ELC 708: Modelling and Simulations

Fall 11, 12, 13

The course introduces graduate students to performance evaluation techniques of computer systems. While the course mainly focuses on analytic approaches for performance evaluation, it introduces the students to simulation techniques and practices as a common approach for highly complex systems. The main addressed topics include using funmental probalistic concepts for establishing system models using Discrete and Continuous Random Variables in both standard and transform RV representation. Indepth coverage for important operations including random sum of random variables. Markov Process (continuous and discrete, state classification, transient and steady state analysis for irreducable and absorbing chains, Embedded Markov Chain). Birth and death models (M/M/1 queuing System - M/M/m Queuing System - M/G/1 Queuing System). Finally, relevant topics such as Phase type modeling and prformance evaluation, Markov Reward theory, and Complex analysis of distribution transforms are introduced towards the end of the course