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Linux : le guide de survie

L'essentiel du code et des commandes
2e édition

Le compagnon indispensable pour ne jamais se sentir perdu dans un environnement Linux. Lire la suite

Les tutoriaux pour s'initier aux fondamentaux de Linux ne manquent pas. Le Guide de survie ne s’encombre pas de cela et vous propose de commencer par la pratique, avec des solutions immédiatement applicables à diverses situations que vous êtes susceptibles de rencontrer au quotidien.

Il s’agit du seul guide à proposer à la fois :

  • Des exemples de phrases de code qui permettent de commander une suite de tâches fastidieuses ou répétitives ;
  • 100 fragments de codes et commandes personnalisables pour gérer et manier Linux dans toutes les situations.

Cet ouvrage, fondé sur la simplicité d’utilisation, est le compagnon indispensable pour ne jamais se sentir perdu sous Linux.


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Spécifications


Éditeur
Pearson
Édition
2
Auteur
Scott Granneman,
Langue
français
Catégorie (éditeur)
Manuels et lecture complémentaires > Sciences, techniques et médecine > Informatique scientifique
Catégorie (éditeur)
Manuels et lecture complémentaires > Informatique
Catégorie (éditeur)
Manuels et lecture complémentaires
Code publique Onix
01 Grand public > 06 Professionnel et académique
CLIL (Version 2013-2019 )
3193 INFORMATIQUE
Description public visé
Chef de projet IT, développeur, product Owner, scrum master, entrepreneur tech.
Date de première publication du titre
05 juillet 2019

VitalSource eBook


Date de publication
01 novembre 2013
ISBN-13
9781292037264
Ampleur
Nombre de pages de contenu principal : 564
Code interne
1292037261
Protection technique e-livre
DRM

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Sommaire


I Introduction to Discrete-Event System Simulation 1

Chapter 1 Introduction to Simulation 3

1.1 When Simulation Is the Appropriate Tool 4

1.2 When Simulation Is Not Appropriate 4

1.3 Advantages and Disadvantages of Simulation 5

1.4 Areas of Application 7

1.5 Systems and System Environment 9

1.6 Components of a System 9

1.7 Discrete and Continuous Systems 11

1.8 Model of a System 12

1.9 Types of Models 13

1.10 Discrete-Event System Simulation 13

1.11 Steps in a Simulation Study 14

References 18

Exercises 19

 

Chapter 2 Simulation Examples 21

2.1 Simulation of Queueing Systems 22

2.2 Simulation of Inventory Systems 39

2.3 Other Examples of Simulation 46

2.4 Summary 57

References 57

Exercises 57

 

Chapter 3 General Principles 67

3.1 Concepts in Discrete-Event Simulation 68

3.1.1 The Event Scheduling/Time Advance Algorithm 71

3.1.2 World Views 74

3.1.3 Manual Simulation Using Event Scheduling 77

3.2 List Processing 86

3.2.1 Lists: Basic Properties and Operations 87

3.2.2 Using Arrays for List Processing 88

3.2.3 Using Dynamic Allocation and Linked Lists 90

3.2.4 Advanced Techniques 92

3.3 Summary 92

References 92

Exercises 93

 

Chapter 4 Simulation Software 95

4.1 History of Simulation Software 96

4.1.1 The Period of Search (1955—60) 97

4.1.2 The Advent (1961—65) 97

4.1.3 The Formative Period (1966—70) 97

4.1.4 The Expansion Period (1971—78) 98

4.1.5 Consolidation and Regeneration (1979—86) 98

4.1.6 Integrated Environments (1987—Present) 99

4.2 Selection of Simulation Software 99

4.3 An Example Simulation 102

4.4 Simulation in Java 104

4.5 Simulation in GPSS 112

4.6 Simulation in SSF 117

4.7 Simulation Software 120

4.7.1 Arena 122

4.7.2 AutoMod 123

4.7.3 Extend 124

4.7.4 Flexsim 124

4.7.5 Micro Saint 125

4.7.6 ProModel 125

4.7.7 QUEST 126

4.7.8 SIMUL8 127

4.7.9 WITNESS 128

4.8 Experimentation and Statistical-Analysis Tools 128

4.8.1 Common Features 128

4.8.2 Products 129

References 131

Exercises 132

 

II Mathematical and Statistical Models 147

Chapter 5 Statistical Models in Simulation 149

5.1 Review of Terminology and Concepts 150

5.2 Useful Statistical Models 156

5.3 Discrete Distributions 160

5.4 Continuous Distributions 166

5.5 Poisson Process 186

5.5.1 Properties of a Poisson Process 188

5.5.2 Nonstationary Poisson Process 189

5.6 Empirical Distributions 190

5.7 Summary 193

References 193

Exercises 193

 

Chapter 6 Queueing Models 201

6.1 Characteristics of Queueing Systems 202

6.1.1 The Calling Population 202

6.1.2 System Capacity 204

6.1.3 The Arrival Process 204

6.1.4 Queue Behavior and Queue Discipline 205

6.1.5 Service Times and the Service Mechanism 206

6.2 Queueing Notation 208

6.3 Long-Run Measures of Performance of Queueing Systems 208

6.3.1 Time-Average Number in System L 209

6.3.2 Average Time Spent in System Per Customer w 211

6.3.3 The Conservation Equation: L = λw 212

6.3.4 Server Utilization 213

6.3.5 Costs in Queueing Problems 218

6.4 Steady-State Behavior of Infinite-Population Markovian Models 220

6.4.1 Single-Server Queues with Poisson Arrivals and Unlimited Capacity: M/G/1 221

6.4.2 Multiserver Queue: M/M/c/∞/∞ 227

6.4.3 Multiserver Queues with Poisson Arrivals and Limited Capacity: M/M/c/N/233

6.5 Steady-State Behavior of Finite-Population Models (M/M/c/K/K) 235

6.6 Networks of Queues 239

6.7 Summary 241

References 242

Exercises 243

 

III Random Numbers 249

Chapter 7 Random-Number Generation 251

7.1 Properties of Random Numbers 251

7.2 Generation of Pseudo-Random Numbers 252

7.3 Techniques for Generating Random Numbers 253

7.3.1 Linear Congruential Method 254

7.3.2 Combined Linear Congruential Generators 257

7.3.3 Random-Number Streams 259

7.4 Tests for Random Numbers 260

7.4.1 Frequency Tests 261

7.4.2 Tests for Autocorrelation 265

7.5 Summary 267

References 268

Exercises 269

 

Chapter 8 Random-Variate Generation 272

8.1 Inverse-Transform Technique 273

8.1.1 Exponential Distribution 273

8.1.2 Uniform Distribution 276

8.1.3 Weibull Distribution 277

8.1.4 Triangular Distribution 278

8.1.5 Empirical Continuous Distributions 279

8.1.6 Continuous Distributions without a Closed-Form Inverse 283

8.1.7 Discrete Distributions 284

8.2 Acceptance—Rejection Technique 289

8.2.1 Poisson Distribution 290

8.2.2 Nonstationary Poisson Process 293

8.2.3 Gamma Distribution 294

8.3 Special Properties 296

8.3.1 Direct Transformation for the Normal and Lognormal Distributions 296

8.3.2 Convolution Method 298

8.3.3 More Special Properties 299

8.4 Summary 299

References 299

Exercises 300

 

IV Analysis of Simulation Data 305

Chapter 9 Input Modeling 307

9.1 Data Collection 308

9.2 Identifying the Distribution with Data 310

9.2.1 Histograms 310

9.2.2 Selecting the Family of Distributions 313

9.2.3 Quantile—Quantile Plots 316

9.3 Parameter Estimation 319

9.3.1 Preliminary Statistics: Sample Mean and Sample Variance 319

9.3.2 Suggested Estimators 321

9.4 Goodness-of-Fit Tests 326

9.4.1 Chi-Square Test 327

9.4.2 Chi-Square Test with Equal Probabilities 329

9.4.3 Kolmogorov—Smirnov Goodness-of-Fit Test 331

9.4.4 p-Values and “Best Fits” 333

9.5 Fitting a Nonstationary Poisson Process 334

9.6 Selecting Input Models without Data 335

9.7 Multivariate and Time-Series Input Models 337

9.7.1 Covariance and Correlation 337

9.7.2 Multivariate Input Models 338

9.7.3 Time-Series Input Models 340

9.7.4 The Normal-to-Anything Transformation 342

9.8 Summary 344

References 345

Exercises 346

 

Chapter 10 Verification and Validation of Simulation Models 354

10.1 Model-Building, Verification, and Validation 355

10.2 Verification of Simulation Models 356

10.3 Calibration and Validation of Models 361

10.3.1 Face Validity 362

10.3.2 Validation of Model Assumptions 362

10.3.3 Validating Input—Output Transformations 363

10.3.4 Input—Output Validation: Using Historical Input Data 374

10.3.5 Input—Output Validation: Using a Turing Test 378

10.4 Summary 379

References 379

Exercises 381

 

Chapter 11 Output Analysis for a Single Model 383

11.1 Types of Simulations with Respect to Output Analysis 384

11.2 Stochastic Nature of Output Data 387

11.3 Measures of Performance and Their Estimation 390

11.3.1 Point Estimation 390

11.3.2 Confidence-Interval Estimation 392

11.4 Output Analysis for Terminating Simulations 393

11.4.1 Statistical Background 394

11.4.2 Confidence Intervals with Specified Precision 397

11.4.3 Quantiles 399

11.4.4 Estimating Probabilities and Quantiles from Summary Data 400

11.5 Output Analysis for Steady-State Simulations 402

11.5.1 Initialization Bias in Steady-State Simulations 403

11.5.2 Error Estimation for Steady-State Simulation 409

11.5.3 Replication Method for Steady-State Simulations 413

11.5.4 Sample Size in Steady-State Simulations 417

11.5.5 Batch Means for Interval Estimation in Steady-State Simulations 418

11.5.6 Quantiles 422

11.6 Summary 423

References 423

Exercises 424

 

Chapter 12 Comparison and Evaluation of Alternative System Designs 432

12.1 Comparison of Two System Designs 433

12.1.1 Independent Sampling with Equal Variances 436

12.1.2 Independent Sampling with Unequal Variances 438

12.1.3 Common Random Numbers (CRN) 438

12.1.4 Confidence Intervals with Specified Precision 446

12.2 Comparison of Several System Designs 448

12.2.1 Bonferroni Approach to Multiple Comparisons 449

12.2.2 Bonferroni Approach to Selecting the Best 454

12.2.3 Bonferroni Approach to Screening 457

12.3 Metamodeling 458

12.3.1 Simple Linear Regression 459

12.3.2 Testing for Significance of Regression 463

12.3.3 Multiple Linear Regression 466

12.3.4 Random-Number Assignment for Regression 466

12.4 Optimization via Simulation 467

12.4.1 What Does ‘Optimization via Simulation’ Mean? 468

12.4.2 Why is Optimization via Simulation Difficult? 469

12.4.3 Using Robust Heuristics 470

12.4.4 An Illustration: Random Search 473

12.5 Summary 476

References 476

Exercises 477

 

V Applications 483

Chapter 13 Simulation of Manufacturing and Material-Handling Systems 485

13.1 Manufacturing and Material-Handling Simulations 486

13.1.1 Models of Manufacturing Systems 486

13.1.2 Models of Material-Handling 487

13.1.3 Some Common Material-Handling Equipment 488

13.2 Goals and Performance Measures 489

13.3 Issues in Manufacturing and Material-Handling Simulations 490

13.3.1 Modeling Downtimes and Failures 491

13.3.2 Trace-Driven Models 495

13.4 Case Studies of the Simulation of Manufacturing and Material-Handling Systems 496

13.5 Manufacturing Example: A Job-Shop Simulation 499

13.5.1 System Description and Model Assumptions 499

13.5.2 Presimulation Analysis 502

13.5.3 Simulation Model and Analysis of the Designed System 503

13.5.4 Analysis of Station Utilization 503

13.5.5 Analysis of Potential System Improvements 504

13.5.6 Concluding Words 506

13.6 Summary 506

References 506

Exercises 507

 

 

Chapter 14 Simulation of Computer Networks 550

15.1 Introduction 550

15.2 Traffic Modeling 552

15.3 Media Access Control 555

15.3.1 Token-Passing Protocols 556

15.3.2 Ethernet 559

15.4 Data Link Layer 561

15.5 TCP 562

15.6 Model Construction 569

15.6.1 Construction 569

15.6.2 Example 571

15.7 Summary 573

References 574

Exercises 574

Appendix 576

Index 591

 


Compléments


Interview auteur - 5 questions à Jean-Yves Léger


F0205_Sommaire


F0205_Introduction


F0205_Chapitre 1


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