Quantitative Evaluation of Model-Driven Performance Analysis and Simulation of Component-Based Architectures

Abstract

Abstract-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 means of analytical or numerical analysis or by system 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.

Scaling Size and Parameter Spaces in Variability-Aware Software Performance Models (T)

“…Here, the main restriction is the lack of support for fork/join synchronization barriers, as in [17], [3] (see also [9] for a more general discussion on this). Fig.…”

Control Strategies for Self-Adaptive Software Systems

“…Following a greybox approach, only the details relevant for the specific adaptation concern are modeled. As an example, performance-based adaptation typically relies on performance prediction approaches for component-based architectures, which in turn rely on performanceannotated software architecture models [Balsamo et al 2004;Brosig et al 2015]. Architectural models are either directly employed or transformed into stochastic models (e.g., Queuing Networks [Tribastone 2013], Petri Nets [Ding et al 2014], Markov models [Calinescu et al 2012]) and used in the specification of the adaptation logic of the system.…”

Energy Consumption Analysis and Design of Energy-Aware WSN Agents in fUML

“…Up to day this proof is not provided for most of the transformation in literature, but it is leaved to intuition. Finally, in literature, we can find some example of multi-dimensional analysis environment, such as Palladio [10], using proprietary modeling notations. Differently from them, we do not use own notations, but UML that is a standard de-facto modeling language hence the impact of AMF is wider and more general.…”

“…Set as body of a UML Opaque action 10. Both state machine and sequence diagram are not part of the fUML model and it is used for explanation purposes.…”