基于时间自动机的AADL端到端流的延迟分析

对复杂嵌入式系统的端到端信息流进行延迟分析是一种有效的实时性评估方法.体系结构分析与设计语言(AADL)是描述复杂嵌入式系统的标准语言,其中端到端流描述组件间的通讯.目前针对AADL模型中端到端流的延迟分析,手工方法能够深入剖析流语义,分析精确度高,但耗时且低效;自动化方法虽有较高的效率,但在延迟属性的覆盖度及语义精细度上都远远不足,导致分析结果精确度低.另外,这两类方法多只关注于最坏情况等典型场景,而无法分析不确定因素的影响.本文提出一种基于时间自动机的端到端流延迟分析方法,首先总结端到端流延迟的影响属性,并建立延迟属性的元模型,在此基础上提出面向流延迟分析的时间自动机模型生成方法,通过对时间自动机的仿真实现流延迟的分析.最后通过案例说明了该方法能够正确表达流的传输语义及延迟属性语义,即有足够的表达能力;展示了方法能灵活分析多样交互场景以及随机时间因素,即有灵活的分析能力;另外仿真过程的状态变迁及时间变量变化过程也为改进设计模型提供依据与建议.

Approach to analyzing AADL end-to-end flow latency based on timed automata

End-to-end flow latency analysis of complex embedded system is an effective way to assess system's real-time performance. Architecture analysis and design language (AADL) is the standard language to specify the architecture of complex embedded system, end-to-end flow of which describes the components' interaction. At present, most manual latency analysis approaches could consider flow's precise semantics. However, the efficiency of this time consuming analysis is low; although automatic methods have better efficiency, the accuracy is not satisfying because of the low coverage of delay-contribution attributes. What's more, both kinds of methods only focus on the typical interactive situations such as the worst-latency analysis. The impact of design elements resulting in uncertainty cannot be analyzed. Thus, one approach to analyzing AADL end-to-end flow latency based on timed automata model was proposed. First, latency-contributed attributes meta-model of flow was constructed to improve the coverage of latency-contributed attributes. Then the transformation from AADL end-to-end flow to timed automata model was studied. Latency analyzing was performed through simulating the timed automata model. Finally, the experiments based on one case study demonstrate that our way can precisely model the transportation semantics and the latency attributes semantics of a flow, illustrating our way's sufficient expressive competence. Additionally, the experiments also show flexible analysis ability of our approach by dealing with the various interactive scenarios and even the random time factors in the transportation of information. Meanwhile, the state transition and the changing on the time factors provide a basis and suggestions for the further improvement of the original AADL model.