Chapter 1 Computer Simulation Approach

8/13/2019 Chapter 1 Computer Simulation Approach

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Computer Simulation Approach

By Dereje Shiferaw


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Contents Models experiments and computers

System, system entity and system state Models and model classification

Simulation as experimentation

Why simulation?

When is simulation an appropriate tool Field of applications

Key phases in computer simulation

Discrete and continuous simulations

Computing: data driven software and Bespoke program

Experimentation: Interactive experiment and classicalexperiment


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INTRODUCTION

Simulation

Definition: is a numerical technique for conducting experiments on adigital computer, which involves certain types of mathematical and logical

models over extended period of real time.

System simulation

Definition:The technique of solving problems by the observation of theperformance ,over time, of the dynamic model of the system. It is an

experiment of physical scenario on computer.

Consider a situation where an expert has to decide how many and what

type of network or support resources to have available

Model the system and study its performance before actually setting

the system

Analyze system

Make operation or resource policy decisions


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Technical Attractions of Simulation

Ability to compress time, expand time

Simulation time: representation of physical time

Physical Time: time in physical system

Ability to control sources of variation Avoids errors in measurement

Ability to stop and review

Ability to restore system state

Facilitates replication

Modeler can control level of detail


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Ways To Study A System


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Models, experiments and computers

Model

Meaning

representation of construction and working of system, it is similarto but simpler than the system it represents

Purpose

to enable the analyst to predict the effect of changes to the system

Good model

close approximation to the real system incorporate most of its salient features

Not so complex

judicious tradeoff between realism and simplicity

Experiment

Running a given model of a system.

time taken for the actual system and the model may not be the same

Computer

All models are either mathematical or software and will be executedon a computer. Hence the computer is the means for conducting

experiments on the model


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System, system entity and system state

System

A system is defined as a collection of interacting components thatreceives input and provides output for some purpose

System entity

Is something (customer, objects etc) that changes the state of a system

It is one basic component of a simulation model

System state

is a set of data that captures the salient variables of the system

allows us to describe system evolution over time

is stored in one or more program variables that represent various data

structures (e.g., the number of customers in a queue, or their exactsequence in the queue).

State definition depends on the modeling needs, particularly statistics

to be collected


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Models and model classification

A model intended for simulation study can be

A physical model simplified or scaled down physicalobject

Example: Scaled model of an airplane

Mathematical or analytical model a set of equations orrelations among mathematical variables. Themathematical model can be deterministic or stochastic,static or dynamic

Example: Mathematical model of a factory workflow

Computer modela program description of the system . A

computer model with random elements and an underlyingtime element is known as Monte Carlo simulation model

Example : The operation of a manufacturing unit over aperiod of time


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Simulation as experimentation

A simulation of a system is

the operation of a model of the system tool to evaluate the performance of a system, existing or

proposed, under different configurations of interest and overlong periods of real time

The model can be reconfigured and experimented with

Used to test condition which is impossible, too expensiveor impractical to do in the system it represents

Example: desert battle simulation

- one force invading another,

- 10 years ago

- 66,239 tanks, trucks and other vehicles

- Kuwait,

- multiple supercomputers


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Simulation experiment is a test or series of tests in whichmeaningful changes are made to the input variables

From simulation experiment

The operation of the model can be studied, Properties concerning the behavior of the actual system or its

subsystem can be inferred.

Examples:- 1-billion-atom model of material deformation

- a 2.64-million-atom model of the complex maker ofprotein in all organisms, a ribosome

- the Blue Brain project at EPFL (Switzerland) 2005


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Why is simulation?

Answer questions like:

What is the best design for a new telecommunications network?

What are the associated resource requirements?

How will a telecommunication network perform when the traffic

load increases by 50%?

How will a new routing algorithm affect its performance?

Which network protocol optimizes network performance?

What will be the impact of a link failure?


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Gaining insight into the system operation

Developing operation or resource policy to improve system

performance

Deciding the type and size of network and resource to be used

Testing new concepts and/or systems before implementation

Testing an appropriate Time to leave for a TCP/IP packet in a wireless sensor

network for energy optimization

Testing the effectiveness of a DdoS prevention mechanism

Gaining information without disturbing the system

Analysis of security of a given network or Internet


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A simulation model is implemented in a computerprogram. Compared to analytical modelling, it is

relatively in expensive. It is preferred to analytical

modelling when:

When analytical model with tractable solution isunknown

When underlying model is complex

When driving analytical model is difficult or finding

analytical solution is difficult or time consuming orwhen it is difficult to capture necessary details using

mathematical model

When is simulation appropriate tool?


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Field of applications

Production system, inventory systems, Manufacturingprocess. Material handling and logistics

Estimate a set of productivity measures

Computer systems and communication networks

Design and planning of capacity so as to minimizeresponse time

Military

Business process reengineering activities

Health care operations, financial and banking operation,transportation and air ports

Improving performance


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Key phases in computer simulation

1. Model development

2. Experiment design

3. Output analysis4. Conclusion formulation

5. Making decision to alter system under study


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1. Simulation Model development

Modeling is the most important part of a simulation

study. It is determining factor

A simulation model consists of

System entities

Input variables

Performance measures

Functional relationships


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For the above system

Server and queue are system entities

Arrival rate and service rate are input variables

Mean wait time and maximum queue length are performancemeasures

Time in system is functional relationship

time_servicetime_waitsystem_in_time

Example: Simulation of a single server system


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Steps in model building

Identify the problem

List problems of existing system Produce requirements for proposed system

Formulate the problem

Identify bounds, end user

Define objectives and performance measure

Collect and process real system data

Collect data on system spec, input variables and

performance of existing system

Identify sources of randomness and select appropriate

probability distribution for each random input (exp,

poison, normal etc)


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Steps cont

Formulate and develop a model

Develop schematic and network diagrams,

translate to programs

Validate and verify the model

Verifycheck if model is correct to specifications

Validate- check if model output agree with real

system Document model for future use


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2. Experiment design

A simulation model is a test or series of tests in which meaningful

changes are made to the inputs so that we may observe andidentify the reasons for changes in performance measures

What data need to be obtained, in what form and how much

Steps

A) select appropriate experimental design

Select performance measure, key inputs and their range

B) Establish experimental conditions for runs

Starting conditions, length of run, number of independent runs,

C) perform simulation runs

Example: Simulation study of DDoS attack detection algorithm- model traffic and network

- design experiment


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Discrete and continuous simulations

Discrete simulation state trajectory is piecewise constant function whose

jumps are triggered by discrete events.

Simulation state remains unchanged unless a simulationeven occurs

When an event occur, the model undergoes a statetransition

The model evolution is governed by a clock and achronologically ordered event list.

Continuous simulation

Simulation where the system state is continuous over time Is used to model fluid flows, aircraft dynamics etc

Based on mathematical model of system

Model is digitized and simulated using program


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Computing: data driven software and

Bespoke program

Data driven software

Data is the major aspect of system

Design based on end results and inputs rather

than objects which are behaviors

Bespoke program

Custom or tailor made programs used for specific

application

Easy to use, has fewer errors, increase

productivity and unique out of competitors


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Experimentation: Interactive

experiment and classical experiment

Simulation experiments are interactive

experiments

Experiments can be repeated any number of times

with various inputs and parameters

Classical experiments are hard ware based or

use physical objects

are costly and take longer time

Chapter 1 Computer Simulation Approach

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