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— The Möbius framework is an environment for supporting multiple modeling formalisms and solution techniques. Models expressed in formalisms that are compatible with the framework are translated into equivalent models using Möbius framework components. This translation preserves the structure of the models, allowing efficient solutions. The framework is(More)
Despite the development of many modeling formalisms and model solution methods, most tool implementations support only a single formalism. Furthermore , models expressed in the chosen formalism cannot be combined with models expressed in other formalisms. This monolithic approach both limits the usefulness of such tools to practitioners, and hampers(More)
Predicting the performance and dependability of modern computer and communication systems has become increasingly difficult due to the complexity of such systems. In order to obtain accurate measures of a system's performance and dependability, it is often necessary to have detailed models of the system's components, which vary in nature (e.g., hardware,(More)
Möbius is an extensible framework for system modeling. Models can be designed using several modeling languages, which are called formalisms. Multiple solution methods, including discrete-event simulation and state-space solvers, are available. Some systems are modeled with a combination of several models. There are two methodologies for supporting(More)
Möbius is a framework for building extensible modeling tools that support model specification in multiple modeling formalisms as well as many different model solution methods. Heterogeneous models appear to be the only reasonable approach to modeling large systems that cross over many different application domains. The key to the Möbius tool is an abstract(More)
iii To my parents iv ACKNOWLEDGEMENTS I would like to thank my thesis advisor, Professor William H. Sanders. He gave me the opportunity to be a research assistant despite my lack of prior experience in distributed systems. He has always been willing to give advice and offer his technical ingenuity. He has managed to keep me focused on the objectives of the(More)
Realistic computer systems are hard to model using state-based methods because of the large state spaces they require and the likely stiffness of the resulting models (because activities occur at many time scales). One way to address this problem is to decompose a model into submodels, which are solved separately but which exchange results. We call modeling(More)
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