CSR-EHS Predictable Adaptive Residual Monitoring for Real-time Embedded Systems

适用于实时嵌入式系统的 CSR-EHS 可预测自适应残留监测

• 批准号: 0720654
• 负责人: Matthew Dwyer
• 金额: $ 50万
• 依托单位: University of Nebraska-Lincoln
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-09-01 至 2012-02-29
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0720654&HistoricalAwards=false
• 关键词: CSR; EHS; Predictable; Adaptive; Residual

The use of software to control devices deployed, for example, in medical, transportation, and power systems demands correct software operation. Current approaches to assuring the correct operation of embedded software face significant challenges in scaling to the next generation of embedded applications. A promising strategy for meeting these challenges is to deploy "monitoring" software that will "watch" and "warn" of problematic situations that the operational software should adapt to avoid. Unfortunately, current approaches for software monitoring result in excessive overhead and are not sensitive to the fundamental timeliness requirements in embedded software.This project is investigating technologies for the timely and efficient monitoring of software in embedded systems. Specifically, the project explores the synergistic combination of three approaches: (1) exploiting the results of static analysis to calculate a minimal "residual" analysis problem to be monitored at run-time, (2) "adapting" the degree of observation of the software dynamically during execution while preserving the fidelity of monitoring, and (3) using scheduling techniques to ensure that monitors detect patterns of software behavior with a "predictable" worst-case delay. Together these techniques are being combined into a single predictable, adaptable, residual (PAR) monitoring infrastructure within which a variety of implementation strategies will be realized. Evaluation of the cost-effectiveness of these techniques will be carried out in the context of RTSJ and sensor-network infrastructures and applications. These techniques and the PAR infrastructure are the basis for projects in both real-time systems and software validation courses to train the next generation of embedded software engineers.

使用软件来控制部署在医疗、运输和电力系统中的设备需要正确的软件操作。 当前确保嵌入式软件正确运行的方法在扩展到下一代嵌入式应用方面面临着重大挑战。 应对这些挑战的一个有希望的策略是部署“监视”软件，该软件将“监视”和“警告”操作软件应该适应以避免的问题情况。 不幸的是，目前的软件监控方法导致过多的开销，并不敏感的基本实时性要求在嵌入式software.This项目正在研究技术，及时和有效的监控嵌入式系统中的软件。 具体而言，该项目探讨了三种方法的协同组合：（1）利用静态分析的结果来计算要在运行时监视的最小“剩余”分析问题，（2）在执行期间动态地“适配”软件的观察程度，同时保持监视的保真度，以及（3）使用调度技术来确保监视器以“可预测的”最坏情况延迟来检测软件行为的模式。 这些技术正被组合成一个单一的可预测的，可适应的，剩余（PAR）的监测基础设施，在其中各种实施战略将被实现。 将在RTSJ和传感器网络基础设施和应用的背景下对这些技术的成本效益进行评估。这些技术和PAR基础设施是实时系统和软件验证课程项目的基础，以培养下一代嵌入式软件工程师。