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Optimal Reconfiguration of Dynamic Software Product Lines Based on Performance-influence Models

Weckesser, Markus and Kluge, Roland and Pfannemüller, Martin and Matthé, Michael and Schürr, Andy and Becker, Christian (2018):
Optimal Reconfiguration of Dynamic Software Product Lines Based on Performance-influence Models.
In: SPLC '18, New York, NY, USA, ACM, In: Proceeedings of the 22Nd International Conference on Systems and Software Product Line - Volume 1, New York, NY, USA, pp. 98-109, ISBN 978-1-4503-6464-5,
DOI: 10.1145/3233027.3233030,
[Online-Edition: http://doi.acm.org/10.1145/3233027.3233030],
[Conference or Workshop Item]

Item Type: Conference or Workshop Item
Erschienen: 2018
Creators: Weckesser, Markus and Kluge, Roland and Pfannemüller, Martin and Matthé, Michael and Schürr, Andy and Becker, Christian
Title: Optimal Reconfiguration of Dynamic Software Product Lines Based on Performance-influence Models
Language: English
Series Name: SPLC '18
Place of Publication: New York, NY, USA
Publisher: ACM
ISBN: 978-1-4503-6464-5
Uncontrolled Keywords: dynamic software product lines, machine learning, performance-influence models
Divisions: 18 Department of Electrical Engineering and Information Technology
18 Department of Electrical Engineering and Information Technology > Institute of Computer Engineering > Real-Time Systems
18 Department of Electrical Engineering and Information Technology > Institute of Computer Engineering
DFG-Collaborative Research Centres (incl. Transregio)
DFG-Collaborative Research Centres (incl. Transregio) > Collaborative Research Centres
DFG-Collaborative Research Centres (incl. Transregio) > Collaborative Research Centres > CRC 1053: MAKI – Multi-Mechanisms Adaptation for the Future Internet
DFG-Collaborative Research Centres (incl. Transregio) > Collaborative Research Centres > CRC 1053: MAKI – Multi-Mechanisms Adaptation for the Future Internet > A: Construction Methodology
DFG-Collaborative Research Centres (incl. Transregio) > Collaborative Research Centres > CRC 1053: MAKI – Multi-Mechanisms Adaptation for the Future Internet > A: Construction Methodology > Subproject A1: Modelling
DFG-Collaborative Research Centres (incl. Transregio) > Collaborative Research Centres > CRC 1053: MAKI – Multi-Mechanisms Adaptation for the Future Internet > A: Construction Methodology > Subproject A4: Self-Adaptation
Event Title: Proceeedings of the 22Nd International Conference on Systems and Software Product Line - Volume 1
Event Location: New York, NY, USA
Date Deposited: 04 Oct 2018 14:37
DOI: 10.1145/3233027.3233030
Official URL: http://doi.acm.org/10.1145/3233027.3233030
Alternative Abstract:
Alternative abstract Language
Today’s adaptive software systems (i) are often highly configurable product lines, exhibiting hundreds of potentially conflicting configuration options; (ii) are context dependent, forcing the system to reconfigure to ever-changing contextual situations at runtime; (iii) need to fulfill context-dependent performance goals by optimizing measurable nonfunctional properties. Usually, a large number of consistent configurations exists for a given context, and each consistent configuration may perform differently with regard to the current context and performance goal(s). Therefore, it is crucial to consider nonfunctional properties for identifying an appropriate configuration. Existing black-box approaches for estimating the performance of configurations provide no means for determining context-sensitive reconfiguration decisions at runtime that are both consistent and optimal, and hardly allow for combining multiple context-dependent quality goals. In this paper, we propose a comprehensive approach based on Dynamic Software Product Lines (DSPL) for obtaining consistent and optimal reconfiguration decisions. We use training data obtained from simulations to learn performance-influence models. A novel integrated runtime representation captures both consistency properties and the learned performance-influence models. Our solution provides the flexibility to define multiple context-dependent performance goals. We have implemented our approach as a standalone component. Based on an Internet-of-Things case study using adaptive wireless sensor networks, we evaluate our approach with regard to effectiveness, efficiency, and applicability.UNSPECIFIED
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