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.

当前，数据处理工作负载的多样性迅速增长。 同样，持续存储技术的新进步也在出现。 因此，重要的是要有新技术来基准测试和适当配置存储系统，以获得最佳的性能和可靠性。该项目建议在运行具有不同工作负载的多个应用程序（例如基于Flash基于Flash的固态驱动器（SSD））上的多个应用程序时，可以准确捕获新的输入/输出（I/O）模型，以准确捕获I/O行为。 此外，该项目还开发了新的方法来确定不同类型的持续存储设备的最合适的内部算法，并根据I/O活动动态调整相关的算法参数。该项目通过解决由大规模数据密集型应用程序发出的挑战来解决存储系统的经验贡献。具体而言，它进步（1）如何在运行多个工作负载对新兴存储系统的过程中分析各种系统组件的影响； （2）如何设计交互式框架，使用户可以修改现代存储设备的内部算法和参数； （3）如何使新手相对于其工作负载和数据处理要求配置存储系统； （4）如何得出I/O模型来预测未来的I/O工作负载模式，并因此提前配置存储系统以提高性能。该项目将带来更好的存储系统设计，具有高性能和可靠性。该项目的结果将对许多取决于处理大量数据的领域产生重大影响。该项目将通过计算机科学和工程计划与本科生和研究生分享研究结果，并为女学生，代表性不足的少数民族以及第一代大学生提供职业机会。该项目将通过新的工业合作将所提出的技术传播到行业，并促进技术转移。开发的基础架构将通过基于网络的门户提供给研究社区。该项目下开发的公开披露的NSF资助的工作产品将在项目网站（https://damrl.cis.fiu.edu/research/hats https：//damrl.cis.edu.edu/research/）上维护至少在佛罗里达国际大学（FIU）（FIU​​）（FIU​​）的末期。作为该项目的一部分生成和收集的数据将存入数字存储库服务（DRS）（https://repository.library.northeastern.edu/）（neu）（neu），并在项目末尾维持了至少5年。已开发的软件代码和工具将在学术文章中发表，并通过NEU的DRS在线提供，以及FIU的Project网站。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛影响的评估审查标准来通过评估来获得支持的。

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
• 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

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
• 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

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
• 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

Performance and Consistency Analysis for Distributed Deep Learning Applications

• DOI: 10.1109/ipccc50635.2020.9391566
• 发表时间: 2020-11
• 期刊: 2020 IEEE 39th International Performance Computing and Communications Conference (IPCCC)
• 作者: Danlin Jia;M. Saha;J. Bhimani;N. Mi

Fine-grained control of concurrency within KV-SSDs

• DOI: 10.1145/3456727.3463777
• 发表时间: 2021-06
• 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