Collaborative Research: CNS Core: Medium: Movement of Computation and Data in Splitkernel-disaggregated, Data-intensive Systems

合作研究：CNS 核心：媒介：Splitkernel 分解的数据密集型系统中的计算和数据移动

• 批准号: 2106388
• 负责人: Ang Chen
• 金额: $ 30万
• 依托单位: William Marsh Rice 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: 2021-06-15 至 2024-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2106388&HistoricalAwards=false
• 关键词: Collaborative; Research; CNS; Core; Medium

Resource disaggregation promises to change the way in which we design and operate cloud infrastructure. But despite its positive impact on the operation and cost-efficiency of data centers, disaggregation comes with overheads that arise from introducing a PCIe and network round trip for each access to remote memory, storage, or accelerators -- overheads that are particularly pronounced for data-intensive applications. The core research question that this proposal will answer is what the advent of disaggregation implies for the decades of architectural decisions made by the designers of data-intensive systems, and likewise, what OS-level primitives are required to adapt these systems to a disaggregated setting. To that end, this work proposes a co-design of disaggregated operating systems and middleware for data-intensive systems that achieves both high performance and ease of use, Data-intensive systems like the ones targeted by this work power much of today’s world, providing analytics for everything from business informatics to Internet search and healthcare. The proposed work will assist data center operators in transitioning their infrastructure to support resource disaggregation while minimizing performance overheads to applications written in common frameworks.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.

资源分解有望改变我们设计和运营云基础设施的方式，但尽管它对数据中心的运营和成本效率产生积极影响，但分解也会带来因每次访问引入 PCIe 和网络往返而产生的开销。远程内存、存储或加速器——对于数据密集型应用程序来说尤其明显的开销，该提案将回答的核心研究问题是，分解的出现对设计人员几十年来做出的架构决策意味着什么。数据密集型系统的数据密集型系统，以及类似地，需要哪些操作系统级原语来使这些系统适应分类设置，为此，这项工作提出了数据密集型系统的分类操作系统和中间件的协同设计，以实现这两个目标。高性能和易用性，像这项工作所针对的数据密集型系统为当今世界的大部分地区提供动力，提供从商业信息学到互联网搜索和医疗保健的各种分析。拟议的工作将帮助数据中心运营商将其基础设施转变为数据中心运营商。支持资源分解，同时最大限度地减少以通用框架编写的应用程序的性能开销。该奖项反映了 NSF 的法定使命，并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。

项目成果

Templating Shuffles

模板随机播放

• 发表时间: 2023-01
• 期刊: CIDR
• 作者: Zhang, Qizhen;Wu, Jiacheng;Chen, Ang;Liu, Vincent;Loo, Boon Thau
• 通讯作者: Loo, Boon Thau

Optimizing Data-intensive Systems in Disaggregated Data Centers with TELEPORT

使用 TELEPORT 优化分类数据中心中的数据密集型系统

• DOI: 10.1145/3514221.3517856
• 发表时间: 2022-06-10
• 期刊: Proceedings of the 2022 International Conference on Management of Data
• 作者: Qizhen Zhang;Xinyi Chen;Sidharth S Sankhe;Zhi;Ke Zhong;Sebastian Angel;Ang Chen;Vincent Liu;B. T. Loo
• 通讯作者: B. T. Loo