Using ArchE in the Classroom: One Experience

Proceedings of the First Joint WOSP/SIPEW International Conference on Performance Engineering

Koziolek²,

Becker

et al. 2010

143

Quantitative prediction of quality properties (i.e. extrafunctional properties such as performance, reliability, and cost) of software architectures during design supports a systematic software engineering approach. Designing architectures that exhibit a good trade-off between multiple quality criteria is hard, because even after a functional design has been created, many remaining degrees of freedom in the software architecture span a large, discontinuous design space. In current practice, software architects try to find solutions manually, which is time-consuming, can be errorprone and can lead to suboptimal designs. We propose an automated approach to search the design space for good solutions. Starting with a given initial architectural model, the approach iteratively modifies and evaluates architectural models. Our approach applies a multi-criteria genetic algorithm to software architectures modelled with the Palladio Component Model. It supports quantitative performance, reliability, and cost prediction and can be extended to other quantitative quality criteria of software architectures. We validate the applicability of our approach by applying it to an architecture model of a component-based business information system and analyse its quality criteria trade-offs by automatically investigating more than 1200 alternative design candidates.

Section: Evolutionary Optimisationmentioning

confidence: 99%

Section: Limitations and Future Workmentioning

confidence: 99%

See 1 more Smart Citation

Automatically improve software architecture models for performance, reliability, and cost using evolutionary algorithms

Proceedings of the First Joint WOSP/SIPEW International Conference on Performance Engineering

Koziolek²,

Becker

et al. 2010

143

“…McGregor et al [28] have developed the ArchE framework. ArchE assists the software architect during the design to create architectures that meet quality requirements.…”

Section: Related Workmentioning

confidence: 99%

A Hybrid Approach for Multi-attribute QoS Optimisation in Component Based Software Systems

Research Into Practice – Reality and Gaps

Ardagna

Koziolek³

et al. 2010

Abstract. Design decisions for complex, component-based systems impact multiple quality of service (QoS) properties. Often, means to improve one quality property deteriorate another one. In this scenario, selecting a good solution with respect to a single quality attribute can lead to unacceptable results with respect to the other quality attributes. A promising way to deal with this problem is to exploit multi-objective optimization where the objectives represent different quality attributes. The aim of these techniques is to devise a set of solutions, each of which assures a trade-off between the conflicting qualities. To automate this task, this paper proposes a combined use of analytical optimization techniques and evolutionary algorithms to efficiently identify a significant set of design alternatives, from which an architecture that best fits the different quality objectives can be selected. The proposed approach can lead both to a reduction of development costs and to an improvement of the quality of the final system. We demonstrate the use of this approach on a simple case study.

“…McGregor et al [23] have developed the ArchE framework. ArchE assists the software architect during the design to create architectures that meet quality requirements.…”

Section: Related Workmentioning

confidence: 99%

Optimising multiple quality criteria of service-oriented software architectures

Proceedings of the 1st International Workshop on Quality of Service-Oriented Software Systems

Brosch

Reussner

2009

Quantitative prediction of quality criteria (i.e. extra-functional properties such as performance, reliability, and cost) of service-oriented architectures supports a systematic software engineering approach. However, various degrees of freedom in building a software architecture span a large, discontinuous design space. Currently, solutions with a good trade-off between multiple quality criteria have to be found manually. We propose an automated approach to search the design space by modifying the architectural models, to improve the architecture with respect to multiple quality criteria, and to find optimal architectural models. The found optimal architectural models can be used as an input for trade-off analyses and thus allow systematic engineering of high-quality software architectures. Using this approach, the design of a high-quality component-based software system is eased for the software architect and thus saves cost and effort. Our approach applies a multi-criteria genetic algorithm to software architectures modelled with the Palladio Component Model (PCM). Currently, the method supports quantitative performance and reliability prediction, but it can be extended to other quality properties such as cost as well.