CSE567M: Computer Systems Analysis (Fall 2017)

Instructor: Prof. Raj Jain, jain@wustl.edu
Office: Jolley 208
Office Hours: Tuesday/Thursday: 11:00AM-12:00PM (By Appointment)

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.

Course Description:
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.

Credits:3 Units.

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.

Tentative Schedule:


ClassDayDateTopicChapter
1Tuesday8/29/2017Course Introduction
2Thursday8/31/2017Common MistakesChapter 2
Selection of Techniques and Metrics (Part 1)Chapter 3
3Tuesday9/5/2017Selection of Techniques and Metrics (Part 2)Chapter 3
Summarizing Measured Data (Part 1)Chapter 12
4Thursday9/7/2017Summarizing Measured Data (Part 2)Chapter 12
5Tuesday9/12/2017Summarizing Measured Data (Part 3)Chapter 12
Data for homework 12B (right-click and save link/target as)Chapter 13
Comparing Systems Using Random Data (Part 1)Chapter 13
6Thursday9/14/2017Comparing Systems Using Random Data (Part 2)Chapter 13
Data for Exercise 13.2 (right-click and save link/target as)Chapter 13
Data for Exercise 13.3 (right-click and save link/target as)Chapter 13
Simple Linear Regression Models (Part 1)Chapter 14
7Tuesday9/19/2017Simple Linear Regression Models (Part 2)Chapter 14
Data for Exercise 14.7 (right-click and save link/target as)Chapter 13
8Thursday9/21/2017Simple Linear Regression Models (Part 3)Chapter 14
Other Regression Models (Part 1)Chapter 15
9Tuesday9/26/2017Mid-Term Exam 1
10Thursday9/28/2017Other Regression Models (Part 2)Chapter 15
11Tuesday10/3/2017Other Regression Models (Part 3)Chapter 15
Project Guidelines (Part 1)Chapter 16
Experimental DesignsChapter 16
12Thursday10/5/20172**k Experimental DesignsChapter 17
Factorial Designs with Replication (Part 1)Chapter 18
13Tuesday10/10/2017Factorial Designs with Replication (Part 2)Chapter 18
Fractional Factorial DesignsChapter 19
14Thursday10/12/2017One Factor ExperimentsChapter 20
Two Factor Full Factorial Design w/o ReplicationsChapter 21
15Tuesday10/17/2017Two Factor Full Factorial Designs with ReplicationsChapter 22
16Thursday10/19/2017General Full Factorial DesignsChapter 23
17Tuesday10/24/2017Introduction to Queueing TheoryChapter 30
18Thursday10/26/2017Analysis of Single QueueChapter 31
19Tuesday10/31/2017Mid-Term Exam 2
20Thursday11/2/2017Queueing NetworksChapter 32
21Tuesday11/7/2017Operational LawsChapter 33
22Thursday11/9/2017Mean-Value Analysis Chapter 34
23Tuesday11/14/2017Time Series AnalysisChapter 37
24Thursday11/16/2017Heavy Tailed Distributions,Self-Similar Processes, and Long-Range DependenceChapter 38
25Tuesday11/21/2017Random Number GenerationChapter 26
Thursday11/23/2017Thanks Giving Break
26Tuesday11/28/2017Analysis of Simulation ResultsChapter 34
27Thursday11/30/2017Art of Data PresentationChapter 10
28Tuesday12/5/2017Clustering Techniques
29Thursday12/7/2017Final Exam

Grading:

Mid-Term Exam (Best of 2 Mid-Terms)30%
Final Exam30%
Class participation5%
Homeworks15%
Project20%
Class participation includes class attendence and class discussion.

Collaboration Policy: All students are exptected to do the homeworks by themselves. Group projects must be pre-approved by the instructor.


Complete List of Audio and Video Recording of Lectures by Raj Jain
Back to Raj Jain's Home Page