Collaborative Research: CNS Core: Medium: Reconfigurable Kernel Datapaths with Adaptive Optimizations

协作研究：CNS 核心：中：具有自适应优化的可重构内核数据路径

• 批准号: 2105868
• 负责人: Thomas Anderson
• 金额: $ 60万
• 依托单位: University of Washington
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-10-01 至 2024-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2105868&HistoricalAwards=false
• 关键词: Collaborative; Research; CNS; Core; Medium

Operating systems are a key part of every computer system. They sit between applications and hardware to provide security isolation as well as the sharing of physical resources such as the CPU, memory, and disk. Since operating systems run security critical code, they are hard to change safely, yet the policies they use for resource management can have a large impact on application and user-perceived performance. This project aims to develop an easily deployable architecture for lightweight data collection in the kernel, and then to use machine learning on that data to discover and deploy better kernel policies without compromising kernel security.The key idea is called reconfigurable kernel datapaths. Many parts of the operating system kernel process requests - to send network packets to the Internet, to store or look up file data, and to start and stop applications. Each of these involves a number of policy questions that could potentially benefit from machine learning. For example, the file system is often used in a repeated way, and by learning those patterns, it can potentially anticipate application behavior to bring data from disk into memory before it is requested. Operating systems do this to some extent already, but the policies they use are only weakly informed by data and can be counterproductive on certain access patterns. Reconfigurable kernel datapaths aims to address this, by providing a standardized architecture for instrumenting kernel datapaths, gathering data on the effectiveness of kernel policies, using machine learning to discover better policies, and finally deploying those policies using the same reconfigurable kernel datapaths used to gather data in the first place.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.

操作系统是每个计算机系统的关键部分。它们位于应用程序和硬件之间，提供安全隔离以及物理资源（如CPU、内存和磁盘）的共享。由于操作系统运行安全关键代码，因此很难安全地更改它们，但它们用于资源管理的策略可能会对应用程序和用户感知的性能产生很大影响。 该项目旨在开发一个易于部署的架构，用于在内核中进行轻量级数据收集，然后使用机器学习来发现和部署更好的内核策略，而不会影响内核安全性。其核心思想称为可重构内核数据路径。 操作系统内核的许多部分处理请求--将网络数据包发送到Internet，存储或查找文件数据，以及启动和停止应用程序。 其中每一个都涉及许多可能从机器学习中受益的政策问题。例如，文件系统通常以重复的方式使用，通过学习这些模式，它可以潜在地预测应用程序的行为，以便在请求数据之前将数据从磁盘带入内存。 操作系统已经在某种程度上做到了这一点，但是它们使用的策略只受到数据的微弱影响，并且在某些访问模式上可能适得其反。 可重构内核数据路径旨在解决这一问题，它提供了一个标准化的架构，用于检测内核数据路径，收集有关内核策略有效性的数据，使用机器学习来发现更好的策略，最后，使用最初用于收集数据的相同可重新配置内核数据路径部署这些策略。该奖项反映了NSF的法定使命，并通过使用基金会的学术价值和更广泛的影响审查标准。