Throughput approximation of decision free processes using decomposition

We present an approach for the efficient approximation of the throughput of decision free processes, a class of stochastic process algebra models. Stochastic process algebras are modeling formalisms which are based on communicating sequential processes, in contrast to stochastic Petri nets which focus on causality and concurrency. The algorithm we are using is based on model decomposition at the specification level of stochastic process algebras and has been adopted from marked graphs, a well known sub-class of Petri nets. It works in a divide and conquer fashion and it is able to reduce the size of the state space by more than one order of magnitude while the deviation of the exact result is relatively low.