CSR: Small: Heterogeneous Storage Systems with Emerging Technologies for Solving Big Data Problems

CSR：小型：利用新兴技术解决大数据问题的异构存储系统

• 批准号: 1812537
• 负责人: David Du
• 金额: $ 49.91万
• 依托单位: University of Minnesota-Twin Cities
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-08-01 至 2023-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1812537&HistoricalAwards=false
• 关键词: CSR; Small; Heterogeneous; Storage; Systems

The past decade has witnessed tremendous advances in computing, communication and storage technologies. With the unprecedented connectivity provided by Internet, many new data-driven applications have emerged and are being developed. The information technology (IT) infrastructure of this new era calls for low cost and high-performance storage, flexible ways of processing data for decision making and information retrieval, and keeping data for extremely long durations. This project intends to address certain aspects of these challenges. That is, 1) developing heterogeneous storage systems with low cost and high performance based on emerging storage technologies and 2) leveraging active storage devices for supporting decision making and information retrieval. An active storage device is a storage device having some additional, but limited processing power and can process data locally on the device. Active storage devices can be used to effectively filter out the unnecessary data from storage to memory to support decision making and information retrieval. This project will encourage innovative and creative thinking for integrated solutions that combine emerging storage technologies/devices to build storage systems supporting decision making and information retrieval, enhancing the fundamental understanding of large scale data management in big data era, and quickly developing prototypes to demonstrate the capabilities of these designs. This project is also closely collaborated with a number of industrial companies through NSF Industry-University Cooperative Research Center for Research in Intelligent Storage (CRIS). The outcomes of the project can be quickly adopted by storage industry. The expected project outcomes include fostering the advancement of science and technology especially for data-driven type of scientific research, making society more efficient with big data solutions/applications, and creating better ways of managing and migrating huge amount of available data with better performance and lower cost. Through collaboration with storage industry, the project will provide an ideal hands-on learning and development environment to teach both graduate and undergraduate students important system building and experimental skills that are critical for today's IT workforce. The project outcomes will be incorporated into classroom teaching of both course projects and the core courses in computer science and data science programs for both graduate and under-graduate students. The outcomes of the project include managing algorithms for new storage technologies/devices, new storage architectures, different storage systems designs, new data models, information access methods/algorithms and new ways to deliver information and making decisions. The research outcomes will be deposited and available to public in http://cris.cs.umn.edu.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.

过去十年中，在计算，通信和存储技术方面取得了巨大进步。随着Internet提供的前所未有的连接性，许多新的数据驱动应用程序已经出现并正在开发中。这个新时代的信息技术（IT）基础架构要求低成本和高性能存储，灵活的处理数据来制定决策和信息检索，并将数据保留在极长的持续时间内。 该项目旨在解决这些挑战的某些方面。也就是说，1）基于新兴存储技术的基于新成本和高性能的异质存储系统，以及2）利用主动存储设备来支持决策制定和信息检索。 主动存储设备是一种存储设备，具有一些额外但有限的处理能力，并且可以在设备上本地处理数据。活动存储设备可用于有效地滤除从存储到内存的不必要数据，以支持决策制定和信息检索。该项目将鼓励对集成解决方案进行创新和创造性的思维，这些解决方案结合新兴的存储技术/设备，以构建支持决策和信息检索的存储系统，增强对大数据时代大规模数据管理的基本了解，并迅速开发原型以证明这些设计的功能。 该项目还通过NSF行业 - 大学合作研究中心（CRIS）与许多工业公司密切合作。该项目的结果可以由存储行业迅速采用。预期的项目成果包括促进科学和技术的发展，尤其是针对数据驱动的科学研究类型，使社会通过大数据解决方案/应用程序提高了效率，并创造了更好的方法来管理和迁移大量可用数据，具有更好的性能和更低的成本。 通过与存储行业的合作，该项目将提供理想的动手学习和开发环境，以教授研究生和本科生重要的系统构建和实验技能，这对于当今的IT劳动力至关重要。该项目成果将纳入课程项目的课堂教学和研究生和本科生的计算机科学和数据科学课程中的核心课程。该项目的结果包括管理新的存储技术/设备的算法，新的存储架构，不同的存储系统设计，新数据模型，信息访问方法/算法以及提供信息并做出决策的新方法。 该研究结果将被存入并在http：//cris.cs.umn.edu.Edu.Edu中向公众提供，该奖项反映了NSF的法定任务，并使用基金会的知识分子优点和更广泛的影响审查标准，认为值得通过评估来获得支持。

项目成果

Sliding Look-Back Window Assisted Data Chunk Rewriting for Improving Deduplication Restore Performance

滑动回溯窗口辅助数据块重写以提高重复数据删除恢复性能

• 发表时间: 2019
• 期刊: Usenix conference on File and Storage Technologies
• 作者: Zhichao Cao, Shiyong Liu

Can We Store the Whole World Data in DNA-Storage?

我们可以将全世界的数据存储在 DNA 存储器中吗？

• 发表时间: 2020
• 期刊: Usenix HotStorage 2020
• 作者: Bingzhe Li, Nae-Young Song

WAS-Deletion: Workload-Aware Secure Deletion Scheme for Solid-State Drives

• DOI: 10.1109/iccd53106.2021.00047
• 发表时间: 2021-10
• 期刊: 2021 IEEE 39th International Conference on Computer Design (ICCD)
• 作者: Bingzhe Li;D. Du

HintStor: A Framework to Study I/O Hints in Heterogeneous Storage

• DOI: 10.1145/3489143
• 发表时间: 2022-03
• 期刊: ACM Transactions on Storage (TOS)
• 作者: Xiongzi Ge;Zhichao Cao;D. Du;P. Ganesan;Dennis Hahn

TransKV: A Networking Support for Transaction Processing in Distributed Key-value Stores

TransKV：分布式键值存储中事务处理的网络支持

• DOI: 10.1109/bigdataservice52369.2021.00011
• 发表时间: 2021
• 期刊: IEEE International Conference on Big Data Computing Service and Applications (BigDataService
• 作者: Eldakiky, Hebatalla;Du, David Hung-Chang
• 通讯作者: Du, David Hung-Chang