SHF: Medium: Early Reliability Modeling and Prediction of Embedded Software Systems

SHF：中：嵌入式软件系统的早期可靠性建模和预测

• 批准号: 0905665
• 负责人: Nenad Medvidovic
• 金额: $ 21万
• 依托单位: University of Southern California
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-03-01 至 2012-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0905665&HistoricalAwards=false
• 关键词: SHF; Medium; Early; Reliability; Modeling

To build high quality software, it is increasingly important for engineers to reason quantitatively about critical system properties, such as performance and reliability, as early as possible in the design process. Ideally, these properties are assessed before significant time and cost are expended on implementation. However, making useful predictions in early design stages is difficult at best due to the interplay among many relevant factors, such as complex properties of software components, effects of the firmware, and conflicts between desired system attributes. In this project, we focus on design-time evaluation of architectures with respect to one key attribute: reliability. The approach we pursue will enable an engineer to build a multi-faceted, hierarchical model of a system and assess its reliability in an incremental, scalable fashion. Moreover, we particularly focus on the area of embedded systems. Embedded systems present a rich target of opportunity for this work as (1) they demand close interplay of software and execution substrate; (2) they are often unencumbered with legacy concerns, allowing easier introduction and exploration of new techniques such as the one we propose; (3) they often have stringent and complex requirements, yet are seldom approached from a software architectural perspective. An extensive evaluation of our research focuses on two measures of interest: tractability (intended to address scalability issues existing in real, complex systems) and sensitivity (intended to address issues of confidence in our predictions under numerous design-time uncertainties).

为了构建高质量的软件，对于工程师来说，在设计过程中尽早对关键系统属性（如性能和可靠性）进行定量推理变得越来越重要。理想情况下，在花费大量时间和成本实现之前对这些属性进行评估。然而，由于许多相关因素（如软件组件的复杂属性、固件的影响以及期望的系统属性之间的冲突）之间的相互作用，在早期设计阶段做出有用的预测是很困难的。在这个项目中，我们关注架构的设计时评估，其中一个关键属性是：可靠性。我们采用的方法将使工程师能够建立一个系统的多面、分层模型，并以增量、可扩展的方式评估其可靠性。此外，我们特别关注嵌入式系统领域。嵌入式系统为这项工作提供了丰富的机会目标，因为(1)它们需要软件和执行基板的密切相互作用；(2)它们通常不受遗留问题的影响，允许更容易地引入和探索新技术，例如我们提出的技术；(3)它们通常有严格和复杂的需求，但是很少从软件架构的角度来处理。对我们研究的广泛评估集中在两个感兴趣的度量上：可追溯性（旨在解决真实复杂系统中存在的可扩展性问题）和敏感性（旨在解决在众多设计时不确定性下我们预测的信心问题）。

项目成果

Predicting Inference Latency of Neural Architectures on Mobile Devices

• DOI: 10.1145/3578244.3583735
• 发表时间: 2023-04
• 期刊: Proceedings of the 2023 ACM/SPEC International Conference on Performance Engineering
• 作者: Zhuojin Li;Marco Paolieri;L. Golubchik