Lectures

Lecture 1: Course introduction (1 lecture)

Part-1: The Science and Art of Network Performance Evaluation (12 lectures)

Part 1A: Lecture 2 to 4 (3 lectures): The Science of NPE

Lecture 2: The science of performance evaluation

Lecture 3: Common NPE errors

Lecture 4: NPE techniques and metrics

Part 1B: Lecture 5 to 8 (6 lectures): Statistical background (grammar of the science of NPE)

Lecture 5 : Introduction to statistics

Lecture 6 : Summarizing measured data

Lecture 7: Sampling a Population

Lecture 8: Hypothesis testing

Lecture 9: Confidence Intervals

Lecture 10: Model fitting or Regression models

Part 1C: Lecture 11 to 13 (3 lectures): The Art of NPE

Lecture 11 : The art of modeling and performance evaluation

Lecture 12: Lies, Damned Lies and Statistics

Lecture 13: How to present quantitative data visually?

Part-2: Experimental/ Empirical Network Performance Evaluation (11 lectures)

Lecture 14: Introduction to empirical science and to the concept of measurement

Part 2A: Lecture 15 to 17 (3 lectures): Workload Characterization

Lecture 15: Workload Characterization Techniques

Lecture 16  Common Distributions (Mediocristan)

Lecture 17: Common Distributions (Extremistan) 

Lecture 18  Introduction to Fractals

Lecture 19: Self-Similarity and Long-Range-Dependence (LRD)

Part 2B: Lecture 18 to 21 (4 lectures): Design of Experiments

Lecture 20: Experimental Design

Lecture 21: One-Factor Experiments

Lecture 22: Full Factorial Design

Lecture 23  Fractional Factorial Design

Part 2C: Lecture 22 to 24 (3 lectures): Internet Measurement and Empirical Case Studies

Lecture 24: Network Management

Lecture 25: Internet Measurement Issues and Tools

Lecture 26: Internet Measurement Results

Part-3: Modeling and Simulation based Network Performance Evaluation (20 lectures)

Part 3A: Lecture 25 to 28 (6 lectures): Mathematical preliminaries (Probability)

Lecture 27: Introduction to Stochastic Processes

Lecture 28: Common Stochastic Processes

Lecture 29  Continuous-Time Markov Chains (CTMCs)

Lecture 30: DIscrete-Time Markov Chains (DTMCs)

Part 3B: Lecture 29 to 37 (7 lectures): Analytical modeling

Lecture 31: Introduction to Queuing Theory

Lecture 32: Fundamentals of Queueing Theory

Lecture 33: Single-Server Queues

Lecture 34: Multiple-Server Queues

Lecture 35: Queueing Networks

Lecture 36: Operational Analysis

Lecture 37: Analysis of Queueing Networks

Part 3C: Lecture 38 to 44 (7 lectures): Simulation modeling

Lecture 38: Introduction to Simulation

Lecture 39: Random Number Generation

Lecture 40: Verification and Validation

Lecture 41: Input Modeling

Lecture 42: Output Analysis

Lecture 43: Comparing Systems

Lecture 44: Simulation Tools

Lecture 45: Course summary/ conclusions