Teaching Assistant: Maede Zolanvari (Office hours: Monday/Friday 1-2PM)
All question about the homeworks and mid-term exams 1 and 2 grading should be directed to TA.
Comparing systems using measurement, simulation, and queueing models. Common mistakes and how to avoid them, selection of techniques and metrics, art of data presentation, summarizing measured data, comparing systems using sample data, introduction to experimental design, fractional factorial designs, introduction to simulation, common mistakes in simulations, analysis of simulation results, random number generation, random variate generation, commonly used distributions, introduction to queueing theory, single queues, and queueing networks. The techniques of the course can be used to analyze and compare any type of systems including algorithms, protocols, network, or database systems. Students do a project involving application of these techniques to a problem of their interest.
Prerequisites: CSE 131 or CSE 126 or their respective equivalents. CSE 280 is not required. If you have any questions about the prerequisites, please feel free to see the instructor or discuss in the first session. Some knowledge of probability theory is helpful.
Time:Tuesday-Thursday 1:00PM-2:30PM, Lopata 101
Text Book: Raj Jain, "The Art of Computer Systems Performance Analysis: Techniques for Experimental Design, Measurement, Simulation, and Modeling," Wiley-Interscience, New York, NY, April 1991, ISBN:0471503363
Please download the errata for the book.
Audio/Video recordings of the previous offerings of this course are available at 2015, 2013, 2011, 2008 and 2006.
For Audio/Video recordings of individual lectures, click the class lecture below.
|2||Thursday||8/31/2017||Common Mistakes||Chapter 2|
|3||Tuesday||9/5/2017||Selection of Techniques and Metrics||Chapter 3|
|4||Thursday||9/7/2017||Summarizing Measured Data||Chapter 12|
|5||Tuesday||9/12/2017||Comparing Systems Using Random Data||Chapter 13|
|6||Thursday||9/14/2017||Simple Linear Regression Models||Chapter 14|
|7||Tuesday||9/19/2017||Other Regression Models||Chapter 15|
|8||Thursday||9/21/2017||Experimental Designs||Chapter 16|
|9||Tuesday||9/26/2017||Mid-Term Exam 1|
|10||Thursday||9/28/2017||2**k Experimental Designs||Chapter 17|
|11||Tuesday||10/3/2017||Factorial Designs with Replication||Chapter 18|
|12||Thursday||10/5/2017||Fractional Factorial Designs||Chapter 19|
|13||Tuesday||10/10/2017||One Factor Experiments||Chapter 20|
|14||Thursday||10/12/2017||Two Factor Full Factorial Design w/o Replications||Chapter 21|
|15||Tuesday||10/17/2017||Two Factor Full Factorial Designs with Replications||Chapter 22|
|16||Thursday||10/19/2017||General Full Factorial Designs||Chapter 23|
|17||Tuesday||10/24/2017||Introduction to Queueing Theory||Chapter 30|
|18||Thursday||10/26/2017||Analysis of Single Queue||Chapter 31|
|19||Tuesday||10/31/2017||Mid-Term Exam 2|
|20||Thursday||11/2/2017||Queueing Networks||Chapter 32|
|21||Tuesday||11/7/2017||Operational Laws||Chapter 33|
|22||Thursday||11/9/2017||Mean-Value Analysis||Chapter 34|
|23||Tuesday||11/14/2017||Time Series Analysis||Chapter 37|
|24||Thursday||11/16/2017||Heavy Tailed Distributions,Self-Similar Processes, and Long-Range Dependence||Chapter 38|
|25||Tuesday||11/21/2017||Random Number Generation||Chapter 26|
|Thursday||11/23/2017||Thanks Giving Break|
|26||Tuesday||11/28/2017||Analysis of Simulation Results||Chapter 34|
|27||Thursday||11/30/2017||Art of Data Presentation||Chapter 10|
|Mid-Term Exam (Best of 2 Mid-Terms)||30%|
Collaboration Policy: All students are exptected to do the homeworks by themselves. Group projects must be pre-approved by the instructor.