Automated Transformation of Component-Based Software Architecture Models to Queueing Petri Nets

Abstract

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.

Rapid performance modeling by transforming use case maps to palladio component models

“…Koziolek et al [13] map PCM instances to layered queuing networks, which are then solved using the fast, heuristic layered queueing network solver. Meier et al [15] explain how to map PCM instances to queueing Petri nets, which are solved by approximate analytical solvers or simulation.…”

“…The contribution of this paper is UCM2PCM, a model transformation from UCMs into the Palladio Component Model (PCM) [2], which enables component-oriented performance modeling as well as multiple solution methods (e.g., discrete event-based simulation [2], layered queuing networks [13], queuing Petri nets [15]). The transformation bridges semantic gaps between the requirements-oriented UCM notation and the component-oriented PCM notation.…”

Integration of event-based communication in the palladio software quality prediction framework

“…It supports automatic transformations of architecture-level performance models to predictive performance models including layered queuing networks [17], stochastic process algebras [7], queuing Petri nets [18], and simulation models [5,4].…”

“…In [26], we briefly sketched a basic extension of PCM to support the modeling of event-based communication. In a follow up poster paper [25], we described an idea of using a model-to-model transformation to map the newly introduced model elements to existing PCM model elements allowing to use the available analytical and simulative analysis techniques, e.g., [5,18,17], while significantly reducing the modeling effort.…”

Automated extraction of architecture-level performance models of distributed component-based systems

“…The latter describe performance-relevant aspects of the system's software architecture, execution environment, and usage profile (e.g., UML models augmented with performance annotations). They can normally be transformed into predictive performance models by means of automatic model-to-model transformations [3]. By modeling the system's performance-influencing factors explicitly, architecture-level models allow to predict the impact of changes in the software components, resource allocations and/or system workloads.…”