Collaborative Research: CNS core: OAC core: Small: New Techniques for I/O Behavior Modeling and Persistent Storage Device Configuration

合作研究： CNS 核心：OAC 核心：小型：I/O 行为建模和持久存储设备配置新技术

• 批准号: 2008324
• 负责人: Janki Bhimani
• 金额: $ 25.51万
• 依托单位: Florida International University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-05-01 至 2024-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2008324&HistoricalAwards=false
• 关键词: Collaborative; Research; CNS; core; OAC

Currently, there is a rapidly growing diversity in data processing workloads. Likewise, new advancements in persistent storage technologies are emerging. Therefore, it is important to have new techniques for benchmarking and appropriately configuring storage systems in order to obtain the best possible performance and reliability. This project proposes to derive new input/output (I/O) models to capture I/O behaviors accurately when running multiple applications with different workloads on storage systems such as flash-based solid-state drives (SSDs). In addition, this project develops new approaches to identify the most appropriate internal algorithm for different types of persistent storage devices and dynamically adjust the associated algorithm parameters according to I/O activities.This project makes empirical contributions to storage systems by addressing challenges issued by large-scale data-intensive applications. Specifically, it advances (1) how to analyze the impact of various system components while running multiple workloads on emerging storage systems; (2) how to design interactive frameworks that allow users to modify the internal algorithms and parameters of modern storage devices; (3) how to enable novices to configure storage systems with respect to their workloads and data processing requirements; and (4) how to derive I/O models to predict future I/O workload patterns and accordingly configure storage systems in advance for better performance.This project will lead to better storage systems design with high performance and reliability. The outcome of this project will bring a significant impact on many areas that are dependent on processing a large amount of data. This project will share the findings with undergraduate and graduate students through computer science and engineering programs and open up career opportunities to female students, underrepresented minorities, and first-generation college students. This project will disseminate the proposed techniques into the industry and foster technology transfer through new industrial collaborations. The developed infrastructure will be available to the research community through a web-based portal.All the publicly disclosable NSF funded work products developed under this project will be maintained at the project website (https://damrl.cis.fiu.edu/research/) at Florida International University (FIU) for at least five years beyond the end of the project. Data generated and collected as part of this project will be deposited into Digital Repository Service (DRS) (https://repository.library.northeastern.edu/) at Northeastern University (NEU) and maintained for at least 5 years beyond the end of the project. The developed software code and tools will be published in scholarly articles and be made available online via NEU's DRS, and FIU's project website.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.

目前，数据处理工作负载的多样性正在迅速增长。 同样，持久存储技术的新进步正在出现。 因此，拥有用于基准测试和适当配置存储系统的新技术非常重要，以获得最佳的性能和可靠性。该项目建议派生新的输入/输出 (I/O) 模型，以便在基于闪存的固态硬盘 (SSD) 等存储系统上运行具有不同工作负载的多个应用程序时准确捕获 I/O 行为。 此外，该项目开发了新方法来确定不同类型持久存储设备最合适的内部算法，并根据 I/O 活动动态调整相关算法参数。该项目通过解决大型存储设备提出的挑战，为存储系统做出了实证贡献。 - 规模数据密集型应用程序。具体来说，它推进了（1）如何在新兴存储系统上运行多个工作负载时分析各种系统组件的影响； （2）如何设计交互框架，允许用户修改现代存储设备的内部算法和参数； （3）如何让新手能够根据自己的工作负载和数据处理需求来配置存储系统； (4)如何导出I/O模型来预测未来的I/O工作负载模式，并相应地提前配置存储系统以获得更好的性能。该项目将带来更好的高性能和可靠性的存储系统设计。该项目的成果将对许多依赖于处理大量数据的领域带来重大影响。该项目将通过计算机科学和工程项目与本科生和研究生分享研究结果，并为女学生、代表性不足的少数族裔和第一代大学生提供就业机会。该项目将向行业传播拟议的技术，并通过新的行业合作促进技术转让。开发的基础设施将通过基于网络的门户向研究界提供。根据该项目开发的所有可公开披露的 NSF 资助的工作产品将在项目网站 (https://damrl.cis.fiu.edu/research /) 项目结束后至少在佛罗里达国际大学 (FIU) 工作五年。作为该项目的一部分生成和收集的数据将存入东北大学 (NEU) 的数字存储服务 (DRS) (https://repository.library.northeastern.edu/)，并在项目结束后保存至少 5 年。该项目。开发的软件代码和工具将以学术文章的形式发表，并通过 NEU 的 DRS 和 FIU 的项目网站在线提供。该奖项反映了 NSF 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进行评估，被认为值得支持标准。

项目成果

Do Temperature and Humidity Exposures Hurt or Benefit Your SSDs?

温度和湿度暴露会对 SSD 造成伤害还是有益？

• DOI: 10.23919/date54114.2022.9774582
• 发表时间: 2022-01
• 期刊: Automation and Test in Europe Conference. The European Event for Electronic System Design and Test (DATE’22
• 作者: Maruf, Adnan;Brahmakshatriya, Sashri;Li, Baolin;Tiwari, Devesh;Quan, Gang;Bhimani, Janki
• 通讯作者: Bhimani, Janki

KV-SSD: What Is It Good For?

KV-SSD：它有什么好处？

• DOI: 10.1109/dac18074.2021.9586111
• 发表时间: 2021-12
• 期刊: 2021 58th ACM/IEEE Design Automation Conference (DAC
• 作者: Saha, Manoj P.;Maruf, Adnan;Kim, Bryan S.;Bhimani, Janki
• 通讯作者: Bhimani, Janki

SNIS: Storage-Network Iterative Simulation for Disaggregated Storage Systems

SNIS：分解存储系统的存储网络迭代仿真

• DOI: 10.1109/ipccc51483.2021.9679397
• 发表时间: 2021-10
• 期刊: and Communications Conference (IPCCC
• 作者: Jia, Danlin;Li, Tengpeng;Zhang, Xiaoqian;Wang, Li;Bayati, Mahsa;Lee, Ron;Sheng, Bo;Mi, Ningfang
• 通讯作者: Mi, Ningfang

Fine-grained control of concurrency within KV-SSDs

KV-SSD 内并发的细粒度控制

• DOI: 10.1145/3456727.3463777
• 发表时间: 2021-06-14
• 期刊: Proceedings of the 14th ACM International Conference on Systems and Storage
• 作者: J. Bhimani;Jingpei Yang;N. Mi;Changho Choi;M. Saha;Adnan Maruf
• 通讯作者: Adnan Maruf

Performance and Consistency Analysis for Distributed Deep Learning Applications

分布式深度学习应用程序的性能和一致性分析

• 发表时间: 2020-01
• 期刊: 2020 International Performance Computing and Communications Conference (IPCCC'20
• 作者: Jia, Danlin;Saha, Manoj Pravakar;Bhimani, Janki;Mi, Ningfang
• 通讯作者: Mi, Ningfang