Kangsun Lee

OOPM/RT: a multimodeling methodology for real-time simulation

When we build a model of real-time systems, we need ways of representing the knowledge about the system and also time requirements for simulating the model. Considering these different needs, our question is “How can we determine the optimal model that simulates the system by a given deadline while still producing valid outputs at an acceptable level of detail?” We have designed OOPM/RT (Object-Oriented Physical Modeler for Real-Time Simulation) methodology. The OOPM/RT framework has three phases: (1) Generation of multimodels in OOPM using both structural and behavioral abstraction techniques, (2) Generation of AT (Abstraction Tree) which organizes the multimodels based on the abstraction relationship to facilitate the optimal model selection process, and (3) Selection of the optimal model that guarantees the deliver simulation results by the given amount of time. A more-detailed model (low abstraction model) is selected when we have enough time to simulate, while a less-detailed model (high abstraction model) is selected when the deadline is immediate. The basic idea of selection is to trade structural information for a faster runtime while minimizing the loss of behavioral information. We propose two possible approaches for the selection: an integer-programming-based approach and a search-based approach. By systematically handling simulation deadlines while minimizing the modelers interventions, OOPM/RT provides an efficient modeling environment for real-time systems.

Dynamic model abstraction

While complex behavior can be generated through simple systems, as in chaotic and nonlinear systems, complex systems axe found where a systems study contains multiple physical objects and interactions. Through the use of hierarchy, we are able to simplify and organize the complex system. Every level within the hierarchy may be refined into another level. System abstraction involves simplification through structural system representation as well as through behavioral approximations of executed model structure. There has been little work on creating a unified taxonomy for model abstraction. We present such a taxonomy and define two major sub-fields of model abstraction, while illustrating both sub-fields through detailed examples. The introduction of this taxonomy provides system and simulation researchers with a way in which to view and manage complex systems.

Semiautomated method for dynamic model abstraction

As complex models are used in practice, modelers require efficient ways of abstracting their models. Through the use of hierarchy, we are able to simplify and organize the complex system. The problem with the hierarchical modeling is that system components in each level are dependent on the next- lowest level so that we are unable to run each level independently. We present a way to augment hierarchical modeling where abstraction can take place on two fronts: structural and behavioral. Our approach is to use structural abstraction in order to organize the system hierarchically, and then apply behavioral abstraction to each level in order to approximate lower levels behavior so that it can be executed independently. The proposed abstraction method is done by semi-automatic way and gives advantages to view and analyze complex systems at different levels of abstraction.

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 models 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) IP (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.