Collaborative Research: CNS Core: Medium: Reinvented Data Plane for Memory-Disaggregated Datacenters

协作研究：CNS 核心：中：为内存分解数据中心重新发明数据平面

• 批准号: 2106838
• 负责人: Harry Xu
• 金额: $ 82.67万
• 依托单位: University of California-Los Angeles
• 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: 2021-08-01 至 2025-07-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2106838&HistoricalAwards=false
• 关键词: Collaborative; Research; CNS; Core; Medium

Cloud computing has become one of the most crucial elements behind how the human society operates today. The cloud is powered by large datacenters, each containing thousands of machines deployed across the globe. Recently, resource disaggregation has been proposed as an effective means to improve datacenter reliability and resource utilization by building each resource type as a standalone resource “blade” and connecting different resource blades with a network fabric. This project develops a novel kernel/runtime data plane that allows users to run their applications efficiently on memory-disaggregated clusters. It builds on an important insight: the current kernel/runtime modules do not have any coordination between themselves, leading to severe performance problems. We solve the problem with four major thrusts. The first two thrusts develop semantics-aware caching, prefetching, and swapping modules so that these low-level systems can operate with the consideration of what applications need, improving efficiency. The last two thrusts develop disaggregation-aware runtime and application support, making it possible for application/runtime developers to leverage objects' location information to unlock massive optimization opportunities. This project builds a brand-new kernel/runtime data plane, making it possible for future resource-disaggregated datacenters to deliver superior efficiency to user applications. The development of the project includes a postdoctoral scholar as well as several graduate and undergraduate students, including students from minority groups. It not only advances the state of the art of resource disaggregation, but also trains current and future system developers. The project produces a set of artifacts including technical reports, published papers, as well as code repositories. All artifacts are made available online at the PIs' websites.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.

云计算已经成为当今人类社会运作方式背后最关键的因素之一。 云计算由大型数据中心提供支持，每个数据中心包含部署在地球仪上的数千台机器。 最近，已经提出资源分解作为通过将每个资源类型构建为独立的资源“刀片”并将不同的资源刀片与网络结构连接来提高数据中心可靠性和资源利用率的有效手段。这个项目开发了一个新的内核/运行时数据平面，允许用户在内存分散的集群上有效地运行他们的应用程序。它建立在一个重要的见解上：当前的内核/运行时模块之间没有任何协调，导致严重的性能问题。我们用四个主要的推力来解决这个问题。前两个目标是开发语义感知的缓存、预取和交换模块，以便这些低级系统可以考虑应用程序的需求进行操作，从而提高效率。 最后两个目标是开发分解感知的运行时和应用程序支持，使应用程序/运行时开发人员能够利用对象的位置信息来解锁大量优化机会。 该项目构建了一个全新的内核/运行时数据平面，使未来的资源分解的数据中心能够为用户应用程序提供上级效率。该项目的开发包括一名博士后学者以及几名研究生和本科生，包括少数群体的学生。它不仅推进了资源分解的艺术状态，而且还培训了当前和未来的系统开发人员。该项目产生了一组工件，包括技术报告，发表的论文，以及代码存储库。该奖项反映了NSF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。

项目成果

Hermit: Low-Latency, High-Throughput, and Transparent Remote Memory via Feedback-Directed Asynchrony

• 发表时间: 2023
• 作者: Yifan Qiao;Chenxi Wang;Zhenyuan Ruan;A. Belay;Q. Lu;Yiying Zhang;Miryung Kim;Guoqing Harry Xu

Bamboo: Making Preemptible Instances Resilient for Affordable Training of Large DNNs

• DOI: 10.48550/arxiv.2204.12013
• 发表时间: 2022-04
• 期刊: ArXiv
• 作者: John Thorpe;Pengzhan Zhao;Jon Eyolfson;Yifan Qiao;Zhihao Jia;Minjia Zhang;R. Netravali;Guoqing Harry

Accelerating Graph Mining Systems with Subgraph Morphing

• DOI: 10.1145/3552326.3567489
• 发表时间: 2023-05
• 期刊: Proceedings of the Eighteenth European Conference on Computer Systems
• 作者: Kasra Jamshidi;Harry Xu;Keval Vora

Canvas: Isolated and Adaptive Swapping for Multi-Applications on Remote Memory

• DOI: 10.48550/arxiv.2203.09615
• 发表时间: 2022-03
• 作者: Chenxi Wang;Yifan Qiao;Haoran Ma;Shiafun Liu;Yiying Zhang;Wenguang Chen;R. Netravali;Miryung Kim;Guoqing Harry Xu

GEMEL: Model Merging for Memory-Efficient, Real-Time Video Analytics at the Edge

• 发表时间: 2022-01
• 作者: Arthi Padmanabhan;Neil Agarwal;Anand Iyer;Ganesh Ananthanarayanan;Yuanchao Shu;Nikolaos Karianakis;G. Xu;R. Netravali