CS721 : Network Performance Evaluation

Course Overview

Course Synopsis

This is a graduate level course. The purpose of this course is to present a comprehensive breadth-focused overview of empirical, analytical, and simulation techniques used for modeling and studying the performance of communication networks. In particular, following details will be covered: a. Empirical techniques: how to design valid experiments through which we systematically analyze communication networks through measurements? b. Analytical techniques: how to we make analytical models to analyze and model the performance of communication networks? In particular, we will gain an overview of queueing theory and its most important results. c. Simulation techniques: how do we make computational models to analyze and model the performance of communication networks?

Course Learning Outcomes

Upon successful completion of this course, students should be able to:

  • Apply simulation techniques to develop valid models of communication networks
  • Apply queueing-based models to characterize communication networks and to gain insights about their performance
  • Perform measurement-based empirical performance analysis of communication networks
  • Design a set of experiments to obtain the most information for a given level of effort
  • Understand the inherent trade-offs involved in using simulation, measurement, and analytical modeling
  • Avoid common simulation/ modeling/ measurement/ data presentation/ data analysis errors
  • Evaluate the relative merits of alternative system/algorithm design solutions
  • Present quantitative results visually in an effective manner
  • Engage in research in the field of performance analysis and evaluation

Course Calendar

Introduction to Network Performance Evaluation (NPE)1
Science of NPE2
Common NPE Errors3
NPE Performance Metrics & Techniques4
Introduction to Statistics5
Summarizing Measured Data6
Sampling a Population7
Hypothesis testing8
Assignment No. 1
Confidence Intervals9
Model fitting or Regression models10
Art of Modeling and NPE11
Lies, Damned Lies and Statistics12
Visual Display of Quantitative Data13
Empirical Methods and Measurements14
Workload Modeling15
Common Distributions (Mediocristan)16
Assignment No. 2
Common Distributions (Extremistan)17
Introduction to Fractals18
Self-similarity and LRD19
Experimental Design20
One-Factor Experiments21
Full Factorial Designs22
Mid-term Examination
Fractional Factorial Designs23
Network Management24
Internet Measurement Issues and Tools25
Internet Measurement Results26
Introduction to Stochastic Processes27
Common Stochastic Processes28
Discrete-Time Markov Chains29
Continuous-Time Markov Chains30
Introduction to Queuing Theory31
Course Viva
Fundamentals of Queueing Theory32
Single-Server Queueing Systems33
Multi-Server Queueing Systems34
Queueing Networks35
Operational Analysis36
Academic Research Paper
Analysis of Queueing Networks37
Introduction to Simulation38
Random Number Generation39
Verification and Validation40
Input Modeling41
Output Analysis42
Comparing Systems43
NPE Tools44
Course Summary45
Final-term Examination
Back to Top