mdh.sePublications
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • 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
Learning-Based Self-Adaptive Assurance of Timing Properties in a Real-Time Embedded System
Mälardalen University, School of Innovation, Design and Engineering. RISE, SICS, Sweden.
RISE, SICS, Sweden.ORCID iD: 0000-0002-1512-0844
RISE, SICS, Sweden.
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.ORCID iD: 0000-0003-1597-6738
Show others and affiliations
2018 (English)In: ICST Workshop on Testing Extra-Functional Properties and Quality Characteristics of Software Systems ITEQS'18, 2018Conference paper, Published paper (Refereed)
Abstract [en]

Providing an adaptive runtime assurance technique to meet the performance requirements of a real-time system without the need for a precise model could be a challenge. Adaptive performance assurance based on monitoring the status of timing properties can bring more robustness to the underlying platform. At the same time, the results or the achieved policy of this adaptive procedure could be used as feedback to update the initial model, and consequently for producing proper test cases. Reinforcement-learning has been considered as a promising adaptive technique for assuring the satisfaction of the performance properties of software-intensive systems in recent years. In this work-in-progress paper, we propose an adaptive runtime timing assurance procedure based on reinforcement learning to satisfy the performance requirements in terms of response time. The timing control problem is formulated as a Markov Decision Process and the details of applying the proposed learning-based timing assurance technique are described.

Place, publisher, year, edition, pages
2018.
Keywords [en]
Timing properties, self-adaptive performance assurance, real-time embedded systems, reinforcement learning
National Category
Computer Systems
Identifiers
URN: urn:nbn:se:mdh:diva-38954OAI: oai:DiVA.org:mdh-38954DiVA, id: diva2:1205985
Conference
ICST Workshop on Testing Extra-Functional Properties and Quality Characteristics of Software Systems ITEQS'18, 09 Apr 2018, Västerås, Sweden
Available from: 2018-05-15 Created: 2018-05-15 Last updated: 2018-05-15Bibliographically approved

Open Access in DiVA

No full text in DiVA

Authority records BETA

Bohlin, MarkusLisper, Björn

Search in DiVA

By author/editor
Helali Moghadam, MahshidSaadatmand, MehrdadBohlin, MarkusLisper, Björn
By organisation
School of Innovation, Design and EngineeringEmbedded Systems
Computer Systems

Search outside of DiVA

GoogleGoogle Scholar

urn-nbn

Altmetric score

urn-nbn
Total: 11 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • 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