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Towards Better Adap...
Towards Better Adaptive Systems by Combining MAPE, Control Theory, and Machine Learning
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- Weyns, Danny (författare)
- Linnéuniversitetet,Institutionen för datavetenskap och medieteknik (DM),Catholic University of Leuven, Belgium
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- Schmerl, Bradley (författare)
- Carnegie Mellon University, USA
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- Kishida, Masako (författare)
- National Institute of Informatics, Japan
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- Leva, Alberto (författare)
- Politecnico di Milano, Italy
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- Litoiu, Marin (författare)
- York University, Canada
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- Ozay, Necmiye (författare)
- University of Michigan, USA
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- Paterson, Colin (författare)
- University of York, UK
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- Tei, Kenji (författare)
- Waseda University, Japan
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(creator_code:org_t)
- IEEE, 2021
- 2021
- Engelska.
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Ingår i: Proceedings of the 2021 International Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS). - : IEEE. - 9781665402897 - 9781665402903 ; , s. 217-223
- Relaterad länk:
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https://urn.kb.se/re...
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https://doi.org/10.1...
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Abstract
Ämnesord
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- Two established approaches to engineer adaptive systems are architecture-based adaptation that uses a Monitor-Analysis-Planning-Executing (MAPE) loop that reasons over architectural models (aka Knowledge) to make adaptation decisions, and control-based adaptation that relies on principles of control theory (CT) to realize adaptation. Recently, we also observe a rapidly growing interest in applying machine learning (ML) to support different adaptation mechanisms. While MAPE and CT have particular characteristics and strengths to be applied independently, in this paper, we are concerned with the question of how these approaches are related with one another and whether combining them and supporting them with ML can produce better adaptive systems. We motivate the combined use of different adaptation approaches using a scenario of a cloud-based enterprise system and illustrate the analysis when combining the different approaches. To conclude, we offer a set of open questions for further research in this interesting area.
Ämnesord
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datavetenskap (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Sciences (hsv//eng)
Nyckelord
- cloud enterprise system
- control theory
- machine learning
- MAPE
- Self-adaptive systems
- Software engineering
- Adaptation decisions
- Adaptation mechanism
- Architectural models
- Architecture based adaptation
- Cloud-based
- Enterprise system
- Adaptive control systems
- Computer Science
- Datavetenskap
Publikations- och innehållstyp
- ref (ämneskategori)
- kon (ämneskategori)
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