Compositional generation of software architecture performance QN models

Abstract

Early performance analysis based on queueing network models (QNM) has been often proposed to support software designers during the software development process. These approaches aim at addressing performance issues as early as possible in order to reduce design failures. All of them try to adapt to software systems the well-known system performance analysis methodology. This implies that they assume at design time the availability of information about the hardware platform the software runs on. In recent years, we have proposed a methodology that allows quantitative reasoning on software aspects without considering hardware aspects. In this work, we extend our methodology to encompass a compositional approach to performance analysis of software architecture described by means of UML 2.0 diagrams. The main improvements include the characterization of architectural patterns and of their corresponding QNM pattern; the use of multi-chain queueing network as system target model and the identification of the information needed to parameterize the system model.