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Type: Journal article
Title: Model-driven performance prediction of systems of systems
Author: Falkner, K.
Szabo, C.
Chiprianov, V.
Puddy, G.
Rieckmann, M.
Fraser, D.
Aston, C.
Citation: Software and Systems Modeling, 2018; 17(2):415-441
Publisher: Springer
Issue Date: 2018
ISSN: 1619-1366
Statement of
Katrina Falkner, Claudia Szabo, Vanea Chiprianov, Gavin Puddy, Marianne Rieckmann, Dan Fraser, Cathlyn Aston
Abstract: Systems of systems exhibit characteristics that pose difficulty in modelling and predicting their overall performance capabilities, including the presence of operational independence, emergent behaviour, and evolutionary development. When considering systems of systems within the autonomous defence systems context, these aspects become increasingly critical, as constraints on the performance of the final system are typically driven by hard constraints on space, weight and power. System execution modelling languages and tools permit early prediction of the performance of model-driven systems; however, the focus to date has been on understanding the performance of a model rather than determining whether it meets performance requirements, and only subsequently carrying out analysis to reveal the causes of any requirement violations. Moreover, such an analysis is even more difficult when applied to several systems cooperating to achieve a common goala-a system of systems. In this article, we propose an integrated approach to performance prediction of model-driven real-time embedded defence systems and systems of systems. Our architectural prototyping system supports a scenario-driven experimental platform for evaluating model suitability within a set of deployment and real-time performance constraints. We present an overview of our performance prediction system, demonstrating the integration of modelling, execution and performance analysis, and discuss a case study to illustrate our approach.
Keywords: Performance prediction; systems of systems; model-driven engineering; system execution modelling
Rights: © Springer-Verlag Berlin Heidelberg 2016
RMID: 0030051667
DOI: 10.1007/s10270-016-0547-8
Appears in Collections:Computer Science publications

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