Credits and Contact Hours

3 credits, 43 hours

Course Instructor Name

Dr. Mohammad Awad, Prof. Hamid Al-Azemi

Textbook

• Leon Garcia, Probability, Statistics, and Random Processes for Electrical Engineering, 3rd edition, Prentice Hall, 2008, ISBN 0-13-147122-8
• Reference
• G. Bolch et al., Queuing Networks and Markov Chains, 2nd edition, Wiley- Interscience, 2005.

Catalog Description

Theory and application of analytic methods for evaluating the performance and for capacity planning of computer networks. Review of the basic probability theory. Advanced methods in probabilistic analysis. Random processes. Markovian queuing models. Network protocols. Traffic modeling. Event-driven simulation.

Prerequisite

CpE-356

Specific Goals for the Course

Upon successful completion of this course, students will be able to:

• Learn and know how to apply Little's result. (Student outcomes: 1)
• Learn the definitions and physical meanings of the different performance measures used in the modeling of computer network.s (Student outcomes: 1)
• Obtain values of the different performance measures given a steady state distribution or from given data measurements. (Student outcomes: 1)
• Derive the performance measures either from the generating functions of the different distributions or derive the generating functions of given distributions. (Student outcomes: 1)
• Model either simple discrete time systems using Markov chains or simple continuous time systems using Markov processes and solve for the steady state probabilities. (Student outcomes: 1)
• Understand and be able to use birth-death systems to model computer systems and networks. (Student outcomes: 1)
• Use simple Markovian queues to model computer systems and networks. (Student outcomes: 1, 2)
• Understand the different random number generation techniques, and be able to apply them to generate samples from any given distribution. (Student outcomes: 1)
• Design and implement either a continuous or discrete event simulation. (Student outcomes: 2)
• Conduct simulation experiments and interpret the results. (Student outcomes: 2)

Topics to Be Covered

• Introduction to modeling, probability theory, combinatorics, random variables and stochastic processes; Little's result.
• Simulation techniques: random number generation; time-driven simulation; event-driven simulation.
• Markovian models: discrete Markov chains, and continuous Markov processes.
• Single queues: birth-death processes; M/M/1, M/M/*, M/M/1/L/M queues.