UZH-Logo

Maintenance Infos

Focussing multi-objective software architecture optimization using quality of service bounds


Koziolek, Anne; Noorshams, Qais; Reussner, Ralf (2011). Focussing multi-objective software architecture optimization using quality of service bounds. In: Dingel, Juergen; Solberg, Arnor. Models in Software Engineering. Berlin / Heidelberg: Springer, 384-399.

Abstract

Quantitative prediction of non-functional properties, such as performance, reliability, and costs, of software architectures supports systematic software engineering. Even though there usually is a rough idea on bounds for quality of service, the exact required values may be unclear and subject to trade-offs. Designing architectures that exhibit such good trade-off between multiple quality attributes is hard. Even with a given functional design, many degrees of freedom in the software architecture (e.g. component deployment or server configuration) span a large design space. Automated approaches search the design space with multi-objective metaheuristics such as evolutionary algorithms. However, as quality prediction for a single architecture is computationally expensive, these approaches are time consuming. In this work, we enhance an automated improvement approach to take into account bounds for quality of service in order to focus the search on interesting regions of the objective space, while still allowing trade-offs after the search. We compare two different constraint handling techniques to consider the bounds.To validate our approach, we applied both techniques to an architecture model of a component-based business information system. We compared both techniques to an unbounded search in 4 scenarios. Every scenario was examined with 10 optimization runs, each investigating around 1600 architectural candidates. The results indicate that the integration of quality of service bounds during the optimization process can improve the quality of the solutions found, however, the effect depends on the scenario, i.e. the problem and the quality requirements. The best results were achieved for costs requirements: The approach was able to decrease the time needed to find good solutions in the interesting regions of the objective space by 25% on average.

Quantitative prediction of non-functional properties, such as performance, reliability, and costs, of software architectures supports systematic software engineering. Even though there usually is a rough idea on bounds for quality of service, the exact required values may be unclear and subject to trade-offs. Designing architectures that exhibit such good trade-off between multiple quality attributes is hard. Even with a given functional design, many degrees of freedom in the software architecture (e.g. component deployment or server configuration) span a large design space. Automated approaches search the design space with multi-objective metaheuristics such as evolutionary algorithms. However, as quality prediction for a single architecture is computationally expensive, these approaches are time consuming. In this work, we enhance an automated improvement approach to take into account bounds for quality of service in order to focus the search on interesting regions of the objective space, while still allowing trade-offs after the search. We compare two different constraint handling techniques to consider the bounds.To validate our approach, we applied both techniques to an architecture model of a component-based business information system. We compared both techniques to an unbounded search in 4 scenarios. Every scenario was examined with 10 optimization runs, each investigating around 1600 architectural candidates. The results indicate that the integration of quality of service bounds during the optimization process can improve the quality of the solutions found, however, the effect depends on the scenario, i.e. the problem and the quality requirements. The best results were achieved for costs requirements: The approach was able to decrease the time needed to find good solutions in the interesting regions of the objective space by 25% on average.

Altmetrics

Additional indexing

Item Type:Book Section, refereed, original work
Communities & Collections:03 Faculty of Economics > Department of Informatics
Dewey Decimal Classification:000 Computer science, knowledge & systems
Language:English
Date:2011
Deposited On:09 Feb 2012 16:21
Last Modified:05 Apr 2016 15:24
Publisher:Springer
Series Name:Lecture Notes in Computer Science
Number:6627
ISSN:0302-9743 (P) 1611-3349 (E)
ISBN:978-3-642-21209-3
Publisher DOI:10.1007/978-3-642-21210-9_37
Other Identification Number:merlin-id:3925

Download

Full text not available from this repository.View at publisher

TrendTerms

TrendTerms displays relevant terms of the abstract of this publication and related documents on a map. The terms and their relations were extracted from ZORA using word statistics. Their timelines are taken from ZORA as well. The bubble size of a term is proportional to the number of documents where the term occurs. Red, orange, yellow and green colors are used for terms that occur in the current document; red indicates high interlinkedness of a term with other terms, orange, yellow and green decreasing interlinkedness. Blue is used for terms that have a relation with the terms in this document, but occur in other documents.
You can navigate and zoom the map. Mouse-hovering a term displays its timeline, clicking it yields the associated documents.

Author Collaborations