Efficient Evaluation of CSAN Models by State Space Analysis Methods

We have recently introduced a high-level extension for stochastic activity networks (SANs) called coloured stochastic activity networks (CSANs). CSANs have several distinguishing properties, which make them quite appropriate for modeling and evaluation of software performance and dependability. CSANs have introduced a construct called coloured place for data manipulation. A coloured place holds a list of tokens of a userdefined token type. CSAN models can be evaluated by state space analysis techniques or discrete-event simulation. However, their state spaces will become very large, even for a small CSAN model. For efficient evaluation of these models by state space analysis methods, we will introduce measure-adaptive state space analysis process in this paper. Based on this method, it is possible to construct high-level CSAN models. However, for efficient evaluation, it is possible to generate and analyze a reduced state space based on user-specified performance or dependability measures.