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

Weckesser, Markus ; Kluge, Roland ; Pfannemüller, Martin ; Matthé, Michael ; Schürr, Andy ; Becker, Christian (2018)
Optimal Reconfiguration of Dynamic Software Product Lines Based on Performance-influence Models.
Proceeedings of the 22Nd International Conference on Systems and Software Product Line - Volume 1. New York, NY, USA
doi: 10.1145/3233027.3233030
Conference or Workshop Item, Bibliographie

Item Type: Conference or Workshop Item
Erschienen: 2018
Creators: Weckesser, Markus ; Kluge, Roland ; Pfannemüller, Martin ; Matthé, Michael ; Schürr, Andy ; Becker, Christian
Type of entry: Bibliographie
Title: Optimal Reconfiguration of Dynamic Software Product Lines Based on Performance-influence Models
Language: English
Date: 2018
Place of Publication: New York, NY, USA
Publisher: ACM
Series: SPLC '18
Event Title: Proceeedings of the 22Nd International Conference on Systems and Software Product Line - Volume 1
Event Location: New York, NY, USA
DOI: 10.1145/3233027.3233030
URL / URN: 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
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
Date Deposited: 04 Oct 2018 14:37
Last Modified: 11 Oct 2018 09:49
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