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

  • Authors:

  • Anne Koziolek;Qais Noorshams;Ralf Reussner

  • Affiliations:

  • Karlsruhe Institute of Technology, Karlsruhe, Germany;Karlsruhe Institute of Technology, Karlsruhe, Germany;Karlsruhe Institute of Technology, Karlsruhe, Germany

  • Venue:
  • MODELS'10 Proceedings of the 2010 international conference on Models in software engineering
  • Year:
  • 2010

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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.