NET 4001/ITEC 5113
Network Simulation
Introduction to Simulation
© Marc St-Hilaire
Overview
• What is simulation?
• Why/why not use simulation?
• Advantages/disadvantages
• Definition of system and its components
• What is a model?
• Categorization of models
• Different steps
• Simulation software
• Readings
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What is simulation?
Definitions
1) “Imitation of the operation of a real-world process or
system over time.” (Banks et al.)
2) “Process of designing a model of a real system and
conducting experiments for the purpose of understanding the
behavior of the system.” (Shannon, 1975)
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Done by hand or computer
Involves generation of artificial history to draw conclusion
Used to investigate different “What if” scenarios
Can be used with existing (effect of change) and new systems (predict
performance)
– In contrast to optimization models, simulation models are “run” rather
than “solved”.
Simulation vs Emulation vs Real system
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Why use simulation?
• Study and/or experiment the internal interaction of complex
systems
• Different changes can be simulated
• Give insight for improvement in the system under investigation
• See how the system reacts to different inputs
• Verify and give strength to analytic solutions
• Can be used for training new employee
– CAE (mainly known for their flight simulators): http://www.cae.com
• Video of where simulation is going in the military (Live, Virtual, and Constructive LVC)
• Video talking about a massive Simulation Training Centre in Brunei
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Do NOT use simulation if:
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The problem can be solved by common sense
The problem can be solved analytically
It is easier to solve the problem by direct experimentations
Simulation cost > savings
Resources and/or time are not available
No data is available
Verification and validation can't be performed
– Verification: Building the model correctly
– Validation: Building the correct model to capture the desired
characteristics
• System behavior is too complex or can’t be defined (e.g., some
human behavior)
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Advantages vs. disadvantages of simulation
Advantages
– Money $$
– Level of details
– Different scenarios can be evaluated
– Insight can be obtained from the interaction of variables
Disadvantages
– Need of special training
– Errors (manipulation, model and so on)
– Simulators are not always perfect
– It is simulation…
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What is a system?
When we perform a simulation, it’s because we want to simulate a
system.
System: Group of objects that are joined together
(relationships/interactions) in order to accomplish some
purposes.
System environment: A system can be affected by changes
occurring outside the system. These changes are said to
occur in the system environment.
It is important to decide where to place the boundary between
the system and its environment. This decision may depend on
the purpose of the simulation.
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System vs. boundary
In this example, the arrival of orders is outside the system boundary
and therefore considered as a constraint.
System
boundary
System
(eg. Factory)
Environment
(eg. arrival of orders)
Figure 1: System vs. boundary
However, in some other cases (e.g., study the effect of supply
on demand), it can be considered as part of the system.
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Components of a system
Entity: Object of interest
Attribute: Property of an entity
Activity: Time period of specified length
State: Collection of variables representing the system at any
given time
Event: occurrence that might change the system state
– Endogenous: event occurring inside the system
– Exogenous: event occurring in the environment of the system
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Examples
1) Banking System:
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Entities: Customers
Attributes: Check account balance
Activities: Making deposit
Events: Arrivals and departures
State variables: Number of busy tellers, number of customers waiting, etc.
2) Communication System:
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Entities: Messages (or packets)
Attributes: Length, destination
Activities: Transmitting
Events: Arrivals
State variables: Number of messages waiting to be transmitted, etc.
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Model
To study a system, we can:
1) Experiment with the system itself (not always possible!)
2) Develop a model
What is a model?
• Representation (simplification) of a real-world system
• Generally composed of assumptions (mathematical, logical,
etc.)
• Some models are impossible to solve mathematically…that’s
why we need simulation
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Categorization of models
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Discrete Systems
– State variables change only at
discrete set of points in time.
– Example: Number of customers
in a bank.
Continuous Systems
– State variables change
continuously in time.
– Example: water level behind a
dam (in meter).
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Continuous
Discrete
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1
0
Time
Figure 2: Discrete vs. Continuous
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Categorization of models
• Stochastic/Deterministic: A deterministic model has no random
variables (known input). Stochastic models have random variables.
• Static/Dynamic: In a static model, time is not a significant variable
(static is like a picture of the system at a given time).
• Continuous/Discrete: In continuous system, state variables evolve
continuously.
Model
Stochastic
Static
Deterministic
Dynamic
Continuous
Static
Discrete
Dynamic
Continuous
Discrete
Discrete-Event Simulation
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Steps in a simulation study*
Alternatives
1) Discovery or
orientation
3) Running of
the model
Performance
analysis
Computer program
4) Implementation
Are you building it right?
Calibration
Are you building the right thing?
2) Model building
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*Discrete Event Simulation Systems,
4th
edition, Banks et al.
Simulation software
A lot of software have been developed to do simulation:
• Network simulators, computer networks and communications
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Packet Tracer
Riverbed/OPNET
NS-2, NS-3
OMNET++, GTnetS and so on…
• General simulators for real life situations
– Arena (business processes), QUEST (labor, fluids, automated storage),
ProModel (manufacturing systems), and the list goes on…
Also possible to run simulation with standard programming
languages such as Java, C/C++, python, etc. However, all details
must be programmed…
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Readings
• Chapter 1 from Discrete Event System Simulation, 5th Edition,
Banks et al. (Mandatory)
• Chapter 4 (Simulation Software) from Discrete Event System
Simulation, 5th Edition, Banks et al. (Optional)
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