Abstract
Model-based evaluation is extensively used to estimate the performance and reliability of dependable systems. Traditionally, these systems were small and self-contained, and the main challenge for model-based evaluation has been the efficiency of the solution process. Recently, the problem of specifying and maintaining complex models has increasingly gained attention, as modern systems are characterized by many components and complex interactions. Components share similarities, but at the same time, also exhibit variations in their behavior due to different configurations or roles in the system. From the modeling perspective, variations lead to replicating and altering a small set of base models multiple times. Variability is taken into account only informally, by defining a sample model and explaining its possible variations. In this article, we address the problem of including variability in performability models, focusing on stochastic activity networks (SANs). We introduce the formal definition of stochastic activity networks templates (SAN-T), a formalism based on SANs with the addition of variability aspects. Differently from other approaches, parameters can also affect the structure of the model, like the number of cases of activities. We apply the SAN-T formalism to the modeling of the backbone network of an environmental monitoring infrastructure. In particular, we show how existing SAN models from the literature can be generalized using the newly introduced formalism.
Highlights
F ORMAL methods have been extensively used to estimate the performance and reliability metrics of computer systems
We propose a new formalism based on stochastic activity networks (SANs) [9] that we call stochastic activity network templates (SAN-Ts)
The work in this article enables the application of the TMDL framework with SANs, since we introduce here a templatelevel formalism based on SANs and the associated concretize function
Summary
F ORMAL methods have been extensively used to estimate the performance and reliability metrics of computer systems. In the dependability [5] and performability [6] domain, many works have proposed approaches to automatically generate formal models from design models (e.g., UML models) enriched with information on the failure/repair processes of components The idea behind these works is that software and systems engineers can take advantage of formal models without being proficient in them, because model transformations embed the knowledge of experts in an automated “push-a-single-button” tool [7], [8]. While these approaches succeed in providing an applicationspecific abstraction to users of a certain domain, they are not flexible enough to relieve dependability experts from the effort of modeling complex systems.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have