Distributed Semantics and Implementation for Systems with Interaction and Priority

The paper studies a distributed implementation method for the BIP (Behavior, Interaction, Priority) component framework for modeling heterogeneous systems. BIP offers two powerful mechanisms for describing composition of components by combining interactions and priorities. A system model is layered. The lowest layer contains atomic components; the second layer, describes possible interactions between atomic components; the third layer includes priorities between the interactions. The current implementation of BIP is based on global state operational semantics. An Engine directly interprets the operational semantics rules and computes the possible interactions between atomic components from global states. The implementation method is a translation from BIP models into distributed models involving two steps. The first translates BIP models into partial state models where are known only the states of the components which are ready to communicate. The second implements interactions in the partial state model by using message passing primitives. The main results of the paper are conditions for which the three models are observationally equivalent. We show that in general, the translation from global state to partial state models does not preserve observational equivalence. Preservation can be achieved by strengthening the premises of the operational semantics rules by an oracle. This is a predicate depending on the priorities of the BIP model. We show that there are many possible choices for oracles. Maximal parallelism is achieved for dynamic oracles allowing interaction as soon as possible. Nonetheless, these oracles may entail considerable computational overhead. We study performance trade-offs for different types of oracles. Finally, we provide experimental results illustrating the application of the theory on a prototype implementation.