Model-Based performance prediction with the palladio component model

TLDR

Component‑based software engineering seeks to predict extra‑functional properties such as performance, but existing theories are still maturing and require parametric specification to account for hardware and usage factors. This paper introduces a Palladio‑based model and a simulation tool that enable performance predictions for component‑based architectures. The approach uses the Palladio Component Model to describe architectures parametrically, supporting the CBSE development process by distributing model creation among developer roles, and implements a simulation tool that performs the predictions. A case study demonstrates that the predictions achieve sufficient accuracy to inform architectural design decisions.

Abstract

One aim of component-based software engineering (CBSE) is to enable the prediction of extra-functional properties, such as performance and reliability, utilising a well-defined composition theory. Nowadays, such theories and their accompanying prediction methods are still in a maturation stage. Several factors influencing extra-functional properties need additional research to be understood. A special problem in CBSE stems from its specific development process: Software components should be specified and implemented independent from their later context to enable reuse. Thus, extra-functional properties of components need to be specified in a parametric way to take different influence factors like the hardware platform or the usage profile into account. In our approach, we use the Palladio Component Model (PCM) to specify component-based software architectures in a parametric way. This model offers direct support of the CBSE development process by dividing the model creation among the developer roles. In this paper, we present our model and a simulation tool based on it, which is capable of making performance predictions. Within a case study, we show that the resulting prediction accuracy can be sufficient to support the evaluation of architectural design decisions.