ECE/CS 541:
Computer Systems Analysis

Fall 2026
Time and Location 9:30-10:45 a.m. on Tuesdays and Thursdays, 2013 ECEB 
Instructor

Prof. David Nicol
(dmnicol AT illinois.edu)
451 Coordinated Science Laboratory
Teaching Assistant

Patrick Marschoun
(pmm6 AT illinois.edu)
448 Coordinated Science Laboratory
Office Hours

Prof. David M. Nicol

Tuesdays and Fridays, 11:00 a.m. - 12:00 p.m, and by appointment, in CSL 451

Patrick Marschoun

Mondays 1:00 - 2:00 p.m, Wednesdays 12:00 a.m. - 1:00 p.m, and by appointment, in CSL 445
Canvas https://canvas.illinois.edu/courses/74907
Software https://github.com/ProfessorNicol/541_tools
Exams

Midterm 1: September 17 in class

Midterm 2: October 22 in class

Final: Tentatively on December 11 from 1:30 - 4:30 pm
 

Course Schedule

Date Activity Notes
Aug 25 Lecture 1 [slides] Introduction
Aug 27 Lecture 2 [slides] Probability Review
Sept 1 Lecture 3 [slides]

Analysis Formalisms
Sept 3 Problem Solving [p-set] [solutions] Quiz and problems from Lectures 1-3
Sept 8 Lecture 4 [slides] Combinatorial Models, Simulation
Sept 10 Problem Solving [p-set][solutions] Quiz and problems from Lecture 4
Sept 15 Midterm 1 Review [practice-exam] Questions answered on material from lectures 1-4, in-class and/or submitted on-line
Sept 17 Midterm 1 In class, close book, closed devices,  2 pages of notes permitted for reference
Sept 22 Lecture 5 Markov process (DTMC formulation)
Sept 24 Problem Solving Quiz and Problems from Lecture 5
Sept 29 Lecture 6 Solution of DTMC models
Oct 1 Review of Midterm 1 Return exams, work through correct solutions
Oct 6 Lecture 7 Introduction to Continuous Time Markov Chains
Oct 8 Lecture 8 Solution Techniques for CTMC models
Oct 13 Lecture 9 Building Large CTMC models
Oct 15 Problem Solving Quiz and problems from Lectures 7-9
Oct 20 Midterm 2 Review Questions answered on material from lectures 5-9, in-class and/or submitted on-line
Oct 22 Midterm 2 In class, close book, closed devices,  2 pages of notes permitted for reference
Oct 27 Lecture 10 Introduction to Discrete Event Simulation
Oct 29 Problem Solving Quiz and problems from Lecture 10
Nov 3 Lecture 11 Output Analysis
Nov 5 Review of Midterm 2 Return exams, work through correct solutions

Nov 10

 Lecture 12 Introduction to Design of Experiments
Nov 12 Problem Solving Quiz and problems from Lectures 11-12
Nov 17 Lecture 13 Design of Experiments Development
Nov 19 Problem Solving Quiz and problems from Lecture 13
Nov 24 Fall Break  
Nov 26 Fall Break  
Dec 1 Project Presentations 1/2 of the groups make presentations to the class on their projects
Dec 3 Project Presentations 1/2 of the groups make presentations to the class on their projects
Dec 7 Review for Final Questions answered on material from Lectures 1-13, in-class and/or submitted on-line
Dec 11 Final Current schedule 1:30-4:30 pm.   May change, will be settled by Oct. 14
 

Assignments & Projects
Group Project TBA

 

COURSE DESCRIPTION:

Text

No single textbook required.   Each topic is extensively covered on-line, use provided lecture slides as guides.   Lecture slides will be posted on Canvas at least one day before the class in which it is presented.

Prerequisites

ECE 313 or equivalent.

Overview

Development of analytic and simulation models of computer systems and application of such models to system analysis: latency, bandwidth, availability, reliability, security. The intent of the course is to prepare students to develop analyses of computer systems in application domains where they may work or perform research.   The course focuses on methodologies, tools, and data structures required to perform such analyses. There is an emphasis on working through case studies that highlight different modeling and analysis techniques. 

The course will emphasize hands-on in-class problem solving.  Most weeks the first lecture will cover technical material, illustrating its use with workable examples.  A week's second lecture will start with an in-class quiz on the previous lecture's material,  and be followed by presentation of modeling problems the students are to work on in-class.  Throughput the class the instructor and teaching assistant will be available for consultation and advice, the solutions will be required at the end of the class.   Precise grading will not be applied to problem solutions,  the point is to ensure students engage in the modeling process.

Owing to the challenges of performance assessment in the age of AI,  all material used to assess students will be gathered in-person, in-class.

Grading Policy

Activity Grade Composition
Weekly Quizzes 10%
Weekly Problem Sets 10%
Midterm 1 20%
Midterm 2 20%
Group Project 15%
Final Exam 25%
 

Course Rules

All exams will be in-class, closed-book, but will allow for two sheets of 8.5"x11" paper to contain notes available for consultation during the exam.  For group projects students will be assigned to groups of 4-5 in size, and the group will develop a project that models and analyzes some complex system using the tools and methodologies taught in the course, and make a class presentation on the project, its approach to modeling, and the results that can be concluded from the project's experiments.  No written report is required, only the slides used in the presentation.