摘要:pyCPA通信线程中任务链的响应时间分析

Johannes Schlatow, Jonas Peeck, R. Ernst
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引用次数: 0

摘要

只提供摘要形式。当为时序分析建模软件组件时,我们通常会遇到导致优先关系的任务功能链。由于这些任务链表示与功能相关的操作序列,因此在实时系统中,通常需要它们的端到端延迟。当映射到软件组件时,功能链通常导致通信线程。由于线程是被调度的,而不是任务,因此出现了特定的任务链属性,可以通过扩展静态优先级抢占系统中此类任务链的忙窗分析来利用这些属性进行响应时间分析。我们通过pyCPA的分析扩展来实现这个分析,pyCPA是一个研究级的成分性能分析(CPA)实现。本演示的主要范围是展示如何在实际的基于组件的系统中合理地执行CPA。它还演示了如何使用pyCPA分析框架对CPA进行研究。在本演示的过程中,我们展示了两种提取适当时序模型的方法:1)从基于契约的软件组件规范中派生,以及2)适用于黑盒组件的基于跟踪的方法。我们还演示了如何将这个计时模型输入到分析扩展中,以获得感兴趣的任务链的响应时间结果。最后,我们介绍了开发的分析扩展如何加速CPA,从而实现自动的设计空间探索和线程优先级分配的优化,以满足预定义的延迟需求。
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Demo Abstract: Response-Time Analysis for Task Chains in Communicating Threads with pyCPA
Summary form only given. When modelling software components for timing analysis, we typically encounter functional chains of tasks that lead to precedence relations. As these task chains represent a functionally-dependent sequence of operations, in real-time systems, there is usually a requirement for their end-to-end latency. When mapped to software components, functional chains often result in communicating threads. Since threads are scheduled rather than tasks, specific task chain properties arise that can be exploited for response-time analysis by extending the busy-window analysis for such task chains in static-priority preemptive systems. We implemented this analysis by means of an analysis extension for pyCPA, a research-grade implementation of compositional performance analysis (CPA). The major scope of this demo is to show how CPA can be reasonably performed for realistic component-based systems. It also demonstrates how research on and with CPA is conducted using the pyCPA analysis framework. In the course of this demo, we show two approaches for the extraction of an appropriate timing model: 1) the derivation from a contract-based specification of the software components and 2) a tracing-based approach suitable for black-box components. We also demonstrate how this timing model is fed into the analysis extension in order to obtain response-time results for the task chains of interest. Finally, we present how the developed analysis extension speeds up the CPA and therefore enables an automated design-space exploration and optimisation of the threads' priority assignments in order to satisfy the pre-defined latency requirements.
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