具有历史的高度可扩展的可扩展模型
Towards Highly Scalable Runtime Models with History
摘要：
物联网等高级系统包括许多异构、互连和自主的实体，这些实体通常在高度动态的环境中运行。由于其庞大的规模和复杂性，产生了大量的监测数据，需要以节省时间和资源的方式进行存储、检索和挖掘。架构自适应使这些系统的控制、编排和操作自动化。这只能通过由监控数据支持的复杂决策方案来实现，这些数据完全捕获系统行为及其历史。采用模型驱动的工程技术，我们提出了一个高度可扩展的，历史感知的方法来存储和检索监测数据的形式丰富的运行时模型。我们利用基于规则的适应，在系统中的变化事件触发适应规则。首先，我们提出了一个计划，增量检查模型查询的时态逻辑公式的形式表示的适应规则对一个运行时模型的历史条件。然后，我们增强了模型，只保留时间上相关的查询信息，从而减少了所需的最小信息的积累。最后，我们证明了我们的方法的可行性和可扩展性，通过实验模拟智能医疗系统采用真实世界的医疗指南。
英文摘要：
Advanced systems such as IoT comprise many heterogeneous, interconnected, and autonomous entities operating in often highly dynamic environments. Due to their large scale and complexity, large volumes of monitoring data are generated and need to be stored, retrieved, and mined in a time- and resource-efficient manner. Architectural self-adaptation automates the control, orchestration, and operation of such systems. This can only be achieved via sophisticated decision-making schemes supported by monitoring data that fully captures the system behavior and its history. Employing model-driven engineering techniques we propose a highly scalable, history-aware approach to store and retrieve monitoring data in form of enriched runtime models. We take advantage of rule-based adaptation where change events in the system trigger adaptation rules. We first present a scheme to incrementally check model queries in the form of temporal logic formulas which represent the conditions of adaptation rules against a runtime model with history. Then we enhance the model to retain only information that is temporally relevant to the queries, therefore reducing the accumulation of information to a required minimum. Finally, we demonstrate the feasibility and scalability of our approach via experiments on a simulated smart healthcare system employing a real-world medical guideline.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
