Course Overview
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Course Synopsis
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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?
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Course Learning Outcomes
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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
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Course Calendar
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1
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Introduction to Network Performance Evaluation (NPE)
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4
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NPE Performance Metrics & Techniques
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5
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Introduction to Statistics
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6
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Summarizing Measured Data
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10
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Model fitting or Regression models
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11
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Art of Modeling and NPE
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12
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Lies, Damned Lies and Statistics
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13
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Visual Display of Quantitative Data
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14
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Empirical Methods and Measurements
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16
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Common Distributions (Mediocristan)
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17
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Common Distributions (Extremistan)
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18
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Introduction to Fractals
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19
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Self-similarity and LRD
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21
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One-Factor Experiments
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22
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Full Factorial Designs
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23
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Fractional Factorial Designs
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25
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Internet Measurement Issues and Tools
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26
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Internet Measurement Results
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27
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Introduction to Stochastic Processes
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28
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Common Stochastic Processes
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29
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Discrete-Time Markov Chains
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30
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Continuous-Time Markov Chains
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31
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Introduction to Queuing Theory
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32
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Fundamentals of Queueing Theory
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33
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Single-Server Queueing Systems
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34
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Multi-Server Queueing Systems
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37
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Analysis of Queueing Networks
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38
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Introduction to Simulation
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39
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Random Number Generation
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40
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Verification and Validation
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