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Model-based Simulation at Runtime for Self-adaptive Systems
Linnaeus University, Faculty of Technology, Department of Computer Science. Katholieke Univ Leuven, Belgium.ORCID iD: 0000-0002-1162-0817
Linnaeus University, Faculty of Technology, Department of Computer Science.ORCID iD: 0000-0002-1343-5834
2016 (English)In: 2016 IEEE International Conference on Autonomic Computing (ICAC), IEEE, 2016, 364-373 p.Conference paper (Refereed)
Abstract [en]

Modern software systems are subject to uncertainties, such as dynamics in the availability of resources or changes of system goals. Self-adaptation enables a system to reason about runtime models to adapt itself and realises its goals under uncertainties. Our focus is on providing guarantees for adaption goals. A prominent approach to provide such guarantees is automated verification of a stochastic model that encodes up-to-date knowledge of the system and relevant qualities. The verification results allow selecting an adaption option that satisfies the goals. There are two issues with this state of the art approach: i) changing goals at runtime (a challenging type of uncertainty) is difficult, and ii) exhaustive verification suffers from the state space explosion problem. In this paper, we propose a novel modular approach for decision making in self-adaptive systems that combines distinct models for each relevant quality with runtime simulation of the models. Distinct models support on the fly changes of goals. Simulation enables efficient decision making to select an adaptation option that satisfies the system goals. The tradeoff is that simulation results can only provide guarantees with a certain level of accuracy. We demonstrate the benefits and tradeoffs of the approach for a service-based telecare system.

Place, publisher, year, edition, pages
IEEE, 2016. 364-373 p.
Series
Proceedings of the International Conference on Autonomic Computing, ISSN 2474-0756
Keyword [en]
Self-adaptation, models and simulation at run-time, TAS exemplar
National Category
Computer Science
Research subject
Computer and Information Sciences Computer Science, Computer Science
Identifiers
URN: urn:nbn:se:lnu:diva-60161DOI: 10.1109/ICAC.2016.67ISI: 000390681200057ISBN: 978-1-5090-1653-2 (print)OAI: oai:DiVA.org:lnu-60161DiVA: diva2:1068168
Conference
13th IEEE International Conference on Autonomic Computing (ICAC), JUL 17-22, 2016, Wurzburg, GERMANY
Available from: 2017-01-24 Created: 2017-01-24 Last updated: 2017-01-24Bibliographically approved

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Weyns, DannyIftikhar, Muhammad Usman
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CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf