A multimodeling methodology for real-time simulation

Real-time systems differ from traditional data processing systems in that they are constrained by certain nonfunctional requirements(e.g., dependability and timing). Although real-time systems can be modeled using the standard structured design methods, these methods lack explicit support for expressing the real-time constraints. Our objective is to present a modeling methodology in which the real-time systems can be modeled efficiently to meet the given simulation objective and the model's time requirements. We developed a modeling methodology that functional requirements of real-time systems are captured with multiple levels of abstraction. Our approach to guaranteeing timing constraints is to vary the level of abstraction so that the simulation can deliver the desired results within the given amount of time. Two selection approaches have been developed to determine the optimal abstraction level that achieves the best tradeoff model quality for time: 1) TP(Integer Programming)-based approach and 2) Search-based approach. A more detailed model(low abstraction level) is selected when we have ample time, while a less detailed model(high abstraction level) is used when there is an imminent time constraint. One of the contributions of our research is that with the ability to select an optimal model for a given deadline, we provide a way to handle real-time constraints for the simulation group. Also, the determined level of abstraction provides the perspective which allows modelers to configure less important components of the system for a given time-constraint situation.