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GENERATION OF TRANSITION CLASS MODELS FROM FORMAL QUEUEING NETWORK DESCRIPTIONS

A main feature of a novel tool is described, the algorthmic translation of formal queueing network descriptions into transition class models. Transition classes are a highlevel modelling paradigm for the structured and compact denition of Markov chains. Transition class models can be solved with many known solution techniques for Markov chains, exact and approximate, and simulation, even hybrid analysis is possible. The queueing network description language and the translator are extensible with respect to new network features, for example new node types. The tool is extensible with respect to new modelling paradigms and new solvers. Models can be multi-paradigm.