PRESTO: Predicting System-level Disruptions through Parametric Model Checking

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

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	17th International Symposium on Software Engineering for Adaptive and Self-Managing Systems
Publication status	Accepted/In press - 10 Mar 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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© 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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month = mar,

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AU - Fang, Xinwei

AU - Calinescu, Radu

AU - Paterson, Colin

AU - Wilson, Julie C.

N1 - © 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

PY - 2022/3/10

Y1 - 2022/3/10

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.

AB - 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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SN - 978-1-4503-9305-8

BT - 17th International Symposium on Software Engineering for Adaptive and Self-Managing Systems

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