Fang, Xinwei, Calinescu, Radu orcid.org/0000-0002-2678-9260, Paterson, Colin orcid.org/0000-0002-6678-3752 et al. (1 more author) (2022) PRESTO:Predicting System-level Disruptions through Parametric Model Checking. In: SEAMS '22:Proceedings of the 17th Symposium on Software Engineering for Adaptive and Self-Managing Systems. ACM , pp. 91-97.
Abstract
Self-adaptive systems are expected to mitigate disruptions by con- tinually adjusting their configuration and behaviour. This mitiga- tion is often reactive. Typically, environmental or internal changes trigger a system response only after a violation of the system re- quirements. Despite a broad agreement that prevention is better than cure in self-adaptation, proactive adaptation methods are underrepresented within the repertoire of solutions available to the developers of self-adaptive systems. To address this gap, we present a work-in-progress approach for the prediction of system- level disruptions (PRESTO) through parametric model checking. Intended for use in the analysis step of the MAPE-K (Monitor- Analyse-Plan-Execute over a shared Knowledge) feedback control loop of self-adaptive systems, PRESTO comprises two stages. First, time-series analysis is applied to monitoring data in order to iden- tify trends in the values of individual system and/or environment parameters. Next, future non-functional requirement violations are predicted by using parametric model checking, in order to establish the potential impact of these trends on the reliability and perfor- mance of the system. We illustrate the application of PRESTO in a case study from the autonomous farming domain.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2022 Association for Computing Machinery. This is an author-produced version of the published paper. Uploaded in accordance with the publisher’s self-archiving policy. Further copying may not be permitted; contact the publisher for details |
Dates: |
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Institution: | The University of York |
Academic Units: | The University of York > Faculty of Sciences (York) > Computer Science (York) The University of York > Faculty of Sciences (York) > Mathematics (York) |
Depositing User: | Pure (York) |
Date Deposited: | 09 May 2022 15:10 |
Last Modified: | 13 Jan 2025 00:12 |
Published Version: | https://doi.org/10.1145/3524844.3528059 |
Status: | Published |
Publisher: | ACM |
Identification Number: | 10.1145/3524844.3528059 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:186566 |