During the last decade, researchers have proposed a number of model transformations enabling performance predictions. These transformations map performance-annotated software architecture models into stochastic models solved by analytical means or by simulation. However, so far, a detailed quantitative evaluation of the accuracy and efficiency of different transformations is missing, making it hard to select an adequate transformation for a given context. This paper provides an in-depth comparison and quantitative evaluation of representative model transformations to, e.g., queueing petri nets and layered queueing networks. The semantic gaps between typical source model abstractions and the different analysis techniques are revealed. The accuracy and efficiency of each transformation are evaluated by considering four case studies representing systems of different size and complexity. The presented results and insights gained from the evaluation help software architects and performance engineers to select the appropriate transformation for a given context, thus significantly improving the usability of model transformations for performance prediction.

Queueing Petri nets are a powerful formalism that can be exploited for modeling distributed systems and evaluating their performance and scalability. By combining the modeling power and expressiveness of queueing networks and stochastic Petri nets, queueing Petri nets provide a number of advantages. This tutorial presents an introduction to queueing Petri nets first introducing the modeling formalism itself and then summarizing the results of several modeling case studies which demonstrate how queueing Petri nets can be used for performance modeling and analysis. As part of the tutorial, we present QPME (Queueing Petri net Modeling Environment), an open-source tool for stochastic modeling and analysis of systems using queueing Petri nets. Finally, we briefly present a model-to-model transformation automatically generating a queueing Petri net model from a higher-level software architecture model annotated with performance relevant information.

Performance predictions early in the software development process can help to detect problems before resources have been spent on implementation. The Palladio Component Model (PCM) is an example of a mature domain-specific modeling language for component-based systems enabling performance predictions at design time. PCM provides several alternative model solution methods based on analytical and simulation techniques. However, existing solution methods suffer from scalability issues and provide limited flexibility in trading-off between results accuracy and analysis overhead. Queueing Petri Nets (QPNs) are a general-purpose modeling formalism, at a lower level of abstraction, for which efficient and mature simulation-based solution techniques are available. This paper contributes a formal mapping from PCM to QPN models, implemented by means of an automated model-to-model transformation as part of a new PCM solution method based on simulation of QPNs. The limitations of the mapping and the accuracy and overhead of the new solution method compared to existing methods are evaluated in detail in the context of five case studies of different size and complexity. The new solution method proved to provide good accuracy with solution overhead up to 20 times lower compared to PCM's reference solver.