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Simulation
and Modeling
CSC
587 – Spring 2010
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Syllabus
(pdf)
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Submit
your work (click
here):
you will need the credentials supplied in class to login.
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Course
Schedule
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Meeting
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Course
Topic
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4.13
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Input
modeling
Whitt's algorithm for data fitting into a
hyperexponential
Case study: network traffic modeling (ppt on
Google group)
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4.06
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Random
variate generation: inverse transform method
Full-period
multipliers
Exam assigned (see Google group): due on 04.13
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3.30
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Lester
Lipsky guest lecture on scheduling
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3.23
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Spring
recess
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3.16
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Random
number generation
Linear congruential method
Testing for
randomness: KS, Chi Square, Autocorrelation
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3.09
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Single-queue
systems
Little's law
Results for M/M/1, M/G/1 and M/G/inf
queues
Homework 5 due on 03.16: Ch. 6 # 2, 9 and
21
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3.02
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The
Erlangian distribution
Poisson random variable
Classification
of stochastic processes
The Poisson process and its
properties
Project
1 (pdf):
due on 03.16
Homework
4 due
on 03.09 (pdf)
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2.23
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A
Markov model of a computer system
The exponential distribution:
properties, order statistics
Hyperexponential
distribution
Homework 3:
Ch. 5 # 8, 19, 29. Due on 03.02
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2.16
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University
closed due to snow
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2.09
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Discussion
of discrete-event simulation
Trace-driven simulation
Review
of probability concepts: pdf, cdf and reliability function;
Mean,
variance, squared coefficient of variation, covariance,
correlation,
autocorrelation lag-k
Probability
distributions: exponential
Homework
2 (pdf):
due on 02.16
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2.02
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Fundamentals
of random number generation
Generating sample from a discrete
distribution
Discrete-event simulation concepts: virtual time,
time advance, event queue scheduling, event handling.
Simulator
components: events, event queue, event handlers, variate
generation, physical system representation.
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1.26
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Introduction
to discrete-event simulation (Chapter 1)
Homework
1: p.
22 #7
In-class exercise (txt)
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