NETW 707: Modeling & Simulation
Course Instructor
Instructor Office
Instructor Email
: Tallal Elshabrawy
: C3.321
: [email protected]
Teaching Assistants
Mawra Zamzam
: [email protected]
The Real Name of This Course
Modeling & Simulation of Telecommunication Networks
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Course Pre-requisites
PROBABILITY
PROGRAMMING
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Course Main Players
Markov Models
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Probability Models
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Course Instructional Goals
 Understanding the fundamentals of Modeling &
Simulation
 Learning the role of modeling and simulation tools
in designing, analyzing, and evaluating
telecommunication networks
 Learning different tools for modelling various
telecommunication functions and systems
 Building simulation models corresponding to
devised telecommunication models
 Running simulation models and collecting result
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Course Outline
I- Introduction
 Modeling & Simulation
 Simple Simulation Example
II- Error/Flow Control Modeling
 Stop-and-Wait
 Go-Back-N
 Selective Repeat
III- Traffic Modeling
 Poisson Traffic Models
 Self-Similar Traffic Models
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Course Outline
IV- Medium Access Modeling
 ALOHA
 CSMA
V- Network & Topology Modeling
 Graph Models
 Routing Modeling
VI- Simulation Statistics Collection
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Course Assessment
Classification
Description
Quiz –
Theoretical
Best 2 out of 3
10%
Assignments –
Theoretical
Mix of Modeling & Simulation
10%
Project
OMNET++ Based Project
15%
Mid-Term Exam –
Theoretical
Midterm Exam
25%
Final Exam –
Theoretical
Final Exam
40%
Total
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Weight
100%
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Modeling & Simulation: An Introduction
Some slides in this presentation have been copyrighted to Dr. Amr Elmougy
Systems
Systems:
A group of objects joined
together in some regular
interaction or
interdependence towards the
accomplishment of some
purpose.
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Systems
Systems Environment:
A system is affected by
changes that occur outside its
boundaries. Such changes are
said to occur in the system
environment
The boundary between the
system and its environment
depend on the purpose of the
study
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System Environment
o/p
i/p
Boundary
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System Components
Entity
An instantaneous
occurrence that
may change the
state of the system
Event
Object of interest
in the system
Attribute
Property of an
entity
System
An action that takes place
over a period of specified
length and changes the
state of the system
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Activity
State
Collection of variables
necessary to describe the
system at a particular
time,
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System Components Example
Packet
Entity
Packet
Arrival
Length,
Destination
Event
Attribute
Queuing
System
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FIFO
Nof Packets
Activity
State
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Types of Systems
Discrete
Continuous
State variables
change
instantaneously at
separated points in
time
State variables
may change
constantly with
respect to time
Slotted ALOHA Vs Pure ALOHA
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Simulation of Continuous Systems
It is feasible to study continuous systems analytically
mathematically
However when it comes to simulation it is challenging.
Solutions:
 Approximate continuous system as a discrete event
simulation (Discretization)
 Event-Driven Simulations
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Approaches to Studying Systems
System
Experiment
with the
Actual System
Experiment
with a Model
of the System
Physical
Model
Mathematical
Model
Analytical
Solution
Simulation
NETW 707 Modeling & Simulation
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Why do we Need Modeling
 It is not possible to experiment with the actual system, e.g.:
the experiment is destructive
 The system might not exist, i.e. the system is in the design
stage
 In essence it boils down to:
Time
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Cost
Complexity
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Models
 A model is a representation of a system for the purpose
of studying that system
 It is only necessary to consider those aspects of the
system that affect the problem under investigation
 The model is a simplified representation of the system
 The model should be sufficiently detailed to permit valid
conclusions to be drawn about the actual system
 Different models of the same system may be required as
the purpose of the investigation changes
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Modeling Examples
Grade of Service in 2G Cellular Networks
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Modeling Examples
Grade of Service in 2G Cellular Networks
Aggregate Arrivals
Nof Channels
All Servers Busy = Calls Blocked or Delayed
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Approaches to Solving Models
 Mathematically:
Derivation of equations to answer questions
regarding the model.
Derivation Process and Solving could be handled:
 Analytically: Derives closed form (hopefully concise)
expressions
 Numerically: Utilizes the help of numerical
approximations (by hand or by programming)
 By Simulation: Building a program that tries to
imitate the behavior of the model under study
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Introduction to Simulations
 Simulation is the imitation of a real-world process
or system over time [Banks et al.]
 It is used for analysis and study of complex
systems
 Simulation requires the development of a
simulation model
 Computer-based experiments are conducted to
describe, explain, and predict the behaviour of the
real system
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Advantages of Simulations
 Effects of variations in the system parameters can be
observed without disturbing the real system
 New system designs can be tested without committing
resources for their acquisition
 Hypotheses on how or why certain phenomena occur
can be tested for feasibility
 Time can be expanded or compressed to allow for speed
up or slow down of the phenomenon under investigation
 Insights can be obtained about the interactions of
variables and their importance
 Bottleneck analysis can be performed in order to
discover where work processes are being delayed
excessively
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Disadvantages of Simulations
 Model building requires special training
 Simulation results are often difficult to interpret. Most
simulation outputs are random variables - based on
random inputs – so it can be hard to distinguish whether an
observation is the result of system inter-relationship or
randomness
 Simulation modeling and analysis can be time consuming
and expensive
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When are Simulations not Necessary
 The problem can be solved by common sense
 The problem can be solved analytically
 It is less expensive to perform direct experiments
 Costs of modeling and simulation exceed savings
 Resources or time are not available
 Lack of necessary data
 System is very complex or cannot be defined
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Looping around Simulation Limitations
 Modifying System Models
 Adopting Distributed/Parallel Processing
 Utilizing customized simulation packages for the
system under study
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Classification of Simulation Models
Static
Deterministic
Discrete
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Dynamic
Stochastic
Continuous
Static and Dynamic models
Static
• Monte Carlo Simulation
Represents a system
snapshot at a particular
point in time
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Dynamic
• Represents systems as
they change over time
Deterministic and Stochastic Models
Deterministic
• Contain no random
variables
• Has a known set of
inputs that will result in
a unique set of outputs
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Stochastic
• Has one or more
random variables
• Random inputs lead to
random outputs
• Random outputs only
estimates of the true
characteristics of the
system
Discrete and Continuous Models
Discrete
• Simulation progress
over discrete time
instants
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Continuous
• Simulation progresses
over continuous time