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

目前，数据处理工作负载的多样性正在迅速增长。同样，持久性存储技术的新进步也在不断涌现。因此，重要的是要采用新的技术进行基准测试和适当配置存储系统，以获得尽可能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，以准确捕获在存储系统(如基于闪存的固态驱动器(SSD))上运行具有不同工作负载的多个应用程序时的I/O行为。此外，该项目还开发了新的方法来为不同类型的持久存储设备确定最合适的内部算法，并根据I/O活动动态调整相关的算法参数。该项目通过应对大规模数据密集型应用带来的挑战，为存储系统做出了经验贡献。具体地说，它提出了(1)如何分析在运行多个工作负载时各种系统组件对新兴存储系统的影响；(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)如何使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)如何推导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。该项目的结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课程与本科生和研究生分享研究结果，并向女学生、未被充分代表的少数族裔和第一代大学生打开就业机会。该项目将把拟议的技术传播到行业中，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网站提供给研究界。在该项目下开发的所有可公开公布的国家科学基金资助的工作产品将在项目网站(佛罗里达国际大学的https://damrl.cis.fiu.edu/research/))保留至少五年，直到项目结束。作为该项目的一部分，产生和收集的数据将存入东北大学的数字存储服务(DRS)(https://repository.library.northeastern.edu/)，并在项目结束后至少保存5年。开发的软件代码和工具将发表在学术文章中，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优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.