Performance Evaluation by Simulation and Analysis with Applications to Computer Networks

This book is devoted to the most used methodologies for
performance evaluation: simulation using specialized software and
mathematical modeling. An important part is dedicated to the
simulation, particularly in its theoretical framework and the
precautions to be taken in the implementation of the experimental
procedure. These principles are illustrated by concrete
examples achieved through operational simulation languages
(OMNeT ++, OPNET). Presented under the complementary
approach, the mathematical method is essential for the simulation.
Both methodologies based largely on the theory of probability and
statistics in general and particularly Markov processes, a reminder
of the basic results is also available.

Autorentext
An engineer by training, Ken Chen is a professor at the University Paris 13 and professor at Telecom ParisTech.

Zusammenfassung

This book is devoted to the most used methodologies for performance evaluation: simulation using specialized software and mathematical modeling. An important part is dedicated to the simulation, particularly in its theoretical framework and the precautions to be taken in the implementation of the experimental procedure. These principles are illustrated by concrete examples achieved through operational simulation languages (OMNeT ++, OPNET). Presented under the complementary approach, the mathematical method is essential for the simulation. Both methodologies based largely on the theory of probability and statistics in general and particularly Markov processes, a reminder of the basic results is also available.

Inhalt

LIST OF TABLES xv

LIST OF FIGURES xvii

LIST OF LISTINGS xxi

PREFACE xxiii

CHAPTER 1. PERFORMANCE EVALUATION 1

1.1. Performance evaluation 1

1.2. Performance versus resources provisioning 3

1.2.1. Performance indicators 3

1.2.2. Resources provisioning 4

1.3. Methods of performance evaluation 4

1.3.1. Direct study 4

1.3.2. Modeling 5

1.4. Modeling 6

1.4.1. Shortcomings 6

1.4.2. Advantages 7

1.4.3. Cost of modeling 7

1.5. Types of modeling 8

1.6. Analytical modeling versus simulation 8

PART 1. SIMULATION 11

CHAPTER 2. INTRODUCTION TO SIMULATION 13

2.1. Presentation 13

2.2. Principle of discrete event simulation 15

2.2.1. Evolution of a event-driven system 15

2.2.2. Model programming 16

2.3. Relationship with mathematical modeling 18

CHAPTER 3. MODELING OF STOCHASTIC BEHAVIORS 21

3.1. Introduction 21

3.2. Identification of stochastic behavior 23

3.3. Generation of random variables 24

3.4. Generation of U(0, 1) r.v. 25

3.4.1. Importance of U(0, 1) r.v. 25

3.4.2. Von Neumann's generator 26

3.4.3. The LCG generators 28

3.4.4. Advanced generators 31

3.4.5. Precaution and practice 33

3.5. Generation of a given distribution 35

3.5.1. Inverse transformation method 35

3.5.2. Acceptancerejection method 36

3.5.3. Generation of discrete r.v. 38

3.5.4. Particular case 39

3.6. Some commonly used distributions and their generation 40

3.6.1. Uniform distribution 41

3.6.2. Triangular distribution 41

3.6.3. Exponential distribution 42

3.6.4. Pareto distribution 43

3.6.5. Normal distribution 44

3.6.6. Log-normal distribution 45

3.6.7. Bernoulli distribution 45

3.6.8. Binomial distribution 46

3.6.9. Geometric distribution 47

3.6.10. Poisson distribution 48

3.7. Applications to computer networks 48

CHAPTER 4. SIMULATION LANGUAGES 53

4.1. Simulation languages 53

4.1.1. Presentation 53

4.1.2. Main programming features 54

4.1.3. Choice of a simulation language 54

4.2. Scheduler 56

4.3. Generators of random variables 57

4.4. Data collection and statistics 58

4.5. Object-oriented programming 58

4.6. Description language and control language 59

4.7. Validation 59

4.7.1. Generality 59

4.7.2. Verification of predictions 60

4.7.3. Some specific and typical errors 61

4.7.4. Various tests 62

CHAPTER 5. SIMULATION RUNNING AND DATA ANALYSIS 63

5.1. Introduction 63

5.2. Outputs of a simulation 64

5.2.1. Nature of the data produced by a simulation 64

5.2.2. Stationarity 65

5.2.3. Example 66

5.2.4. Transient period 68

5.2.5. Duration of a simulation 69

5.3. Mean value estimation 70

5.3.1. Mean value of discrete variables 71

5.3.2. Mean value of continuous variables 72

5.3.3. Estimation of a proportion 72

5.3.4. Confidence interval 73

5.4. Running simulations 73

5.4.1. Replication method 73

5.4.2. Batch-means method 75

5.4.3. Regenerative method 76

5.5. Variance reduction 77

5.5.1. Common random numbers 78

5.5.2. Antithetic variates 79

5.6. Conclusion 80

CHAPTER 6. OMNET++ 81

6.1. A summary presentation 81

6.2. Installation 82

6.2.1. Preparation 82

6.2.2. Installation 83

6.3. Architecture of OMNeT++ 83

6.3.1. Simple module 84 6.3.2. C...