KlaperSuite: An Integrated Model-Driven Environment for Reliability and Performance Analysis of Component-Based Systems

Abstract

Abstract. Automatic prediction tools play a key role in enabling the application of non-functional requirements analysis to selection and assembly of components for Component-Based Systems, reducing the need for strong mathematical skills to software designers. Exploiting the paradigm of Model Driven Engineering (MDE), it is possible to automate transformations from design models to analytical models, enabling for formal property verification. MDE is the core paradigm of KlaperSuite presented in this paper, which exploits the KLAPER pivot language to fill the gap between Design and Analysis of Component-Based Systems for reliability and performance properties. KlaperSuite is a family of tools empowering designers with the ability to capture and analyze QoS views of their systems by building a one-click bridge towards a number of established verification instruments.

The KlaperSuite framework for model-driven reliability analysis of component-based systems

“…This paper specifically focuses on the evaluation of the reliability prediction capabilities of KlaperSuite, hence we give an extensive overview of how these analyses work in Section 4, while we defer to Appendix A for details about the model transformations that allow to carry out the analysis. For what concerns performance, KLAPER already provides both an LQN based prediction tool and the SimJava 3 -based simulator (a description of these tools can be found in [14,60]). We are currently in the stage of refactoring and fully integrating these analyzers into KlaperSuite; their usability is currently limited.…”

“…In this article we present KlaperSuite, our model-driven proposal to support early-stage analysis of non-functional attributes for component-based systems [68] which we already introduced in [14].…”

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

“…KLAPER [7] is a meta model language for performance prediction of component based systems. With the KLAPER Suite, there is a set of tools to actually create performance prediction from the model.…”

QoS verification and model tuning @ runtime

“…Precisely, by expanding first the c rows representing the variable states (each has τ symbolic terms), we need to compute at most τ c determinants and then linearly combine them. Each submatrix of size t − c does not contain any variable symbol, by construction, thus its determinant can be computed with (t − c) 3 operations among constant numbers (LU-decomposition), thus much faster than the corresponding ones among polynomials. The final complexity is thus τ c · (t − c) 3 ∼ τ c · t 3 , which significantly reduces the original complexity and makes the design-time pre-computation of reachability properties feasible in a reasonable time, even for large values of t.…”

“…This research has been partially funded by the European Commission, Programme IDEAS-ERC, Project 227977-SMScom. Other publications of the author include [10,4,7,3,12,13,11]. He is also co-author of the Dagstuhl proceedings book "Model Driven Quality Prediction" (Springer ed.…”