PRESTO: Predicting System-level Disruptions through Parametric Model Checking

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.