mca Syllabus

Code: CS 704F
Credits: 3
Module I [12]
1. The notion of system, model, simulation. Types of simulations. Illustrative examples. Conceptual and computer models. Verification and validation of models. Simulation experiment. Simulation project life cycle. Description of simulation models. Structure vs. behaviour models. Classification of tasks solved within the modeling and simulation process. Detailed example introduction: database server as a typical queuing system.

2. Description of discrete-event systems behaviour. Modeling of time. The notion of status, event, activity, process and their interdependencies. Object-oriented model design. Simulation time, control of time advancement, event list. Event driven simulation algorithm. Detailed example: implementation of the database server as a queuing system.

3. Random numbers in simulation. Random variables with discrete and continuous probability distribution. Pseudo-random generators. Multiplicative and additive congruential method. Nonuniform random numbers.

Module II [10]
1. Testing of pseudo-random generators. Monte Carlo method. Precision. Queueing systems. Entities: queues, service facilities, storages. Properties of input and output stream. Kendall classification of queueing systems. Entity behaviour and statistical data sampling during the simulation run.
2. Discrete and continuous Markov model. Birth -Death processes.
3. Steady-state queueing systems of types M/M/1, M/M/? , M/M/m, M/Er/1, Er/M/1 and their variants.

Module III [10]
1. Models M/G/1, G/M/1, G/M/m, G/G/1, G/D/1, M+D/D/1. Closed systems and queueing networks.
2. Simulation languages for discrete-event systems. Case study and comparison: Simscript, GPSS, SOL,

Module IV [13]
1. Case study and comparison: Simula 67. Object oriented design and implementation of simulation models. Persistence of objects in C++, case studies. Application in a simulation system.
2. Simulation experiments. Preparation and pre-processing of input data. Statistical data collected during the simulation run. Time dependency of statistics. Histograms. Evaluation and interpretation of results. Model validation and verification.
3. Simulation of digital systems. Abstractions levels of digital system description. Models of signals and functions. Structure vs. behaviour. Models of components. Models of delays.
4. Digital systems simulators - methods of implementation. Flow of simulation time. Synchronous and asynchronous algorithm of digital systems simulation. Acceleration of simulation run.
5. Register-transfer level simulation. Simulation languages of HDL type. VHDL language and tools. Implementation of concurrent statements and processes in VHDL. Modeling of time and event list.

Textbooks:
1.Law, A.M., Kelton, W.D.: Simulation Modeling and Analysis. McGraw-Hill, New York, 2-nd edition, 1991. ISBN 0-07-100803-9.
2. Basmadjian, Mathematical Modeling of Physical Systems, OUP
3. Brewmaud, Markov Chains; With Gibbs Field , Monte Carlo Simulation & Ques, Springer Verlag
4.Hoover,S.V., Perry,R.F.: Simulation: a Problem-Solving Approach. Addison - Wesley, 1990. ISBN 0-201-16880-4.
5.Zeigler,B.P.: Theory of Modeling and Simulation. John Wiley, New York,1976. Re-published Krieger Publ., Malabar, 1985.
6.Fishwick,P.A.: Simulation Model Design and Execution: Building Digital Worlds. Prentice Hall, Englewood Cliffs,1995.
7.Kleinrock, L.: Queuing Systems Vol.I, Vol.II, Wiley & Sons, London, 1975.
8. First Course in Mathematical Modeling, Giordano, Vikas