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        ECE 299 - Discrete Event Simulation and Data Analysis with applications to 
                 Performance and Reliability of Computer Systems and Networks

			Spring Semester 2005




		
                This course will be entirely devoted to methodology, tools
and applications of discrete-event simulation to study performance,
reliability/availability of computer and networked systems. We will also study
statistical methods of data analysis including linear regression and time series analysis.
                  
Textbook:	
		1.Banks et al, Discrete-Event System Simulation, 3rd ed., Prentice-Hall, 2001.
Reference books:
		2.Law and Kelton, Simulation Modeling and Analysis, McGraw Hill, 2000.
                3.Devroye, Non-Uniform Random Variate Generation, Springer-Verlag, 1986.
                4.Trivedi, Prob. & Stat. with Rel., Que., and Comp. Sc. Appl., Wiley, 2001.
                5.Shumway and Stoffer, Time Series Analysis and Its Applications, 
                    Springer-Verlag, 2000. 
		6.Greene, Econometric Analysis, Prentice-Hall, 5th ed., 2003.
                7.Park and Willinger, Self-Similar Network Traffic and Performance
                    Evaluation, Wiley, 2000.
                8.CSIM 19 Simulator: http://www.mesquite.com
		9.The Network Simulator - ns-2: http://www.isi.edu/nsnam/ns/
                10.Marc Greis, Tutorial for the ns-2: http://www.isi.edu/nsman/ns/tutorial/
		11.Matlab, http://www.mathworks.com/
                
Coordinator: 	Kishor Trivedi, Professor of Electrical Engineering
		and Computer Science,

Goals:		This course is designed to give seniors and graduate students 
		an introduction of the discrete-event simulation including statistical
                input data analysis, random variate generation and output analysis; 
                simulation tools such as CSIM and ns-2 and applications to performance 
                and reliability analysis of computer and networked systems. 
		Statistical analysis of measuremeant data including statistical
                inference, hypothesis testing, linear regression 
                and time series analysis will be covered.


Topics:

		1. Introduction to simulation. (2 classes)
		2. Input Data Analysis: Inference. (3 classes)
		3. Output Data Analysis. (2 classes)
		4. Random Variate Generation. (2 classes)
		5. Simulation Tools. (2 classes)
		6. Applications. (10 classes)
		7. Time Series Analysis. (3 classes)
		8. Linear Regression. (3 classes)
		9. Model Validation. (1 class)
		10. Network Traffic Analysis. (5 classes)
		11. Examinations. (2 classes)

Computer usage:
		Several homework assignments which provide experience with 
		modeling methods and experience with modeling tools such as 
		CSIM and ns 2. A project on a major simulation assignment is
                also expected.

		ABET category content as estimated by faculty member 
		who prepared this course description:

        		Engineering Science: 75%
		        Engineering Design : 25%

Prerequisites:
Math 135 or equivalent