GRuM — A flexible model-driven runtime monitoring framework and its application to automated aerial and ground vehicles
GRuM — A flexible model-driven runtime monitoring framework and its application to automated aerial and ground vehicles
复制标题
GRuM – 灵活的模型驱动运行时监控框架及其在自动化空中和地面车辆中的应用
DOI：
10.1016/j.jss.2023.111733
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发表时间：
2023
影响因子：
3.5
通讯作者：
Cleland-Huang, Jane
中科院分区：
文献类型：
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作者：
Vierhauser, Michael;Garmendia, Antonio;Stadler, Marco;Wimmer, Manuel;Cleland-Huang, Jane
网络监控对于确保安全运行和实现网络物理系统（CPS）的自适应行为至关重要。通过识别感兴趣的运行时属性、创建探测器来检测系统以及定义要在运行时检查的约束来建立约束。对于许多系统来说，实现和设置监控平台可能是繁琐和耗时的，因为通用的监控平台不能充分满足特定于域的监控需求。当受监测系统不断发展，需要改变监测平台时，这种情况就会加剧。大多数现有的方法缺乏对用于不同技术的监视器的自动生成和设置的支持，并且不提供对处理系统演进的足够支持。在本文中，我们presentGruM（生成CPSRuntimeMonitor），一个框架，结合模型驱动技术和运行时监控，自动生成一个定制的监控平台，为givenSuM。相关的属性被捕获到一个域模型片段中，对SUM的更改可以通过自动重新生成平台代码来轻松适应。为了证明的可行性和性能，我们评估GRuM对两个不同的系统使用TurtleBot机器人和无人机。结果表明，GruM有利于创建和发展的运行时监控平台，很少的努力，该平台可以处理大量的事件和数据。
Runtime monitoring is critical for ensuring safe operation and for enabling self-adaptive behavior of Cyber-Physical Systems (CPS). Monitors are established by identifying runtime properties of interest, creating probes to instrument the system, and defining constraints to be checked at runtime. For many systems, implementing and setting up a monitoring platform can be tedious and time-consuming, as generic monitoring platforms do not adequately cover domain-specific monitoring requirements. This situation is exacerbated when the System under Monitoring (SuM) evolves, requiring changes in the monitoring platform. Most existing approaches lack support for the automated generation and setup of monitors for diverse technologies and do not provide adequate support for dealing with system evolution. In this paper, we presentGRuM(Generating CPSRuntimeMonitors), a framework that combines model-driven techniques and runtime monitoring, to automatically generate a customized monitoring platform for a givenSuM. Relevant properties are captured in a Domain Model Fragment, and changes to theSuMcan be easily accommodated by automatically regenerating the platform code. To demonstrate the feasibility and performance we evaluatedGRuMagainst two different systems using TurtleBot robots and Unmanned Aerial Vehicles. Results show thatGRuMfacilitates the creation and evolution of a runtime monitoring platform with little effort and that the platform can handle a substantial amount of events and data.