论文标题
PRESTO：通过参数模型检查预测系统级破坏
PRESTO: Predicting System-level Disruptions through Parametric Model Checking
论文作者
论文摘要
预期自适应系统可以通过不断调整其配置和行为来减轻干扰。这种缓解通常是反应性的。通常，只有在违反系统要求后，环境或内部变化才会触发系统响应。尽管有广泛的共识，即预防比在自动适应方面更好地治愈，但在自适应系统开发人员可用的解决方案的曲目中，主动适应方法的代表性不足。为了解决这一差距，我们提出了一种通过参数模型检查的系统级中断（PRESTO）的前进方法。 Presto旨在用于MAPE-K（Monitor-Analyse-plan-execute）的分析步骤中，Presto构成了两个阶段。首先，将时间序分析应用于监视数据，以确定单个系统和/或环境参数值的趋势。接下来，通过使用参数模型检查来预测未来的非功能要求违规，以确定这些趋势对系统的可靠性和性能的潜在影响。我们说明了Presto在自主农业领域的案例研究中的应用。
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.
