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 ex...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Chen, Ken (Engineer) (Autor)
Formato: Electrónico eBook
Idioma:Inglés
Publicado: London, UK : Hoboken, NJ : ISTE, Ltd. ; Wiley, 2015.
Temas:
Computer networks > Evaluation.
Réseaux d'ordinateurs > Évaluation.
COMPUTERS > Computer Literacy.
COMPUTERS > Computer Science.
COMPUTERS > Data Processing.
COMPUTERS > Hardware > General.
COMPUTERS > Information Technology.
COMPUTERS > Machine Theory.
COMPUTERS > Reference.
Computer networks > Evaluation
Acceso en línea:Texto completo
Tabla de Contenidos:
  • Cover
  • Title Page
  • Copyright
  • Contents
  • List of Tables
  • List of Figures
  • List of Listings
  • Preface
  • 1: Performance Evaluation
  • 1.1. Performance evaluation
  • 1.2. Performance versus resources provisioning
  • 1.2.1. Performance indicators
  • 1.2.2. Resources provisioning
  • 1.3. Methods of performance evaluation
  • 1.3.1. Direct study
  • 1.3.2. Modeling
  • 1.4. Modeling
  • 1.4.1. Shortcomings
  • 1.4.2. Advantages
  • 1.4.3. Cost of modeling
  • 1.5. Types of modeling
  • 1.6. Analytical modeling versus simulation
  • PART 1: Simulation2: Introduction to Simulation
  • 2.1. Presentation
  • 2.2. Principle of discrete event simulation
  • 2.2.1. Evolution of a event-driven system
  • 2.2.2. Model programming
  • 2.2.2.1. Scheduler
  • 2.2.2.2. Object-oriented programming
  • 2.3. Relationship with mathematical modeling
  • 3: Modeling of Stochastic Behaviors
  • 3.1. Introduction
  • 3.2. Identification of stochastic behavior
  • 3.3. Generation of random variables
  • 3.4. Generation of U(0, 1) r.v.
  • 3.4.1. Importance of U(0, 1) r.v.
  • 3.4.2. Von Neumann's generator
  • 3.4.3. The LCG generators3.4.3.1. Presentation
  • 3.4.3.2. Properties
  • 3.4.3.2.1. MLCG with M = 2k
  • 3.4.3.2.2. MLCG with M primer number
  • 3.4.3.3. Examples of LCG
  • 3.4.4. Advanced generators
  • 3.4.4.1. Principle
  • 3.4.4.2. Mersenne Twister generator
  • 3.4.4.3. L'Ecuyer's generator
  • 3.4.5. Precaution and practice
  • 3.4.5.1. Nature of the PRNG
  • 3.4.5.2. Choice of seed
  • 3.4.5.3. Multiples substreams of RNG
  • 3.4.5.3.1. Principle
  • 3.4.5.3.2. Example of OMNeT++
  • 3.4.5.4. Quality of the PRNG
  • 3.5. Generation of a given distribution3.5.1. Inverse transformation method
  • 3.5.2. Acceptance rejection method
  • 3.5.2.1. Case of finite support
  • 3.5.2.2. Generalized version
  • 3.5.3. Generation of discrete r.v.
  • 3.5.3.1. Case of the finite discrete r.v.
  • 3.5.3.2. Case of countably infinite discrete r.v.
  • 3.5.4. Particular case
  • 3.5.4.1. Composition
  • 3.5.4.2. Convolution
  • 3.6. Some commonly used distributions and their generation
  • 3.6.1. Uniform distribution
  • 3.6.1.1. Utilization
  • 3.6.1.2. Parameters
  • 3.6.1.3. Generation
  • 3.6.2. Triangular distribution3.6.2.1. Utilization
  • 3.6.2.2. Parameters
  • 3.6.2.3. Generation
  • 3.6.3. Exponential distribution
  • 3.6.3.1. Utilization
  • 3.6.3.1.1. Arrival process
  • 3.6.3.1.2. Memoryless phenomena
  • 3.6.3.2. Parameter
  • 3.6.3.3. Generation
  • 3.6.4. Pareto distribution
  • 3.6.4.1. Utilization
  • 3.6.4.2. Parameters
  • 3.6.4.3. Generation
  • 3.6.5. Normal distribution
  • 3.6.5.1. Utilization
  • 3.6.5.2. Parameters
  • 3.6.5.3. Generation
  • 3.6.6. Log-normal distribution
  • 3.6.6.1. Utilization
  • 3.6.6.2. Parameters

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