PRESTO: Predicting System-level Disruptions through Parametric Model Checking

Xinwei Fang; Radu Calinescu; Colin Paterson; Julie C. Wilson

doi:10.1145/3524844.3528059

PRESTO: Predicting System-level Disruptions through Parametric Model Checking

Xinwei Fang, Radu Calinescu, Colin Paterson, Julie C. Wilson

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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.

Original language	English
Title of host publication	SEAMS '22
Subtitle of host publication	Proceedings of the 17th Symposium on Software Engineering for Adaptive and Self-Managing Systems
Publisher	ACM
Pages	91-97
ISBN (Print)	978-1-4503-9305-8
DOIs	https://doi.org/10.1145/3524844.3528059
Publication status	Published - 15 Aug 2022

Bibliographical note

© 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

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Cite this

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title = "PRESTO: Predicting System-level Disruptions through Parametric Model Checking",

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 changestrigger a system response only after a violation of the system re-quirements. Despite a broad agreement that prevention is betterthan cure in self-adaptation, proactive adaptation methods areunderrepresented within the repertoire of solutions available tothe developers of self-adaptive systems. To address this gap, wepresent 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 controlloop 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 environmentparameters. Next, future non-functional requirement violations arepredicted by using parametric model checking, in order to establishthe potential impact of these trends on the reliability and perfor-mance of the system. We illustrate the application of PRESTO in acase study from the autonomous farming domain.",

author = "Xinwei Fang and Radu Calinescu and Colin Paterson and Wilson, {Julie C.}",

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N2 - 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 changestrigger a system response only after a violation of the system re-quirements. Despite a broad agreement that prevention is betterthan cure in self-adaptation, proactive adaptation methods areunderrepresented within the repertoire of solutions available tothe developers of self-adaptive systems. To address this gap, wepresent 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 controlloop 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 environmentparameters. Next, future non-functional requirement violations arepredicted by using parametric model checking, in order to establishthe potential impact of these trends on the reliability and perfor-mance of the system. We illustrate the application of PRESTO in acase study from the autonomous farming domain.

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