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E-raamat: Network Modeling and Simulation - A Practical Perspective: A Practical Perspective [Wiley Online]

(Cisco Systems), (John Jay College), (Western Michigan University), (West Michigan University)
  • Formaat: 304 pages
  • Ilmumisaeg: 12-Feb-2010
  • Kirjastus: Wiley-Interscience
  • ISBN-10: 047051521X
  • ISBN-13: 9780470515211
Teised raamatud teemal:
  • Wiley Online
  • Hind: 114,19 €*
  • * hind, mis tagab piiramatu üheaegsete kasutajate arvuga ligipääsu piiramatuks ajaks
Network Modeling and Simulation - A Practical Perspective: A Practical PerspectiveSuurem pilt
  • Formaat: 304 pages
  • Ilmumisaeg: 12-Feb-2010
  • Kirjastus: Wiley-Interscience
  • ISBN-10: 047051521X
  • ISBN-13: 9780470515211
Teised raamatud teemal:
Wiley Online e-raamatudRohkem infot Wiley Online kohta
Raamatu kodulehekülg: https://onlinelibrary.wiley.com/doi/book/10.1002/9780470515211
This work shows how to use network modeling and simulation to solve real-world problems faced by developers as they model complex large-scale systems. The book begins by reviewing generic core concepts in systems modeling and simulation, without reference to a specific industry or tool. It then provides examples from computer and telecommunication networks to show how to apply generic simulation concepts to domain-specific problems. The book then goes on to provide tools and strategies for building simulation models and solutions from the ground up. Code examples to illustrate commonly encountered simulation tasks are presented in Simjava, MATLAB, and an original simulation tool, CASiNO (Component Architecture for Simulating Network Objects) built by the authors. Guizani is affiliated with Kuwait University, Kuwait. Annotation ©2010 Book News, Inc., Portland, OR (booknews.com) Network Modeling and Simulation is a practical guide to using modeling and simulation to solve real-life problems. The authors give a comprehensive exposition of the core concepts in modeling and simulation, and then systematically address the many practical considerations faced by developers in modeling complex large-scale systems. The authors provide examples from computer and telecommunication networks and use these to illustrate the process of mapping generic simulation concepts to domain-specific problems in different industries and disciplines. Key features: Provides the tools and strategies needed to build simulation models from the ground up rather than providing solutions to specific problems. Includes a new simulation tool, CASiNO built by the authors. Examines the core concepts of systems simulation and modeling. Presents code examples to illustrate the implementation process of commonly encountered simulation tasks. Offers examples of industry-standard modeling methodology that can be applied in steps to tackle any modeling problem in practice.
Preface xi
Acknowledgments xv
Basic Concepts and Techniques
1(24)
Why is Simulation Important?
1(3)
What is a Model?
4(4)
Modeling and System Terminology
6(1)
Example of a Model: Electric Car Battery Charging Station
6(2)
Performance Evaluation Techniques
8(8)
Example of Electric Car Battery Charging Station
10(3)
Common Pitfalls
13(1)
Types of Simulation Techniques
14(2)
Development of Systems Simulation
16(8)
Overview of a Modeling Project Life Cycle
18(2)
Classifying Life Cycle Processes
20(1)
Describing a Process
21(1)
Sequencing Work Units
22(1)
Phases, Activities, and Tasks
23(1)
Summary
24(1)
Recommended Reading
24(1)
Designing and Implementing a Discrete-Event Simulation Framework
25(20)
The Scheduler
26(6)
The Simulation Entities
32(2)
The Events
34(1)
Hello World
34(2)
Two-Node Hello Protocol
36(2)
Two-Node Hello through a Link
38(3)
Two-Node Hello through a Lossy Link
41(3)
Summary
44(1)
Recommended Reading
44(1)
Honeypot Communities: A Case Study with the Discrete-Event Simulation Framework
45(24)
Background
45(2)
System Architecture
47(2)
Simulation Modeling
49(17)
Event Response in a Machine, Honeypot, and Sensors
49(2)
Event Response in a Worm
51(2)
System Initialization
53(7)
Performance Measures
60(2)
System Parameters
62(2)
The Events
64(2)
Simulation Execution
66(1)
Output Analysis
67(1)
Summary
68(1)
Recommended Reading
68(1)
Monte Carlo Simulation
69(28)
Characteristics of Monte Carlo Simulations
69(1)
The Monte Carlo Algorithm
70(4)
A Toy Example: Estimating Areas
70(2)
The Example of the Electric Car Battery Charging Station
72(1)
Optimizing the Electric Car Battery Charging Station
73(1)
Merits and Drawbacks
74(1)
Monte Carlo Simulation for the Electric Car Charging Station
75(20)
The Traffic Generator
76(3)
The Car
79(1)
The Charging Station
80(2)
The Server
82(3)
Putting It All Together
85(2)
Exploring the Steady State
87(3)
Monte Carlo Simulation of the Station
90(5)
Summary
95(1)
Recommended Reading
96(1)
Network Modeling
97(14)
Simulation of Networks
98(1)
The Network Modeling and Simulation Process
99(1)
Developing Models
100(3)
Network Simulation Packages
103(3)
OPNET: A Network Simulation Package
106(4)
Summary
110(1)
Recommended Reading
110(1)
Designing and Implementing CASiNO: A Network Simulation Framework
111(46)
Overview
112(5)
Conduits
117(4)
Visitors
121(1)
The Conduit Repository
122(1)
Behaviors and Actors
123(8)
Adapter---Terminal
125(1)
Mux---Accessor
126(2)
Protocol---State
128(1)
Factory---Creator
129(2)
Terminals
131(4)
States
135(3)
Making Visitors
138(4)
Muxes
142(7)
Factories
149(5)
Summary
154(1)
Recommended Reading
154(3)
Statistical Distributions and Random Number Generation
157(24)
Introduction to Statistical Distributions
158(2)
Probability Density Functions
158(1)
Cumulative Density Functions
158(1)
Joint and Marginal Distributions
159(1)
Correlation and Covariance
159(1)
Discrete versus Continuous Distributions
160(1)
Discrete Distributions
160(4)
Bernoulli Distribution
160(1)
Binomial Distribution
161(1)
Geometric Distribution
162(1)
Poisson Distribution
163(1)
Continuous Distributions
164(5)
Uniform Distribution
164(1)
Gaussian (Normal) Distribution
165(1)
Rayleigh Distribution
166(1)
Exponential Distribution
167(1)
Pareto Distribution
168(1)
Augmenting CASiNO with Random Variate Generators
169(1)
Random Number Generation
170(2)
Linear Congruential Method
170(1)
Combined Linear Congruential
171(1)
Random Number Streams
172(1)
Frequency and Correlation Tests
172(3)
Kolmogorov---Smirnov Test
173(1)
Chi-Square Test
174(1)
Autocorrelation Tests
174(1)
Random Variate Generation
175(4)
Inversion Method
175(1)
Accept---Reject Method
176(1)
Importance Sampling Method
177(1)
Generate Random Numbers Using the Normal Distribution
177(1)
Generate Random Numbers Using the Rayleigh Distribution
178(1)
Summary
179(1)
Recommended Reading
180(1)
Network Simulation Elements: A Case Study Using CASiNO
181(16)
Making a Poisson Source of Packets
181(2)
Making a Protocol for Packet Processing
183(4)
Bounding Protocol Resources
187(1)
Making a Round-Robin (De)multiplexer
188(2)
Dynamically Instantiating Protocols
190(2)
Putting it All Together
192(3)
Summary
195(2)
Queuing Theory
197(38)
Introduction to Stochastic Processes
198(3)
Discrete-Time Markov Chains
201(2)
Continuous-Time Markov Chains
203(1)
Basic Properties of Markov Chains
203(1)
Chapman-Kolmogorov Equation
204(1)
Birth---Death Process
205(1)
Little's Theorem
206(1)
Delay on a Link
207(1)
Standard Queuing Notation
207(1)
The M/M/1 Queue
208(4)
A CASiNO Implementation of the M/M/1 Queue
209(2)
A SimJava Implementation of the M/M/1 Queue
211(1)
A MATLAB Implementation of the M/M/1 Queue
211(1)
The M/M/m Queue
212(9)
A CASiNO Implementation of the M/M/m Queue
214(3)
A SimJava Implementation of the M/M/m Queue
217(3)
A MATLAB Implementation of the M/M/m Queue
220(1)
The M/M/1/b Queue
221(5)
A CASiNO Implementation of the M/M/1/b Queue
222(2)
A SimJava Implementation of the M/M/1/b Queue
224(1)
A MATLAB Implementation of the M/M/1/b Queue
225(1)
The M/M/m/m Queue
226(6)
A CASiNO Implementation of the M/M/m/m Queue
227(3)
A SimJava Implementation of the M/M/m/m Queue
230(1)
A MATLAB Implementation of the M/M/m/m Queue
231(1)
Summary
232(1)
Recommended Reading
233(2)
Input Modeling and Output Analysis
235(24)
Data Collection
236(1)
Identifying the Distribution
237(3)
Estimation of Parameters for Univariate Distributions
240(4)
Goodness-of-Fit Tests
244(5)
Chi-Square Goodness-of-Fit Test
246(1)
Kolomogorov-Smirnov Goodness-of-Fit Test
247(2)
Multivariate Distributions
249(4)
Correlation and Covariance
249(2)
Multivariate Distribution Models
251(1)
Time-Series Distribution Models
251(2)
Selecting Distributions without Data
253(1)
Output Analysis
253(3)
Transient Analysis
254(1)
Steady-State Analysis
255(1)
Summary
256(1)
Recommended Reading
256(3)
Modeling Network Traffic
259(14)
Introduction
259(1)
Network Traffic Models
260(1)
Constant Bit Rate (CBR) Traffic
260(1)
Variable Bit Rate (VBR) Traffic
260(1)
Pareto Traffic (Self-similar)
261(1)
Traffic Models for Mobile Networks
261(2)
Global Optimization Techniques
263(3)
Genetic Algorithm
263(1)
Tabu Search
263(1)
Simulated Annealing
264(2)
Particle Swarm Optimization
266(1)
Solving Constrained Optimization Problems Using Particle Swarm Optimization
266(1)
Optimization in Mathematics
267(3)
The Penalty Approach
267(1)
Particle Swarm Optimization (PSO)
268(1)
The Algorithm
269(1)
Summary
270(1)
Recommended Reading
270(3)
Index 273
Dr. Guizani is currently a Full Professor and the Chair of the Computer Science Department at Western Michigan University.

Mr. Qadan is the General Manager of Equinox International. He worked and served as a Senior Technical Director for OPNET Technologies (leading modelling and simulation vendor) between 2002 and 2005.
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