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、移动的设备和嵌入式设备的基本软件组件。然而，内核很大，特别复杂，使得它们非常难以正确实现，并且容易出现软件错误。这个项目开发了测试技术，特别适合于在现代内核中发现软件错误，这些内核是高度并发的。该项目旨在开发有效的技术，以帮助开发人员在部署到用户之前发现内核缺陷。该项目开发的方法，发现和分析依赖于时间表的非确定性，以找到具有挑战性的内核并发错误类。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，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时间表。其次，它开发了数据流感知技术，通过产生具有代表性的顺序测试来提高顺序测试的生成，这些顺序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有重大影响的细微语义错误。这项工作提高了几乎所有类别计算机系统的可靠性和安全性，包括物联网设备、消费者台式机、数据中心服务器和关键基础设施。此外，这项工作降低了开发、测试和运营成本，并减少了进入部署系统的错误的发生。因此，该项目减少了数十亿用户使用的各种系统的停机、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，以确保从奖项结束或公开发布（以较晚者为准）起至少七年的安全长期存储。产生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，这些材料将位于https://www.cs.purdue.edu/homes/pfonseca/projects/reliable-concurrent-os.html.This奖项上，反映了NSF的法定使命，并被认为值得通过使用基金会的知识价值和更广泛的影响审查标准进行评估来支持。

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