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: 2345339
• 负责人: Ang Chen
• 金额: $ 60万
• 依托单位: Regents of the University of Michigan - Ann Arbor
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-08-15 至 2024-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2345339&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，以提供安全隔离以及共享CPU，内存和磁盘等物理资源的共享。由于操作系统运行了关键代码，因此很难安全地更改，但是他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的影响。 该项目旨在为内核中的轻质数据收集开发易于部署的体系结构，然后在不损害内核安全的情况下使用机器学习来发现和部署更好的内核策略。 操作系统内核进程请求的许多部分 - 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。 这些中的每一个都涉及许多政策问题，这些问题可能会从机器学习中受益。例如，通常以重复的方式使用该文件系统，并且通过学习这些模式，它可能会预测应用程序行为以将数据从内存中引入内存。 操作系统已经在某种程度上这样做了，但是他们使用的政策仅由数据薄弱，并且在某些访问模式上可能会适得其反。 可重新配置的内核数据索引旨在通过提供一种标准化的体系结构来仪器内核数据，收集有关内核策略有效性的数据，利用机器学习来发现更好的策略，并最终使用相同的重新配置的措施来收集ns news ns depare ns depare。通过使用基金会的智力优点和更广泛影响的评论标准进行评估。