Al-Imam Mohammad Ibn Saud University
CS433
Modeling and Simulation
Lecture 01
Introduction
http://10.2.230.10:4040/akoubaa/cs433/
19 Oct 2008
Dr. Anis Koubâa
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Who I Am?
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 Dr. Anis Koubâa
 Assistant Professor at CS Dept.
 Research Associate at ISEP/IPP-HURRAY! (Portugal)
 Email (Teaching): anis.koubaa.ccis@gmail.com
 Phone: 25 81 921
 Background
 PhD from INPL/LORIA, Nancy (France)
 Master from UHP Nancy I (France)
 My Research
 Main: Wireless Sensor Networks, Cyber-Physical Systems, RealTime Embedded Systems, Quality-of-Service, Performance
Evaluation
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 Other: Security
Goals for Today
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 Understand what is simulation and what
is modelling
 Some applications of simulation and
modelling
 What is the Life Cycle of the Modelling
Process
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Keywords
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 System
 It is a collection of entities that act and interact together
toward the accomplishment of some logical end (computer,
network, communication systems, etc.)
 Simulation
 It is an experiment in a computer where the real system is
replaced by the execution of the program
 It is a program that mimics (imitate) the behaviour of the real
system
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Keywords
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 Model
 It is a simplification of the reality
 A (usually miniature) representation of something; an example
for imitation or emulation
 A model can be Analytical (Queuing Theory) or by Simulation.
 Performance Evaluation of a System means quantifying the
service delivered by the System
 Experimental, Analytical, or by simulation
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Example
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Models of the System
Real System (Motherboard)
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Example
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Simulation Models of the System
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Example
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Example
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Models of the System
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Simulation
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Why we need to Simulate?
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 It may be too difficult, risky, or expensive to observe a
real, operational system
 Parts of the system may not be observable (e.g., internals
of a silicon chip or biological system)
 Uses of simulations
 Analyze systems (performance, behavior) before they are built
 Reduce the number of design errors
 Optimize design to improve the behavior
 Analyze operational systems
 Create virtual environments for training, entertainment
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Applications of Simulation
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 System Analysis
 Telecommunication Networks (ATM, IP, TCP, UDP, WiFi …)
 Transportation systems (Traffic, Urban planning, Metro Planning, …)
 Electronic systems (e.g., microelectronics, computer systems)
 Battlefield simulations (blue army vs. red army)
 Ecological systems, Manufacturing systems, Logistics …
 Virtual Environments
 Physical phenomena (e.g. Trajectory of projectiles)
 training and entertainment (e.g., military, medicine, emergency planning,
flight simulation)
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A Few Example Applications
War gaming: test
strategies; training
Flight Simulator
Transportation systems:
improved operations;
urban planning
Games
Parallel computer systems:
developing scalable software
Computer communication
network: protocol design
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Computer Simulation
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 A Computer Simulation is a computer program
 that attempts to simulate an abstract model of a particular
system.
 that describes the behavior of a real (physical) system and its
evolution in time
 How it works?
 The behavior of the system is described by state variables
 The simulation program modifies the states variables to
emulate the evolution
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Model Development Life Cycle
Define goals, objectives of study
Develop conceptual model
Develop specification of model
Fundamentally an
iterative process
Develop computational model
Verify model
Validate model
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Model Development Life Cycle
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Determine Goals and Objectives
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What do you what to do with the model
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It may be an end in itself
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More often, it is a means to an end
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Goals may not be known when you start the project!
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One often learns things along the way
Develop Conceptual Model
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An abstract representation of the system
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What should be included in model? What can be left out?
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What abstractions should be used? What is the level of details?
Appropriate choice depends on the purpose of the model
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Model Development Life Cycle
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Develop Specification Model
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A more detailed specification of the model including more specifics
Collect data to populate model
 Example:
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Traffic: Road geometry, signal timing, expected traffic demand, driver behavior
Communication: network topology, message type, inter-arrival time
Empirical data or probability distributions often used
Develop a Computational Model
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Executable simulation model
Software approach
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General purpose programming language
Special purpose simulation language
Simulation package
Other (non-functional) requirements
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Performance
Interoperability with other models/tools/data
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Model Development Life Cycle
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Verification
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Did I Build the Model Right?
‫هل أنجزت النموذج بطريقة صحيح؟‬
Does the computational model match the specification model?
Debugging: checking if the program contains any programming errors.
Verification is different from Validation: (see model validation)!
Validation
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Did I Build the Right Model?
‫هل أنجزت النموذج الصحيح؟‬
Does the computational model match the actual (or envisioned) system?
Typically, the validation of a simulation model can be done by comparing
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Measurements of actual system
An analytic (mathematical) model of the system
Another simulation model
By necessity, validation is always an incomplete activity!
Often can only validate portions of the model
If you can validate the simulation with 100% certainty, why build the
simulation?
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Validation
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Did I build the right model?
Does the computational model match the actual (or
envisioned) system?
Typically, compare against
Measurements of actual system
 An analytic (mathematical) model of the system
 Another simulation model
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By necessity, always an incomplete activity!
Often can only validate portions of the model
 If you can validate the simulation with 100% certainty,
why build the simulation?

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Performance Evaluation
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Performance Metrics
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 The Performance Metric is a measurable quantity that precisely
captures what we want to measure (response time, throughput,
delay, etc.).
 For example, In computer systems, we might evaluate
 The response time of a processor to execute a given task.
 The execution time of two programs in a multi-processor
machine.
 In Network systems, we might evaluate
 The (maximum/average) delay experienced by a voice
packet to reach the destination
 The throughput of the network
 The required bandwidth to avoid congestion
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What does affect the performance?
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 The performance of a system is dramatically affected by the Workload
 The Workload: it characterises the quantity and the nature of the system
inputs
 In the context of Web Servers, system inputs are http requests (GET or
POST requests). The workload characterises
 the intensity of the requests: how many requests are received by the
web server. High intensities deteriorate the performance.
 The nature of the requests: the request can be simple GET request or
a request that require the access of a remote database. The
performance will be different for different request types.
 Benchmarks: used to generate loads that is intended to mimic a typical
user behaviour.
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Why using Models and Simulations?
 Why to use models?
 Implementation on real systems is very complex and costly,
 Experimentation on real systems may be dangerous (e.g. chemical
systems)
 If models adequately describes the reality, experimenting with them
can save money and time, and reduce the development complexity
 When to use simulations?
 Analytic models may be very complex to evaluate, and may lead to
over implication of the real system
 Simulation can be a good alternative to evaluate the system behavior
very close to reality
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Some Terminologies
 State: A variable characterizing an attribute in the system
 the number of (messages/clients) in a queue waiting to be processed
 Event: An occurrence at a point in time which may change the state of the
system
 Arrival/Departure of a message or start transmitting a message
 Entity: An object that passes through the system
 Message (or packet) in a network, A job of a task in computer
 Queue: A queue is buffer where entities are waiting for something to
happen for any reason
 Network: Queues are implemented in routers to handle messages
 Computer: Queues are implemented to handle jobs of tasks
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Some Terminologies
 Creating: Creating is causing an arrival of a new entity to the system at
some point in time.
 Scheduling: Scheduling is the act of
 Random variable: A random variable is a quantity that is uncertain, such
as inter-arrival time between two incoming flights or number of defective
parts in a shipment
 Random variate: A random variate is an artificially generated random
variable
 Distribution: A distribution is the mathematical law which governs the
probabilistic features of a random variable.
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