CNS Core: Medium: Collaborative Research: Towards Enabling Optimal Performance-Cost Tradeoffs in Distributed Storage
CNS 核心:中:协作研究:实现分布式存储中的最佳性能与成本权衡
基本信息
- 批准号:1900665
- 负责人:
- 金额:$ 69.24万
- 依托单位:
- 依托单位国家:美国
- 项目类别:Continuing Grant
- 财政年份:2019
- 资助国家:美国
- 起止时间:2019-10-01 至 2023-09-30
- 项目状态:已结题
- 来源:
- 关键词:
项目摘要
Modern Internet services aim to be always available and provide low latency responses. Typically, this is achieved by storing data on multiple sites close to the end-users. Such an arrangement imposes a fundamental trade-off between response times for read and write requests to storage systems and data storage/transfer costs. Existing distributed storage solutions for addressing this trade-off may result in sub-optimal outcomes. First, realizing different performance-vs-cost trade-offs today requires using radically different solutions with little choice in between. Also, many trade-off points that appear theoretically feasible are currently unachievable in practice. This research project aims to design and build a distributed storage solution that offers a single design to realize a wide range of feasible latency-cost trade-offs.The overarching goal of this project is to design next-generation distributed storage solutions (1) that can be configured to achieve all feasible performance-cost trade-offs on mutable data, and (2) that enables a significant portion of the theoretically feasible trade-off space that is currently unachievable. Specifically, the project involves overcoming the following key challenges: (1) Seamless support for low latency and low cost; (2) Ensuring high performance across widely varying network latency domains and object sizes; and (3) Efficiently maintaining consistency in erasure-coded data. This will allow application developers to select from all feasible points in the trade-off space, using replication or erasure coding, without having to redesign their services.By making a broader region of the trade-off space accessible to cloud service providers, this project will help reduce the price of cloud storage for end users. Additionally, lowering achievable latency bounds enables new, low-cost, geo-distributed services and applications that are interactive and collaborative. The researchers will work with industry partners to apply the outcomes from this project in practice, and leverage the results from this project in classes they teach. Research exposure for undergraduate students will be promoted through research internships. The researchers plan to build upon several already-established outreach activities to help improve diversity of the student population in computer science.The results from the research project, including software, will be made available at: github.com/cns1901410.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.
现代Internet服务的目标是始终可用并提供低延迟响应。通常,这是通过将数据存储在靠近最终用户的多个站点来实现的。这种安排在对存储系统的读写请求的响应时间和数据存储/传输成本之间进行了基本的权衡。用于解决这种权衡的现有分布式存储解决方案可能会导致次优结果。首先,今天实现不同的性能与成本权衡需要使用完全不同的解决方案,而两者之间几乎没有选择。此外,许多理论上可行的折衷点目前在实践中无法实现。本研究项目旨在设计和构建一个分布式存储解决方案,该解决方案提供单一设计,以实现广泛可行的延迟成本权衡。该项目的总体目标是设计下一代分布式存储解决方案(1),该解决方案可以配置为在可变数据上实现所有可行的性能成本权衡,以及(2)实现很大一部分理论上可行的权衡空间,这是目前无法实现的。具体而言,该项目涉及克服以下关键挑战:(1)无缝支持低延迟和低成本;(2)确保在广泛变化的网络延迟域和对象大小上的高性能;(3)有效维护擦除编码数据的一致性。这将允许应用程序开发人员从权衡空间中的所有可行点中进行选择,使用复制或擦除编码,而不必重新设计他们的服务。通过为云服务提供商提供更广泛的权衡空间,该项目将有助于降低最终用户的云存储价格。此外,降低可实现的延迟界限使新的、低成本的、地理分布式的服务和应用程序具有互动性和协作性。研究人员将与行业合作伙伴合作,将该项目的成果应用于实践,并在他们的课堂上利用该项目的成果。通过研究实习促进本科生的研究接触。研究人员计划在几个已经建立的外展活动的基础上,帮助提高计算机科学学生群体的多样性。研究项目的结果,包括软件,将在github.com/cns1901410.This上提供。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智力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
期刊论文数量(7)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
ModelKeeper: Accelerating DNN Training via Automated Training Warmup
- DOI:
- 发表时间:2023
- 期刊:
- 影响因子:15
- 作者:Fan Lai;Yinwei Dai;H. Madhyastha;Mosharaf Chowdhury
- 通讯作者:Fan Lai;Yinwei Dai;H. Madhyastha;Mosharaf Chowdhury
AdaEmbed: Adaptive Embedding for Large-Scale Recommendation Models
AdaEmbed:大规模推荐模型的自适应嵌入
- DOI:
- 发表时间:2023
- 期刊:
- 影响因子:0
- 作者:Lai, Fan;Zhang, Wei;Liu, Rui;Tsai, William;Wei, Xiaohan;Hu, Yuxi;Devkota, Sabin;Huang, Jianyu;Park, Jongsoo;Liu, Xing
- 通讯作者:Liu, Xing
Sol: Fast Distributed Computation Over Slow Networks
- DOI:
- 发表时间:2020
- 期刊:
- 影响因子:0
- 作者:Fan Lai;Jie You;Xiangfeng Zhu;H. Madhyastha;Mosharaf Chowdhury
- 通讯作者:Fan Lai;Jie You;Xiangfeng Zhu;H. Madhyastha;Mosharaf Chowdhury
Hydra : Resilient and Highly Available Remote Memory
- DOI:
- 发表时间:2019-10
- 期刊:
- 影响因子:0
- 作者:Youngmoon Lee;H. Maruf;Mosharaf Chowdhury;Asaf Cidon;K. Shin
- 通讯作者:Youngmoon Lee;H. Maruf;Mosharaf Chowdhury;Asaf Cidon;K. Shin
Egeria: Efficient DNN Training with Knowledge-Guided Layer Freezing
- DOI:10.1145/3552326.3587451
- 发表时间:2022-01
- 期刊:
- 影响因子:0
- 作者:Yiding Wang;D. Sun;Kai Chen-;Fan Lai;Mosharaf Chowdhury
- 通讯作者:Yiding Wang;D. Sun;Kai Chen-;Fan Lai;Mosharaf Chowdhury
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Mosharaf Chowdhury其他文献
CDI-E: An Elastic Cloud Service for Data Engineering
CDI-E:数据工程的弹性云服务
- DOI:
10.14778/3554821.3554825 - 发表时间:
2022 - 期刊:
- 影响因子:0
- 作者:
Prakash Das;Shivangi Srivastava;Valentin Moskovich;Anmol Chaturvedi;Anant Mittal;Yongqin Xiao;Mosharaf Chowdhury - 通讯作者:
Mosharaf Chowdhury
Fair Allocation of Heterogeneous and InterchangeableResources
异构和可互换资源的公平分配
- DOI:
10.1145/3305218.3305227 - 发表时间:
2019 - 期刊:
- 影响因子:0
- 作者:
Xiao Sun;T. Le;Mosharaf Chowdhury;Zhenhua Liu - 通讯作者:
Zhenhua Liu
Pyxis: Scheduling Mixed Tasks in Disaggregated Datacenters
Pyxis:在分类数据中心调度混合任务
- DOI:
- 发表时间:
2024 - 期刊:
- 影响因子:5.3
- 作者:
Sheng Qi;Chao Jin;Mosharaf Chowdhury;Zhenming Liu;Xuanzhe Liu;Xin Jin - 通讯作者:
Xin Jin
Coflow: A Networking Abstraction for Distributed Data-Parallel Applications
- DOI:
- 发表时间:
2015 - 期刊:
- 影响因子:0
- 作者:
Mosharaf Chowdhury - 通讯作者:
Mosharaf Chowdhury
Resource Management in Multi-* Clusters : Cloud Provisioning
多*集群中的资源管理:云配置
- DOI:
- 发表时间:
2010 - 期刊:
- 影响因子:0
- 作者:
Mosharaf Chowdhury - 通讯作者:
Mosharaf Chowdhury
Mosharaf Chowdhury的其他文献
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{{ truncateString('Mosharaf Chowdhury', 18)}}的其他基金
Collaborative Research: Conference: NSF NeTS PI Meeting - Spring 2023
协作研究:会议:NSF NeTS PI 会议 - 2023 年春季
- 批准号:
2309858 - 财政年份:2023
- 资助金额:
$ 69.24万 - 项目类别:
Standard Grant
Collaborative Research: NGSDI: Foundations of Clean and Balanced Datacenters: Treehouse
合作研究:NGSDI:清洁和平衡数据中心的基础:Treehouse
- 批准号:
2104243 - 财政年份:2021
- 资助金额:
$ 69.24万 - 项目类别:
Continuing Grant
Collaborative Research: CNS Core: Medium: Systems Support for Federated Learning
协作研究:CNS 核心:中:联邦学习的系统支持
- 批准号:
2106184 - 财政年份:2021
- 资助金额:
$ 69.24万 - 项目类别:
Continuing Grant
CAREER: End-to-End Network Design for Unified Memory Disaggregation
职业:统一内存分解的端到端网络设计
- 批准号:
1845853 - 财政年份:2019
- 资助金额:
$ 69.24万 - 项目类别:
Continuing Grant
CNS Core: Small: Multi-Scale GPU Resource Management for AI Applications
CNS 核心:小型:AI 应用的多规模 GPU 资源管理
- 批准号:
1909067 - 财政年份:2019
- 资助金额:
$ 69.24万 - 项目类别:
Standard Grant
NeTS: CSR: Medium: Collaborative Research: Enabling Flexible and High Performance Big Data Analytics Over Geo-Distributed Clouds
NeTS:CSR:中:协作研究:通过地理分布式云实现灵活且高性能的大数据分析
- 批准号:
1563095 - 财政年份:2016
- 资助金额:
$ 69.24万 - 项目类别:
Continuing Grant
XPS: FULL: A Cross-Layer Approach Toward Low-Latency Data-Parallel Applications in Rack-Scale Computing
XPS:FULL:机架规模计算中低延迟数据并行应用的跨层方法
- 批准号:
1629397 - 财政年份:2016
- 资助金额:
$ 69.24万 - 项目类别:
Standard Grant
NeTS: Small: Collaborative Research: Enabling Application-Level Performance Predictability in Public Clouds
NeTS:小型:协作研究:在公共云中实现应用程序级性能可预测性
- 批准号:
1617773 - 财政年份:2016
- 资助金额:
$ 69.24万 - 项目类别:
Standard Grant
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