King Saud University
College of Computer and Information Sciences
Department of Computer Engineering
CEN 401 – Queueing Theory and Simulation (3-1-3)
Semester II, Academic Year 2009-2010
Required Course: Time (SMW 1:00-2:00 PM)
Course Description (catalog):
Probability theory: random variables, transformation of random variable. Probability
density functions. Markov chains: stochastic processes, Poisson and Exponential
processes. Queuing Systems: Little’s theorem, M/M/1, M/M/1/K, and M/G/1.
Computer simulation: random number generators, validation tests, generating random
variables, event-driven simulation. Simulation languages and software simulation.
Prerequisites: - Courses
- Topics


STAT 324 or equivalent.
Probability and Statistics;
Knowledge of a programming language.
Textbook(s) and/or Other Required Materials:
Primary:
 Probability and Random Processes for Electrical Engineering, by Alberto LeonGarcia, Addison-Wesley.
 Discrete event system simulation, by J. Banks, J. Carson, B. Nelson and David
Nicol, Fourth Edition, Prentice - Hall.
Supplementary:
 Probability, Random Variables, and Stochastic Processes, by Papoulis and Pillai,
McGraw-Hill.
 Geoffrey Gordon, “System Simulation”, Prentice Hall.
 Narsingh Deo, “System Simulation with Digital Computer”, Prentice Hall.
Course Learning Outcomes: This course requires the student to demonstrate the
following:
1. Have a well – founded knowledge of standard distributions which can describe real
life phenomena.
2. Understand and characterize phenomena which evolve with respect to time in a
probabilistic manner.
3. Be exposed to basic characteristic features of a queuing system and acquire skills in
analyzing queuing models.
4. To provide a strong foundation on concept of simulation and modeling.
5. Understand the techniques of random number generation and randomness testing.
6. To design simulation models for various case studies like traffic flow in networks.
7. To practice on simulation tools and impart knowledge on building simulation
systems.
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Major Topics covered and schedule in weeks:
Topic
Weeks
1
Probability theory: random variables, transformation of random variable.
1
Probability density functions.
1.5
Stochastic processes: Poisson and Exponential processes.
1.5
Stationary Processes: Birth-death processes, Markov Chains.
2
Queuing Systems: Little’s theorem, M/M/1, M/M/k, M/M/∞, M/M/1/k.
1
M/G/1: Pollaczek – Khintchine formula.
1
Random number generators, validation tests.
1
Generating random variates.
1
Input modelling.
2
Event-driven simulation: simulation of M/M/1 queue.
1
Simulation languages.
Assessment Plan for the Course
Students’ performance in homework, quizzes, exams, and class-projects
Contribution of Course to Meeting Curriculum Disciplines:
Curriculum Discipline
Percentage
Mathematics and Basic Science
60
Engineering Science
10
Engineering Design
30
General Education
Relationship of Course to Program Outcomes
Outcome
Program Outcome Description
Level of
Contribution
(a)
(b)
an ability to apply knowledge of mathematics, science, and engineering
an ability to design and conduct experiments, as well as to analyze and
interpret data
(c)
an ability to design a system, component, or process to meet desired needs
within realistic constraints such as economic, environmental, social,
political, ethical, health and safety, manufacturability, and sustainability
(d)
an ability to function on multidisciplinary teams
(e)
an ability to identify, formulate, and solve engineering problems
(f)
an understanding of professional and ethical responsibility
(g)
an ability to communicate effectively
(h)
the broad education necessary to understand the impact of engineering
solutions in a global, economic, environmental, and societal context
(i)
a recognition of the need for, and an ability to engage in life-long learning
(j)
a knowledge of contemporary issues
(k)
an ability to use the techniques, skills, and modern engineering tools
necessary for engineering practice.
H=High, M= Medium, L=Low
Prepared by: Dr Nasser-Eddine Rikli.
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