CAREER: Towards Reliable Operating Systems through Scalable Control- and Data-Flow Analysis

职业：通过可扩展的控制和数据流分析实现可靠的操作系统

• 批准号: 2145888
• 负责人: Pedro Fonseca
• 金额: $ 49.48万
• 依托单位: Purdue University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-01-01 至 2026-12-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2145888&HistoricalAwards=false
• 关键词: CAREER; Towards; Reliable; Operating; Systems

Operating systems kernels are an essential software component of servers, desktops, mobile devices, and embedded devices. However, kernels are large and particularly complex, making them exceptionally difficult to implement correctly and prone to software bugs. This project develops testing techniques that are especially suited to find software bugs in modern kernels, which are highly concurrent. This project is expected to develop effective techniques to help ensure developers find kernel defects before deployment to users.The project develops methods that uncover and analyze schedule-dependent non-determinism to find challenging classes of kernel concurrency bugs. This work is composed of three main components. First, it develops scalable techniques that analyze potential inter-thread communication to pair sequential tests intelligently and select schedules that expose kernel concurrency bugs. Second, it develops data-flow-aware techniques that advance sequential test generation by producing representative sequential tests that expose operating system non-determinism when combined. Third, it explores methods that analyze kernel output across schedules to detect subtle semantic bugs with a high impact on reliability and security.This work increases the reliability and security of virtually all classes of computer systems, including Internet-of-Things devices, consumer desktops, data center servers, and critical infrastructures. In addition, this work reduces the development, testing, and operational costs and reduces the occurrence of bugs that slip into deployed systems. Thus, this project reduces the incidence of downtime, loss of data, and other incorrect behavior across a wide range of systems used by billions of users.All project data is stored in public sites and university storage systems to ensure safe long-term storage for at least seven years from the award conclusion or public release, whichever comes later. The data produced includes system implementations and source code, documentation, kernel analysis datasets, and mentoring material, which will be located at https://www.cs.purdue.edu/homes/pfonseca/projects/reliable-concurrent-os.html.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

操作系统内核是服务器、台式机、移动设备和嵌入式设备的重要软件组件。然而，内核很大而且特别复杂，使得它们很难正确实现并且容易出现软件错误。该项目开发的测试技术特别适合查找高度并发的现代内核中的软件错误。该项目预计将开发有效的技术，以帮助确保开发人员在部署给用户之前找到内核缺陷。该项目开发的方法可以揭示和分析依赖于调度的非确定性，以找​​到具有挑战性的内核并发错误类别。这项工作由三个主要部分组成。首先，它开发了可扩展的技术，可以分析潜在的线程间通信，以智能地配对顺序测试，并选择暴露内核并发错误的计划。其次，它开发了数据流感知技术，通过生成代表性的顺序测试来推进顺序测试的生成，这些测试在组合时暴露操作系统的非确定性。第三，它探索了跨计划分析内核输出的方法，以检测对可靠性和安全性影响很大的微妙语义错误。这项工作提高了几乎所有类别的计算机系统的可靠性和安全性，包括物联网设备、消费台式机、数据中心服务器和关键基础设施。此外，这项工作还降低了开发、测试和运营成本，并减少了已部署系统中出现的错误。因此，该项目减少了数十亿用户使用的各种系统中的停机、数据丢失和其他不正确行为的发生率。所有项目数据都存储在公共站点和大学存储系统中，以确保安全的长期存储自奖项结束或公开发布（以较晚者为准）起至少七年。生成的数据包括系统实现和源代码、文档、内核分析数据集和指导材料，这些数据将位于 https://www.cs.purdue.edu/homes/pfonseca/projects/reliable-concurrent-os.html该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

KIT: Testing OS-Level Virtualization for Functional Interference Bugs

• DOI: 10.1145/3575693.3575731
• 发表时间: 2023-01
• 期刊: Proceedings of the 28th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, Volume 2
• 作者: Cong Liu;Sishuai Gong;Pedro Fonseca

An Extensible Orchestration and Protection Framework for Confidential Cloud Computing

• 发表时间: 2023
• 作者: Adil Ahmad;Alex Schultz;Byoungyoung Lee;Pedro Fonseca

Snowcat: Efficient Kernel Concurrency Testing using a Learned Coverage Predictor

• DOI: 10.1145/3600006.3613148
• 发表时间: 2023-10
• 期刊: Proceedings of the 29th Symposium on Operating Systems Principles
• 作者: Sishuai Gong;Dinglan Peng;Deniz Altinbüken;Google Deepmind;Petros Maniatis