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