Collaborative Research: CNS CORE: Medium: The Case for Blended Storage

合作研究：CNS CORE：Medium：混合存储案例

• 批准号: 2107027
• 负责人: Asaf Cidon
• 金额: $ 37.5万
• 依托单位: Columbia University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-07-01 至 2025-06-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2107027&HistoricalAwards=false
• 关键词: Collaborative; Research; CNS; CORE; Medium

In recent years, data storage technology has been designed for one of two objectives: either improved performance or a lower cost per bit. Consequently, significant effort has gone into building new storage systems that are organized either to be very fast (but expensive) or very cheap (but slow). This proposal takes a different approach: it makes the case for blended storage systems, i.e., software systems that combine multiple different storage technologies, to achieve both high performance and low cost per bit.Widely-used storage data structures and algorithms are designed for a single storage medium in mind, which leads to poor performance when they are run on multiple different storage technologies with different characteristics. Therefore, the proposal rethinks the fundamental data structures and algorithms that underpin modern storage systems for blended storage, including: indexing, data placement, logging, caching, prefetching and approximate membership structures. The proposal will design and implement these data structures and algorithms in a prototype key-value store that is designed specifically for blended storage.The proposed research will redesign widely-used data structures and algorithms for key-value stores to take advantage of blended storage technologies for fast data access at a low cost. Since key-value stores underpin many cloud data systems, the immediate benefits will include high performing data systems at a reduced cost. These will in turn translate to cost-effective cloud services for end users. The PIs will release the software artifacts under open-source licenses to enable other researchers and will work with hardware and cloud providers to transition the research into practice. The education plan will elevate storage-centric systems design as an integral part of systems education.The project repository will be hosted on Github at https://github.com/princeton-sns and maintained indefinitely.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.

近年来，数据存储技术的设计是为了实现以下两个目标之一：提高性能或降低每比特成本。因此，人们投入了大量精力来构建新的存储系统，这些系统要么非常快（但昂贵），要么非常便宜（但速度很慢）。该提案采用了不同的方法：它为混合存储系统（即结合多种不同存储技术的软件系统）提供了理由，以实现高性能和每位低成本。广泛使用的存储数据结构和算法是为考虑到单一存储介质，当它们在具有不同特性的多种不同存储技术上运行时，这会导致性能不佳。因此，该提案重新考虑了支持现代混合存储存储系统的基本数据结构和算法，包括：索引、数据放置、日志记录、缓存、预取和近似成员资格结构。该提案将在专为混合存储设计的原型键值存储中设计和实现这些数据结构和算法。该提案的研究将重新设计广泛使用的键值存储数据结构和算法，以利用混合存储技术以低成本实现快速数据访问。由于键值存储是许多云数据系统的基础，因此直接的好处将包括以更低的成本实现高性能数据系统。这些反过来又将为最终用户转化为具有成本效益的云服务。 PI 将根据开源许可发布软件工件，以便其他研究人员能够使用，并将与硬件和云提供商合作，将研究成果转化为实践。该教育计划将以存储为中心的系统设计提升为系统教育的一个组成部分。该项目存储库将托管在 Github（https://github.com/princeton-sns）上并无限期维护。该奖项反映了 NSF 的法定使命，并已通过使用基金会的智力优点和更广泛的影响审查标准进行评估，被认为值得支持。