  Self-adaptive systems are expected to mitigate disruptions by continually
adjusting their configuration and behaviour. This mitigation is often reactive.
Typically, environmental or internal changes trigger a system response only
after a violation of the system requirements. 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 pre diction 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 identify
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 performance of the system. We illustrate the application of
PRESTO in a case study from the autonomous farming domain.
