Fault-Tolerant Systems Analysis:  Techniques for Model      Generation and Solutions
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{'SignBlockName': 'Yechezkel Zalcstein', 'PO_EMAI': 'zzalcste@nsf.gov', 'PO_PHON': '7032920000'}
Problems arising during the analysis of fault tolerant systems can be           broadly classified into those relating to model construction and those          relating to model solution.  This research is developing new analytical         techniques that will aid in the solution of both types of problems, by          investigating the use of two different modeling languages, fault trees          and extended stochastic Petri Nets.  Fault trees have been                      traditionally used to assess the reliability and failure modes of life          critical systems, but are severely limited in the ability to model the          dynamic behavior intrinsic to fault tolerant systems.  The methodology          under development will address this limitation by defining new solution         methods that allow the consideration of such dynamic behavior within            the fault tree model.  Thus, a modeler may take advantage of fault              trees' concise representation that eases the model construction task,           without experiencing the limitation in modeling power that is                   associated with traditional fault tree models.                                                                                                                  Extended stochastic Petri net (ESPN) models are able to model behavior          that is intrinsically concurrent, asynchronous and nondeterministic,            and thus are also a valuable model construction tool.  An ESPN model            can be used to capture the dynamic behavior that is absent from the             fault three model.  The behavioral decomposition technique is then used         to combine the models together.  Also, the development of DEEP (the             Duke ESPN Evaluation Package) which addresses both the construction of          a valid ESPN model as well as its solution, will enable more general            use of the ESPN modeling language.
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