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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 :
Optimal Reconfiguration of Dynamic Software Product Lines Based on Performance-influence Models.
[Online-Edition: http://doi.acm.org/10.1145/3233027.3233030]
In: Proceeedings of the 22Nd International Conference on Systems and Software Product Line - Volume 1, New York, NY, USA. In: SPLC '18 . ACM , New York, NY, USA
[Konferenz- oder Workshop-Beitrag], (2018)

Offizielle URL: http://doi.acm.org/10.1145/3233027.3233030
Typ des Eintrags: Konferenz- oder Workshop-Beitrag (Keine Angabe)
Erschienen: 2018
Autor(en): Weckesser, Markus ; Kluge, Roland ; Pfannemüller, Martin ; Matthé, Michael ; Schürr, Andy ; Becker, Christian
Titel: Optimal Reconfiguration of Dynamic Software Product Lines Based on Performance-influence Models
Sprache: Englisch
Reihe: SPLC '18
Ort: New York, NY, USA
Verlag: ACM
Freie Schlagworte: dynamic software product lines, machine learning, performance-influence models
Fachbereich(e)/-gebiet(e): 18 Fachbereich Elektrotechnik und Informationstechnik
18 Fachbereich Elektrotechnik und Informationstechnik > Institut für Datentechnik > Echtzeitsysteme
18 Fachbereich Elektrotechnik und Informationstechnik > Institut für Datentechnik
DFG-Sonderforschungsbereiche (inkl. Transregio)
DFG-Sonderforschungsbereiche (inkl. Transregio) > Sonderforschungsbereiche
DFG-Sonderforschungsbereiche (inkl. Transregio) > Sonderforschungsbereiche > SFB 1053: MAKI – Multi-Mechanismen-Adaption für das künftige Internet
DFG-Sonderforschungsbereiche (inkl. Transregio) > Sonderforschungsbereiche > SFB 1053: MAKI – Multi-Mechanismen-Adaption für das künftige Internet > A: Konstruktionsmethodik
DFG-Sonderforschungsbereiche (inkl. Transregio) > Sonderforschungsbereiche > SFB 1053: MAKI – Multi-Mechanismen-Adaption für das künftige Internet > A: Konstruktionsmethodik > Teilprojekt A1: Modellierung
DFG-Sonderforschungsbereiche (inkl. Transregio) > Sonderforschungsbereiche > SFB 1053: MAKI – Multi-Mechanismen-Adaption für das künftige Internet > A: Konstruktionsmethodik > Teilprojekt A4: Selbst-Adaption
Veranstaltungstitel: Proceeedings of the 22Nd International Conference on Systems and Software Product Line - Volume 1
Veranstaltungsort: New York, NY, USA
Hinterlegungsdatum: 04 Okt 2018 14:37
DOI: 10.1145/3233027.3233030
Offizielle URL: http://doi.acm.org/10.1145/3233027.3233030
Alternatives oder übersetztes Abstract:
AbstractSprache
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.Keine Angabe
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