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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
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