CPSC 531: Introduction to Systems
Modeling and Simulation
Instructor: Anirban Mahanti
Office: ICT 745
Email: mahanti@cpsc.ucalgary.ca
Class Location: TRB 101
Lectures: TR 15:30 – 16:45 hours
Class web page:
http://pages.cpsc.ucalgary.ca/~mahanti/teaching/F05/CPSC531/
Notes derived from “Simulation Modeling and
Analysis” by A. Law and W. Kelton, Third
edition, McGraw Hill, 2000.
CPSC 531: Introduction
1
What is this course about?
 This course is primarily about imitating the
operation of real-world systems using
computer programs. Our focus will be on
“discrete-event” simulations. We will learn
how to:
Abstract real-world systems into models
 Implement models using software
 Experiment design

 Systems modeling requires understanding of
 Basic probability, statistics, elementary calculus
 We will also talk about analytically solving
models
CPSC 531: Introduction
2
Tentative topics and schedule
 Introduction (1 week)
 General Principles and Examples (1.5 weeks)
 Statistical Models (1.5 weeks)
 Queuing Models (2 weeks)
 Generating Random-Numbers (1 week)
 Generating Random-Variates (1 week)
 Input Modelling (1 week)
 Output Data Analysis (1 week)
 Experiment Design and Analysis (1.5 weeks)
 Simulation of Computer Systems (1 week) - time
permitting
CPSC 531: Introduction
3
Books and software
 Lecture material primarily drawn from:
Discrete-Event System Simulation (Fourth
Edition), Banks, Carson, Nelson, and Nicol,
Prentice-Hall, 2005. Henceforth, referred to as
[BCNN05].
 Simulation Modeling and Analysis (Third Edition),
Law and Kelton, McGraw Hill, 2000. Henceforth,
referred to as [LK00].
 Other texts – see class web pages.

 Software: C/C++, Unix, CSIM simulation tool
 Learn CSIM on your own; TAs will provide help.
CPSC 531: Introduction
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Assignment, exams, and grading
 Four assignments – programming in
C/C++/CSIM, problem-solving questions

Need to cumulatively obtain a passing grade in the
assignment component of the course
 Midterm examination – closed book/notes
 Final examination – closed book/notes
 Need to obtain a passing grade in the final exam
 Grading – moderate (class average ~ B?)
 Conclusion – need to work hard; read the
text; attend classes and tutorials; learn a lot!
CPSC 531: Introduction
5
Basics of Modeling and Simulation
 What is simulation?
 How to study a system?
 When to use simulations?
 Application areas of simulation
 Terminology – system, state, events
 Model classification
 Types of simulation
 Steps in a simulation study
 Advantages, disadvantages, and pitfalls in a
simulation study
CPSC 531: Introduction
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What is a simulation?
 Simulation – is imitation of the operation of a
facility or process, usually using a computer
Facility being simulated is also called a “system”
 Assumptions/approximations, both logical and
mathematical, are made about how the system
works
 These assumptions form a model of the system

 Models have many applications and can answer
questions such as:
Why does my Web performance suffer when my
room-mate starts using the WiFi connection?
 What will be the path of a hurricane? Etc.

CPSC 531: Introduction
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How to study a system?
CPSC 531: Introduction
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When to use simulations?
 Simulations can be used:
 To study complex system, i.e., systems where
analytic solutions are infeasible.
 To compare design alternatives for a system that
doesn’t exist.
 To study the effect of alterations to an existing
system. Why not change the system??
 To reinforce/verify analytic solutions.
 Simulations should not be used:

If model assumptions are simple such that
mathematical methods can be used to obtain exact
answers (analytical solutions)
CPSC 531: Introduction
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Application Areas
 Design and performance evaluation of computer systems



Determining hardware requirements or protocols for
communications networks
Studying CPU Scheduling algorithms
Evaluation of Web caching policies
 Design and analysis of manufacturing systems

Operation of a production line
 Evaluating designs for service organizations

Study call centers, fast-food restaurants, hospitals, and post
offices
 Evaluating military weapons systems or their logistics
requirements
 Designing and operating transportation systems such as
airports, freeways, ports, and subways
 Analyzing financial or economic systems
CPSC 531: Introduction
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Terminology

System: A collection of objects that act and
interact together toward some logical end


State of a system: Collection of variables and their
values necessary to characterize a system at a
particular time



E.g. Determine number of cashiers needed to provide
express service to customers with 10 or less item at a
superstore – system consists of express cashiers and
customers with 10 or less items
Might depend on desired objectives, performance measures
SS Example: # of express cashiers, time of arrival of
customers with 10 or less items, etc.
Event: A change in system state

Customer arrival, start of service, and customer departure
CPSC 531: Introduction
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Model Classification
[Jain91]
 Continuous-time vs. discrete-time models
 Continuous-event vs. discrete-event models
 Deterministic vs. probabilistic models
 Static vs. dynamic models
 Linear vs. non-linear models
 Open vs. closed models
CPSC 531: Introduction
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Continuous and Discrete-time models
Number of
students
in cpsc 531
Number
of cust.
in queue
Time
(a) Continuous-time
Time
(b) Discrete-time
CPSC 531: Introduction
13
Continuous and Discrete-event models
Distance
traveled
by plane
Number
of cust.
in queue
Time
(a) Continuous-event
Time
(b) Discrete-event
CPSC 531: Introduction
14
Deterministic and Stochastic Models
 Deterministic models produce deterministic
results
 Stochastic or probabilistic models are
subject to random effects
Typically, they have one or more random inputs
(e.g., arrival of customers, service time etc.).
 Outputs from stochastic models are “estimates”
of the true characteristics of the system
 Need to repeat experiments number of times
 Need to have confidence in the results

CPSC 531: Introduction
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More on models
 Static and dynamic models
 Static models – system state independent of time
 Dynamic models - system state change with time
 Linear and non-linear models

Linear models – output is a linear function of input
parameters
 Open and closed models
(a) Open Model
(b) Closed Model
CPSC 531: Introduction
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Types of Simulation (1/2)
 Monte Carlo simulation
 No time element (usually)
 Used for evaluating non-probabilistic expressions
(e.g., an integral) using probabilistic methods
 Wide variety of mathematical problems
 Trace-driven simulation
 Extensively used in computer systems
performance evaluation; e.g., paging algorithms
 Advantages: credibility, easy validation, less
randomness, accurate workload
 Disadvantages: complexity, only a snap-shot,
representative?, single point of validation
CPSC 531: Introduction
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Types of Simulation (2/2)
 Monte
Carlo simulation
 Trace-driven simulation
 Discrete-event simulation: a simulation using
a discrete-event (also called discrete-state)
model of the system
 E.g.,
Widely used for studying computer systems
 Continuous-event simulation: uses a
continuous-state models

E.g., Widely used in chemical/pharmaceutical
studies
 Our focus will be on
discrete-event systems.
CPSC 531: Introduction
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Steps in a Simulation Study
CPSC 531: Introduction
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Advantages, disadvantages, and
pitfalls in a simulation study
 Advantages
 Simulation allows great flexibility in modeling complex
systems, so simulation models can be highly valid
 Easy to compare alternatives
 Control experimental conditions
 Can study system with a very long time frame
 Disadvantages
 Stochastic simulations produce only estimates – with noise
 Simulation models can be expensive to develop
 Simulations usually produce large volumes of output – need
to summarize, statistically analyze appropriately
 Pitfalls
 Failure to identify objectives clearly up front
 Inappropriate level of detail (both ways)
 Inadequate design and analysis of simulation experiments
 Inadequate education, training
CPSC 531: Introduction
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