system modelling and simulation
Semester : VI
Course Code : 18CS645
CIE Marks : 40 SEE Marks : 60
- System Software And Compilers
- computer graphics and visualization
- Web Technology And Its Applications
- data mining and data warehousing
- object oriented modelling and design
- cloud computing and its applications
- advanced JAVA and J2EE
- system modelling and simulation
- mobile application development
- introduction to DATA structures and algorithm
- programming in JAVA
- Introduction to operating system
SYSTEM MODELLING AND SIMULATION
18CS645
SYLLABUS
Module-1
Introduction: When the simulation is the appropriate tool and when it is not appropriate, Advantages and disadvantages of Simulation; Areas of application, Systems and system environment; Components of a system; Discrete and continuous systems, Model of a system; Types of Models, Discrete-Event System Simulation Simulation examples: Simulation of queuing systems. General Principles.
Textbook 1: Ch. 1, 2, 3.1.1, 3.1.3
Module-2
Statistical Models in Simulation: Review of terminology and concepts, Useful statistical models, Discrete distributions. Continuous distributions, Poisson process, Empirical distributions.
Queuing Models: Characteristics of queuing systems, Queuing notation, Long-run measures of performance of queuing systems, Long-run measures of performance of queuing systems cont…, Steady-state behaviour of M/G/1 queue, Networks of queues,
Textbook 1: Ch. 5,6.1 to 6.3, 6.4.1,6.6
Module-3
Random-number generation: Properties of random numbers; Generation of pseudo-random numbers, Techniques for generating random numbers, Tests for Random Numbers, Random-Variate Generation: Inverse transform technique Acceptance-Rejection technique.
Textbook 1: Ch. 7,8.1, 8.2
Module-4
Input Modeling: Data Collection; Identifying the distribution with data, Parameter estimation, Goodness of Fit Tests, Fitting a non-stationary Poisson process, Selecting input models without data, Multivariate and Time-Series input models.
Estimation of Absolute Performance: Types of simulations with respect to output analysis, Stochastic nature of output data, Measures of performance and their estimation, Contd.
Textbook 1: Ch. 9, 11.1 to 11.3
Module-5
Measures of performance and their estimation, Output analysis for terminating simulations Continued.., Output analysis for steady-state simulations.
Verification, Calibration And Validation: Optimization: Model building, verification and validation, Verification of simulation models, Verification of simulation models, Calibration and validation of models, Optimization via Simulation.
Textbook 1: Ch. 11.4, 11.5, 10