CAREER: Composition Approaches for the Analysis of Complex Systems

点击翻译按钮获取中文摘要

Technology advancements are producing increasingly complex systems, that are often expected or required to meet performance demands and yet be extremely reliable and secure. As a result, engineers are increasingly utilizing computer tools to analyze high-level models of a system during its design. Uncertainties, such as device failures or arrivals of requests, lead to stochastic events within the model. Automated computer tools can numerically analyze the stochastic process described by the model to obtain performance measures of the system model, and can utilize implicit data structures to represent much larger stochastic processes than would be possible otherwise. Alternatively, discrete-event simulation can be used to produce confidence intervals for the performance measures.This NSF CAREER research process project extends the applicability of high-level system models by generalizing the types of models supported by implicit representations to include a unified hierarchy in which a model event may itself be described by another more detailed model. This allows a complex system model to be constructed out of several simple components. The goal is to use implicit methods to control the state space explosion that has plagued analysis of complex systems. Techniques to construct and exploit the model hierarchy are being developed and implemented by this project in software libraries and tools. These techniques include numerical analysis methods that use implicit representations, discrete-event simulations, and hybrid approaches that unify numerical analysis methods with discrete-event simulation. The project is developing tools and technology that will enable the analysis of complex systems and impact important areas of computer systems research, including complex real-time embedded systems, Peer-to-Peer networks, safety-critical systems, agent-based systems, and software verification for high-confidence systems.The CAREER research program is closely integrated with educational efforts through assignments, projects, and lectures that are introduced into courses on discrete-event simulation and analysis of stochastic processes.