Basic Simulation Modelling. Systems, Models and Simulation. Different simulation types. Monte Carlo Simulation. Queueing system simulation. Modeling complex systems. Simulation Software (Matlab, ns-2/3, Opnet, OmNET ++, NetSim). Selecting input probability distributions. Generating random numbers and random variates. Statistical analysis of simulation output: Means, variances, confidence intervals etc). Simulation of Communication Systems and Networks. Verification, validation, and accreditation through Simulation.

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

• to understand the main modeling and simulation issues.
• to perceive and utilize Monte Carlo technique.
• to develop programming techniques using event-driven simulation.
• to design and produce random variables and input probability distribution.
• to perform I/O statistical analysis in simulation frameworks.
• to simulate main communication network protocols.
• to manage the development of simulation programs.
• to evaluate and collect simulation performance metrics.

None

Lectures, Labs, Lab Tasks, Semester Project

Written final exam (60%), Lab Tasks (30%), Project Presentation (10%), Oral exam (20%)

• Ρουμελιώτης, Σουραβλάς, Τεχνικές Προσομοίωσης, Εκδόσεις Τζιόλα, 978-960-418-372-2 2011.
• Β. Τσαουσίδης, Ε. Μαμάτας, Ι. Ψαρράς, Ε. Κοσμίδης, Σ. Δημητρίου, Εργαστηριακά Μαθήματα στα Δίκτυα και Διαδίκτυα Υπολογιστών, Εκδόσεις Κλειδάριθμος, 2010.
• A. M. Law W. D. Kelton, “Simulation Modeling and Analysis, McGraw-Hill, Inc, 1991.
• H. Perros, “Computer Simulation Techniques - The Definitive Introduction", 2003.