Lecture Notes

  • Lecture notes in PDF are available before each class from this site.
  • Use Acrobat Reader from Adobe to view and print the PDF files.
  • It’s important to read the related materials indicated in Relevant Reading section, based on the Lecture Notes.
  • JAIN in Relevant Reading denotes the reference book “The Art of Computer Systems Performance Analysis: Techniques for Experimental Design, Measurement, Simulation, and Modeling” by R. Jain
  • Allen in Relevant Reading denotes the required text “Probability, Statistics and Queuing Theory: with Computer Science Applications (2ND)” by A. Allen.
  • Copyright (c) 2004-2024, Liudong Xing. All rights reserved. Personal use of this material is permitted.
Topic Date Notes Relevant Reading
Syllabus & Operational Details
Intro. to CSPE
Background Survey
1/22 (M) Lecture 1 A case study on Remote pipes vs. RPC (Slide 41) PDF
Reference: JAIN Ch. 2 & 3
Measurement Techniques & Tools
1/24 (W) Lecture 2 Reference: JAIN: Ch. 4, 7, 12
An Synthetic Program Example
A Software Monitor Example
Hands-on problem solutions (Slides 24, 30, 31, 32)
Experimental Design and Analysis
1/29 (M) Lecture 3 Reference: JAIN: Part IV;
Project team setup due; Add/Drop deadline
HW#1 assigned
Hands-on problem solutions (Slide 23, Slide35)

Simulations 1/31 (W) Lecture 4 L#3 review questions (Slide 5)
References on AweSim (optional):Introduction to AweSim;
AweSim: The Integrated Simulation System and AweSim Lecture
Probability Theory (Review) 2/5 (M) Lecture 5 Reference: Allen: Ch. 2.0-2.4
HW#1 due; HW#2 assigned
Hands-on problems solutions; Extra
Random Variables (Review) 2/7 (W) 2/12(M) Lecture 6 Reference: Allen: Ch. 2.5-2.6, 3.0-3.2
Extra notes for discrete r.v., continuous r.v.; Slide 37; Extra
HW#2 due; HW#3 assigned
Jointly Distributed R.V. 2/14(W) Lecture 7 Reference: Allen: Ch. 2.7
Solutions to Slides 8, 11; Slide 19; Slide 21, Slide 22
Statistical Inference 2/14(W) Lecture 8 Lecture #8 is a self-study lecture
Reference: Allen: Ch. 7.0-7.2
Proof of unbiasedness Slide 11, Slide 12
Presidents’ Day Holiday: no classes 2/19(M) No Lecture Have a good long weekend!
Stochastic Processes (I): Counting, Poisson, Birth-Death 2/20(T) 2/21(W) Lecture 9 Tuesday Follows Monday’s Class Schedule
Reference: Allen: Ch. 4
Solutions to Slides 7, 17, 23, 24, 31; Slide 16
Stochastic Processes (II): Markov 2/21(W) 2/26(M) Lecture10 Reference: Allen: Ch. 4; Example on Slide20
Solution to Slides 11; Slide 12,16; Slide 17; Slides 27, 29; Slide 30; Extra
HW#3 due; Project proposal due by Friday (2/23)
HW#4 assigned
Queueing Systems (I): Kendall’s notation, D/D/1, M/M/1 2/28(W) Lecture11 Reference: Allen: Ch. 5.0-5.2
Solutions to Slide 37
Midterm Exam Review 3/4(M) Lecture12 Preparation: L#2, 3, 5-7, 9, 10; HW#1-4
Sample Questions and Solution
HW#4 due
Midterm Exam 3/6(W) Exam Good Luck
Spring Break 3/11(M) 3/13(W) No Classes Enjoy the spring break!
Midterm exam solution discussion 3/18(M) Lecture11 Mid-semester survey; Finish Lecture11
Queueing Systems (II): M/M/1/N, M/M/c, M/M/infinity, M/M/1/k/k
3/20(W) 3/25(M) 3/27(W) Lecture13 Allen: Ch. 5.0 ~ 5.2   
Project bibliography due by 3/22, Friday!
Solution to Slide 5 (M/M/1/2), Slide 9, Slide 16, Slide 24, Slide 27, Slide 36, Extra
HW#5 assigned 3/25

Embedded Markov-Chain Queueing Systems & Priority Queueing Systems
3/27(W) 4/1(M) Lecture14 Allen: Ch. 5.3 ~ 5.4 
Detailed Derivations; Solution to Slide 19, Slide24

Queueing Networks 4/1(M) Lecture15 HW#5 due; HW#6 assigned 4/1
Allen: Ch. 6