Composing hierarchical stochastic model from SysML for system availability analysis

Abstract

Comprehensive analytic model for system availability analysis often confronts the largeness issue where a system designer cannot easily handle the model and the solution is not given in a feasible solution time. Hierarchical decomposition of a large state-space model gives a promising solution to the largeness issue when the model is decomposable. However, the decomposability of analytic model is not always manually tractable especially when the model is generated in an automated manner. In this paper, we propose an automated model composition technique from a system design to a hierarchical stochastic model which is the judicious combination of combinatorial and state-space models. In particular, from SysML-based system specifications, a top-level fault tree and associated stochastic reward nets are automatically generated in hierarchical manner. The obtained hierarchical stochastic model can be solved analytically considerably faster than monolithic state-space models. Through an illustrative example of three-tier web application system on a virtualized infrastructure, the accuracy and efficiency of the solution are evaluated in comparison to a monolithic state space model and a static fault tree.