PRESTO: Predicting System-level Disruptions through Parametric Model Checking

Research output: Chapter in Book/Report/Conference proceedingConference 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 languageEnglish
Title of host publication17th International Symposium on Software Engineering for Adaptive and Self-Managing Systems
Publication statusAccepted/In press - 10 Mar 2022

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