The Palladio component model for model-driven performance prediction

By Steffen Becker, Heiko Koziolek, Ralf H. Reussner. Journal of Systems and Software, 2009

796 citations recorded by the source corpus.

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 independently from their later context to enable reuse. Thus, extra-functional properties of components need to be specified in a parametric way to take different influencing factors like the hardware platform or the usage profile into account. Our approach uses 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. This paper presents 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 is sufficient to support the evaluation of architectural design decisions.

Subfields: Artificial Intelligence, Computer Networks and Communications, Software

View Paper: https://www.semanticscholar.org/paper/8e74ef636bc443678a94cfacfe80669226b116b4

How researchers see this paper

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In Artificial Intelligence

1. Mark Staples
2. Ingo M. Weber
3. Xiwei Xu

In Computer Networks and Communications

1. Mark Staples
2. Ingo M. Weber
3. Xiwei Xu

In Software

1. Mark Staples
2. Ingo M. Weber
3. Xiwei Xu

Citations in the corpus

• Architecture for Blockchain Applications (2019, methodology): “In this chapter, we have used the Palladio workbench (Becker et al. 2009) for architecture modelling of the latency of blockchain-based systems.”