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SHF: Small: Redesigning the System Architecture for Ultra-High Density Data Storage

SHF: Small: Redesigning the System Architecture for Ultra-High Density Data Storage
SHF:小型:重新设计超高密度数据存储的系统架构
批准号:
1910958
负责人:
Feng Chen
金额:
$40.0万
依托单位:
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2019
资助国家:
美国
项目状态:
已结题
起止时间:
2019-10-01 至 2024-09-30

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中文摘要
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英文摘要
Computer storage systems are advancing towards extremely high density. With emerging storage technologies, such as Quad-Level Cell (QLC) and 3D vertical NAND, built using advanced 10 nanometer manufacturing processes, one single server will be able to accommodate petabytes of data in the near future. Such an unprecedented storage density growth allows us to build highly consolidated server systems at low costs, but in the meantime, it also brings a variety of critical challenges in terms of scalability, reliability, and shareability. Simply transplanting the existing system stack onto vastly condensed storage would create a severely unbalanced system - although the storage is able to accommodate a huge amount of data, the unoptimized hardware and software components in the system stack are unable to efficiently and reliably manage and deliver the data. Thus, the growth of storage density should be regarded as a fundamental system change, which demands a full-scale re-assessment on the whole system stack. This project systematically studies these challenging research issues by using a cohesive approach to reconsider the storage system architecture design at different levels. This project also aims to increase its impact by training students in research activities, contributing to educational activities, and outreach to K-12 students.The project addresses the critical technical challenges by adopting a holistic, cross-layer, whole-system approach for optimizations across multiple layers in the computer system hierarchy. Specifically, the project investigates various important aspects in the storage architecture design to re-balance the system for highly condensed storage, such as reconsidering the mapping structure for I/O efficiency, leveraging the QLC flash storage and non-volatile memory for efficient data management, offloading computationally intensive storage operations to hardware accelerators. The project also studies the intermediate layers in the system stack to manage hardware and provide simplified, semantic-rich storage services. A set of key data-intensive applications is further studied for effectively leveraging the next-generation high-density storage.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.
期刊论文(5)
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科研奖励(0)
会议论文
TSCache: an efficient flash-based caching scheme for time-series data workloads
TSCache:一种针对时间序列数据工作负载的基于闪存的高效缓存方案
DOI: 10.14778/3484224.3484225
发表时间: 2021
期刊: Proceedings of the VLDB Endowment
影响因子: 2.5
作者: [Liu, Jian, Wang, Kefei, Chen, Feng]
通讯作者: Chen, Feng
SlimCache: An Efficient Data Compression Scheme for Flash-based Key-value Caching
SlimCache:一种基于闪存的键值缓存的高效数据压缩方案
DOI: 10.1145/3383124
发表时间: 2020
期刊: ACM Transactions on Storage
影响因子: 1.7
作者: [Jia, Yichen, Shao, Zili, Chen, Feng]
通讯作者: Chen, Feng
DOI: 10.1109/icdcs47774.2020.00113
发表时间: 2020-11
期刊: 2020 IEEE 40th International Conference on Distributed Computing Systems (ICDCS)
影响因子: --
作者: [Yichen Jia;Feng Chen]
通讯作者: Yichen Jia;Feng Chen
From Flash to 3D XPoint: Performance Bottlenecks and Potentials in RocksDB with Storage Evolution
从闪存到 3D XPoint:RocksDB 存储演进的性能瓶颈和潜力
DOI: 10.1109/ispass48437.2020.00034
发表时间: 2020
期刊: Proceedings of 2020 IEEE International Symposium on Performance Analysis of Systems and Software (ISPASS
影响因子: --
作者: [Jia, Yichen, Chen, Feng]
通讯作者: Chen, Feng
ATD: Sparse and Localized Graph Convolutional Networks for Anomaly Detection and Active Learning
  • 批准号:
    2220574
  • 项目类别:
    Standard Grant
  • 资助金额:
    $10.0万
  • 财政年份:
    2023
  • 负责人:
    Feng Chen
  • 依托单位:
Collaborative Research: SHF: Medium: Hardware and Software Support for Memory-Centric Computing Systems
  • 批准号:
    2312509
  • 项目类别:
    Continuing Grant
  • 资助金额:
    $33.3万
  • 财政年份:
    2023
  • 负责人:
    Feng Chen
  • 依托单位:
FAI: A novel paradigm for fairness-aware deep learning models on data streams
  • 批准号:
    2147375
  • 项目类别:
    Standard Grant
  • 资助金额:
    $39.3万
  • 财政年份:
    2022
  • 负责人:
    Feng Chen
  • 依托单位:
Collaborative Research: SHF: Medium: A New Direction of Research and Development to Fulfill the Promise of Computational Storage
  • 批准号:
    2210755
  • 项目类别:
    Continuing Grant
  • 资助金额:
    $40.0万
  • 财政年份:
    2022
  • 负责人:
    Feng Chen
  • 依托单位:
国内基金
海外基金
昼夜节律性small RNA在血斑形成时间推断中的法医学应用研究
  • 批准号:
  • 项目类别:
    省市级项目
  • 资助金额:
    --
  • 批准年份:
    2024
  • 负责人:
  • 依托单位:
tRNA-derived small RNA上调YBX1/CCL5通路参与硼替佐米诱导慢性疼痛的机制研究
  • 批准号:
  • 项目类别:
    省市级项目
  • 资助金额:
    10.0万元
  • 批准年份:
    2022
  • 负责人:
    张祥忠
  • 依托单位:
Small RNA调控I-F型CRISPR-Cas适应性免疫性的应答及分子机制
Small RNAs调控解淀粉芽胞杆菌FZB42生防功能的机制研究
  • 批准号:
    31972324
  • 项目类别:
    面上项目
  • 资助金额:
    58.0万元
  • 批准年份:
    2019
  • 负责人:
    高学文
  • 依托单位: