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Quantitative Analysis of Service-Oriented Architectures

Quantitative Analysis of Service-Oriented Architectures

Maria-Eugenia Iacob, Henk Jonkers
Copyright: © 2007 |Volume: 3 |Issue: 1 |Pages: 19
ISSN: 1548-1115|EISSN: 1548-1123|ISSN: 1548-1115|EISBN13: 9781615202782|EISSN: 1548-1123|DOI: 10.4018/jeis.2007010103
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MLA

Iacob, Maria-Eugenia, and Henk Jonkers. "Quantitative Analysis of Service-Oriented Architectures." IJEIS vol.3, no.1 2007: pp.42-60. http://doi.org/10.4018/jeis.2007010103

APA

Iacob, M. & Jonkers, H. (2007). Quantitative Analysis of Service-Oriented Architectures. International Journal of Enterprise Information Systems (IJEIS), 3(1), 42-60. http://doi.org/10.4018/jeis.2007010103

Chicago

Iacob, Maria-Eugenia, and Henk Jonkers. "Quantitative Analysis of Service-Oriented Architectures," International Journal of Enterprise Information Systems (IJEIS) 3, no.1: 42-60. http://doi.org/10.4018/jeis.2007010103

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Abstract

In this article we address the integration of functional models with non-functional models in the context of service-oriented enterprise architecture. Starting from the observation that current approaches to model-driven development have a strong focus on functionality, we argue the necessity of including non-functional aspects as early as possible in the service design process. We distinguish two modelling spaces, the design space and the analysis space, which can be integrated by means of model transformations. Quantitative results obtained in the analysis space, using special-purpose analysis techniques, can be related back to the design models by means of a reverse transformation. This provides a framework for incorporating non-functional analysis into methodological support for e-service development. While, for detailed design models, performance analysis is more or less covered by existing techniques, there is still a gap at the architectural overview level. Therefore, we propose an approach for performance analysis of layered, service-oriented architecture models, which consists of two phases: a ‘‘top-down’’ propagation of workload parameters, and a “bottom-up’’ propagation of performance or cost measures. By means of an example, we demonstrate the application of the approach and show that a seamless integration with detailed performance analysis methods (e.g., queueing analysis) can be achieved.

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