonhomogenous Poisson Point processes;
Continuous-Time Markov Chains: global balance equations, time-reversibility, detailed balance, Kelly's Lemma;
Examples: birth-death Markov processes, M/M/1 queues, Burke's Theorem, Jackson Networks;
Basic queueing: models and notation. Little's Result.
Queueing Network Models for Packet Switched Networks:
Kleinrock's Assumptions, routing optimization and optimal capacity allocation
Queues and Queueing Networks:
Open queueing networks and Extending Product-form Results: Quasireversible queues, insensitivity and Multiclass random routing;
Little's Result revisited: Priority queueing systems, Pollazeck-Khinchin Formula;
Closed queueing networks;
Truncation of time reversible Markov Chains;
Assorted topics: Arrival Theorem, ratio of rates formula, Mean Value analysis and sojourn times distributions in networks.
Loss Network Models for Circuit Switched Networks:
Kaufman recursion, large capacity limits; Erlang fixed point approximations;
Routing optimization;. Alternative routing and metastability, trunk reservation
regenerative simulation; simulated annealing and reversible Markov chains;
M/GI/1 queues; Lindley process and stability; Stochastic orderings and performance comparisons
large deviations and their use to estimate overflow probabilties;
Rate and large buffer multiplexing and effective bandwidths.
Resource allocation and utility maximization. max-min fair and other forms of fairness, TCP modeling, mice and elephants
Stochastic network models: stability and performance, balanced fair allocations
Topics in Scheduling:
Web Server load Balancing and scheduling: Priority, SRPT, SITA, fat tailed distributions.
Wireless Base Station Scheduling: opportunistic scheduling, max weight rules
This course is intended for graduate students.
EE381J Probability and Stochastic Processes or equivalent is a prerequisite for the course.
You should also have some background in telecommunication
networks, e.g., an undergraduate course on this topic and/or
EE382N Communication Networks.
If you dont have these
prerequisites, you are encouraged to take these first, but may seek permission
from the instructor.
Course Web site and Text
- I will be using my own notes for this course and will
point you to appropriate other resources
on the relevant material as I go along.
Some pointers to other free texts available on the web
can be found at the course web site in the Blackboard system (courses.utexas.edu).
A partial set of notes, papers and homework assiginments
will be posted as we go along at this web site.
Two new textbooks in this area are great. I got these too late this term to choose one, but I recommend both of them!
- Performance Modeling and Design of Computer Systems: Queueing Theory in Action by
Mor Harchol-Balter, Cambridge, 2013
- Communication Networks: An optimization control and stochastic networks perspective by
R. Srikant and L. Ying, Cambridge, 2013
- Multiservice Loss Models for Broadband Telecommunication Networks, by Keith Ross, Springer Verlag, 1995
- Reversibility and Stochastic Networks, by F. Kelly, J. Wiley, 1979 (can be downloaded from the web)
- High Performance Communication Networks by P. Variaya and J. Walrand, Morgan Kaufman 1996. (Particularly Chapters 6,7)
Grading Policy and Exam Schedule
Homeworks will be assigned on a weekly basis and typically due Wednesday
though this will depend on how we are doing in class.
You must make an individual effort on your homework.
As part of this course you will be required to grade and produce solutions to one
of the homework sets.
The grading policy is as follows:
30% of your grade will be based on homeworks;
5% will be based on class participation or office hours interactions you have with me;
and 60% will be based on your midterm and final (30% and 35% each).
All exams will be closed, i.e., no cheet sheets, notes or books.
The schedule for the exams is as follows:
- Midterm Exam : March
- Final Exam: Friday May 7, 9:00 am to 12:00 noon
Where does this course fit in?
In conjunction with this course, or in the sequel you might consider taking:
Evaluation, Stochastic Networks, as well as related courses on Digital
Signal Processing; and/or Digital
Communications and Optimization of Engineering Systems .
You might consider taking Wireless Communications; Advanced Signal
Processing; and/or Information
Note: All departmental, college and university regulations
concerning drops will be followed. The University of Texas at Austin provides
upon request appropriate academic adjustments for qualified students with
disabilities. For more information, contact the Office of the Dean of Students
at 471-6259, 471-4241 TDD or the College of Engineering Director of Students
with Disabilities at 471-4382.