Slides and Videos

The video lectures can be ordered on a DVD directly from VU's bookstore or can be seen on Youtube through the links below or through this playlist.

Lecture 1: Course introduction (1 lecture) [slides] [video]

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 [slides]  [video] 

Lecture 3: Common NPE errors [slides]  [video] 

Lecture 4: NPE techniques and metrics [slides]  [video] 

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

Lecture 5 : Introduction to statistics [slides]  [video] 

Lecture 6 : Summarizing measured data [slides]  [video] 

Lecture 7: Sampling a Population [slides]  [video] 

Lecture 8: Hypothesis testing [slides]  [video] 

Lecture 9: Confidence Intervals [slides]  [video] 

Lecture 10: Model fitting or Regression models [slides]  [video] 

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

Lecture 11 : The art of modeling and performance evaluation [slides]  [video] 

Lecture 12: Lies, Damned Lies and Statistics [slides]  [video] 

Lecture 13: How to present quantitative data visually? [slides]  [video] 

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

Lecture 14: Introduction to empirical science and to the concept of measurement [slides]  [video] 

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

Lecture 15: Workload Characterization Techniques [slides]  [video] 

Lecture 16  Common Distributions (Mediocristan)  [slides]  [video] 

Lecture 17: Common Distributions (Extremistan)   [slides]  [video] 

Lecture 18  Introduction to Fractals [slides]  [video] 

Lecture 19: Self-Similarity and Long-Range-Dependence (LRD) [slides]  [video] 

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

Lecture 20: Experimental Design [slides]  [video] 

Lecture 21: One-Factor Experiments [slides]  [video] 

Lecture 22: Full Factorial Design [slides]  [video] 

Lecture 23  Fractional Factorial Design [slides]  [video] 

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

Lecture 24: Network Management [slides]  [video] 

Lecture 25: Internet Measurement Issues and Tools [slides]  [video] 

Lecture 26: Internet Measurement Results [slides]  [video] 

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 [slides]  [video] 

Lecture 28: Common Stochastic Processes [slides]  [video] 

Lecture 29  Continuous-Time Markov Chains (CTMCs) [slides]  [video] 

Lecture 30: DIscrete-Time Markov Chains (DTMCs) [slides]  [video] 

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

Lecture 31: Introduction to Queuing Theory [slides]  [video] 

Lecture 32: Fundamentals of Queueing Theory [slides]  [video] 

Lecture 33: Single-Server Queues [slides]  [video] 

Lecture 34: Multiple-Server Queues [slides]  [video] 

Lecture 35: Queueing Networks [slides]  [video] 

Lecture 36: Operational Analysis [slides]  [video] 

Lecture 37: Analysis of Queueing Networks  [slides]  [video] 

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

Lecture 38: Introduction to Simulation  [slides]   [video] 

Lecture 39: Random Number Generation  [slides]   [video] 

Lecture 40: Verification and Validation  [slides]   [video] 

Lecture 41: Input Modeling  [slides]   [video] 

Lecture 42: Output Analysis  [slides]   [video] 

Lecture 43: Comparing Systems  [slides]   [video] 

Lecture 44: Simulation Tools  [slides]   [video] 

Lecture 45: Course summary/ conclusions  [slides]   [video]