ABSTRACT
Discrete-event modeling and simulation is used to create predictions of the system states during time intervals, which can be modified to examine what and if situations. In stochastic discrete-event simulation paradigm, the dynamics of a complex system are captured by discrete state variables, where an event is a combined process of a large number of state transitions between a set of state variables accomplished within the event execution time. The key idea is to segregate the complete state space into a disjoint set of independent events that can be executed simultaneously without any interaction. The application of discrete event-based system modeling techniques in large-scale computer and communication networks has demonstrated the accuracy of this approach for higher order system dynamics within the limits of input data, state partitioning algorithms, uncertainty of information propagation, and highly mobile entities. Discrete-event modeling is a mathematical procedure that is created to describe a dynamic process.
