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Applying Artificial Neural Networks to Reduce the Adaptation Space in Self-Adaptive Systems: an exploratory work
Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM).
2019 (English)Independent thesis Advanced level (degree of Master (One Year)), 10 credits / 15 HE creditsStudent thesis
Abstract [en]

Self-adaptive systems have limited time to adjust their configurations whenever their adaptation goals, i.e., quality requirements, are violated due to some runtime uncertainties. Within the available time, they need to analyze their adaptation space, i.e., a set of configurations, to find the best adaptation option, i.e., configuration, that can achieve their adaptation goals. Existing formal analysis approaches find the best adaptation option by analyzing the entire adaptation space. However, exhaustive analysis requires time and resources and is therefore only efficient when the adaptation space is small. The size of the adaptation space is often in hundreds or thousands, which makes formal analysis approaches inefficient in large-scale self-adaptive systems. In this thesis, we tackle this problem by presenting an online learning approach that enables formal analysis approaches to analyze large adaptation spaces efficiently. The approach integrates with the standard feedback loop and reduces the adaptation space to a subset of adaptation options that are relevant to the current runtime uncertainties. The subset is then analyzed by the formal analysis approaches, which allows them to complete the analysis faster and efficiently within the available time. We evaluate our approach on two different instances of an Internet of Things application. The evaluation shows that our approach dramatically reduces the adaptation space and analysis time without compromising the adaptation goals.

Place, publisher, year, edition, pages
2019. , p. 53
Keywords [en]
Self-Adaptive Systems, Self-Adaptation, Architecture-Based Adaptation, Autonomous Systems, Cyber-Physical Systems, CPS, DeltaIoT, IoT, ActivFORMS, MAPE-K Feedback Loop, Runtime Uncertainties, Adaptation Space, Analysis, Machine Learning, Artificial Neural Network, ANN, Online Learning, Deep Learning, Online Supervised Learning, Incremental Learning, Classification, Multi-Layer Perceptron, MLP
National Category
Computer Vision and Robotics (Autonomous Systems) Computer Sciences Control Engineering Software Engineering
Identifiers
URN: urn:nbn:se:lnu:diva-87117OAI: oai:DiVA.org:lnu-87117DiVA, id: diva2:1341195
Subject / course
Computer Science
Educational program
Software Technology Programme, Master Programme, 60 credits
Presentation
2019-06-05, D1167, Växjö, 11:00 (English)
Supervisors
Examiners
Available from: 2019-08-15 Created: 2019-08-07 Last updated: 2019-08-15Bibliographically approved

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Computer Vision and Robotics (Autonomous Systems)Computer SciencesControl EngineeringSoftware Engineering

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CiteExportLink to record
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Citation style
  • apa
  • ieee
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  • de-DE
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  • Other locale
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Output format
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