Optimising multiple quality criteria of service-oriented software architectures

Abstract

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.

Evolutionary deployment optimization for service‐oriented clouds

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Fast optimization for scalable application deployments in large service centers

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