CRII: SHF: A Novel Address Translation Architecture for Virtualized Clouds

由于其成本效益和能源效率，持久内存（PM）已经越来越多地部署在云环境中，在云环境中，PM可以被虚拟化并作为虚拟机中的内存和存储设备使用。从虚拟地址（虚拟内存地址或文件逻辑地址）到物理地址（内存或存储上的物理位置）的地址转换对PM内存系统和PM存储系统的性能都至关重要。然而，最先进的地址转换会对云计算中的整体系统性能产生负面影响，因为它们最初是为动态随机存取存储器（DRAM）虚拟化和传统存储系统（如硬盘驱动器（hdd）和固态驱动器（ssd））设计的。该项目旨在从根本上改变地址转换在云环境中的完成方式，特别是有利于PM内存和PM存储。这个想法是通过广泛的实验来利用独特的观察，绕过和缩短传统技术的漫长翻译过程。该项目的成功有可能推进下一代云计算，并通过将研究成果整合到新课程中，并向大学和K-12学生教授新兴的云计算技术，为教育活动做出贡献。本项目介绍了XLANE，这是一种通用、有效、可扩展的地址转换架构，适用于高效的虚拟化云。目标是从根本上改变传统的地址转换技术，同时保持与默认设计的完全兼容性。XLANE包括两种技术：直接记忆翻译（DMT）和直接文件翻译（DFT）。DMT支持在内存虚拟化的每个地址转换中将顺序内存访问的次数从24次减少到2次。DFT实现了第一个直接文件映射设计，用于虚拟化超低延迟存储设备，如具有高效直接文件转换的PM。XLANE是一种通用方法，它不仅有利于PM系统，也有利于基于计算快速链接（CXL）的内存系统、分层内存系统和许多其他系统。该项目将使现代云和数据中心能够在虚拟化的PM和基于cxl的内存上运行新兴的数据密集型应用程序（例如，图形计算和人工智能），具有卓越的性能和可扩展性，以及低开销和能耗。本项目由软件和硬件基金会（SHF）核心研究计划和促进竞争研究的既定计划（EPSCoR）共同资助。该奖项反映了美国国家科学基金会的法定使命，并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，被认为值得支持。

Due to its cost effectiveness and energy efficiency, persistent memory (PM) has been increasingly deployed in cloud environments where PM can be virtualized and utilized as both memory and storage devices in virtual machines. Address translation from virtual (a virtual memory address or a file logical address) to physical (a physical location on memory or storage) is critical to the performance of both PM memory system and PM storage system. The state-of-the-art address translations, however, negatively impact the overall system performance in cloud computing because they were originally designed for dynamic random access memories (DRAM) virtualization and traditional storage systems such as hard disk drives (HDDs) and solid state drives (SSDs). This project aims to fundamentally change how address translation is done in the cloud environments specifically beneficial to PM memory and PM storage. The idea is to bypass and short-circuit the lengthy translation process of traditional techniques by leveraging unique observations through extensive experiments. The success of this project has potential to advance next-generation cloud computing and contribute to educational activities by integrating research outcomes into new curriculum and teaching emerging cloud computing technologies to both college and K-12 students. This project introduces XLANE, a general, effective, and scalable address translation architecture for virtualized clouds with high efficiency. The goal is to fundamentally change the traditional address translation techniques while retaining full compatibility to the default designs. XLANE includes two techniques: direct memory translation (DMT) and direct file translation (DFT). DMT enables the reduction of the number of sequential memory accesses from 24 to two in each address translation for memory virtualization. DFT achieves the first direct file mapping design for virtualizing ultra-low latency storage devices like PM with efficient direct file translation. XLANE is a general approach that benefits not only PM systems but also compute express link (CXL)-based memory systems, tiered memory systems, and many others. This project will enable modern clouds and data centers to run emerging data-intensive applications (e.g., graph computing and artificial intelligence) on virtualized PM and CXL-based memory with exceptional performance and scalability, as well as low overhead and energy consumption.This project is jointly funded by the Software and Hardware Foundation (SHF) core research program and the Established Program to Stimulate Competitive Research (EPSCoR).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.