Autonomous systems are often used in applications where environmental and internal changes may lead to requirement violations. Adapting to these changes proactively, i.e., before the violations occur, is preferable to recovering from the failures that may be caused by such violations. However, proactive adaptation needs methods for predicting requirement violations timely, accurately, and with acceptable overheads. To address this need, we present a method that allows autonomous systems to predict violations of performance, dependability and other nonfunctional requirements, and therefore take preventative measures to avoid or otherwise mitigate them. Our method for pre dicting these autonomou s sys t em disrupti o ns (PRESTO) comprises a design time stage and a run-time stage. At design-time, we use parametric model checking to obtain algebraic expressions that formalise the relationships between the nonfunctional properties of the requirements of interest (e.g., reliability, response time, and energy use) and the parameters of the system and its environment. At run-time, we predict future changes in these parameters by applying piece-wise linear regression to online data obtained through monitoring, and we use the algebraic expressions to predict the impact of these changes on the system requirements. We demonstrate the application of PRESTO through simulation in case studies from two different domains.

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 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 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.

MOTIVATION: The identification of suitable conditions for crystallization is a rate-limiting step in protein structure determination. The pH of an experiment is an important parameter and has the potential to be used in data-mining studies to help reduce the number of crystallization trials required. However, the pH is usually recorded as that of the buffer solution, which can be highly inaccurate. RESULTS: Here, we show that a better estimate of the true pH can be predicted by considering not only the buffer pH but also any other chemicals in the crystallization solution. We use these more accurate pH values to investigate the disputed relationship between the pI of a protein and the pH at which it crystallizes. AVAILABILITY AND IMPLEMENTATION: Data used to generate models are available as Supplementary Material. CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

To use crystallography for the determination of the three-dimensional structures of proteins, protein crystals need to be grown. Automated imaging systems are increasingly being used to monitor these crystallization experiments. These present problems of accessibility to the data, repeatability of any image analysis performed and the amount of storage required. Various image formats and techniques can be combined to provide effective solutions to high volume processing problems such as these, however lack of widespread support for the most effective algorithms, such as JPeg2000 which yielded a 64% improvement in file size over the bitmap, currently inhibits the immediate take up of this approach.