CNS Core: III: Medium: Collaborative Research: Optimizing and Understanding Large Parameter Spaces in Storage Systems

CNS 核心：III：中：协作研究：优化和理解存储系统中的大参数空间

• 批准号: 1900706
• 负责人: Erez Zadok
• 金额: $ 82.31万
• 依托单位: SUNY at Stony Brook
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-10-01 至 2024-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1900706&HistoricalAwards=false
• 关键词: CNS; Core; III; Medium; Collaborative

Computer systems contain software which is becoming increasingly complex. Complex software often includes many configurable parameters or knobs that can be adjusted to adjust performance and energy consumption. As an example, a smartphone's settings include many on/off features and "sliders" one can adjust; but trying all the possible combinations to improve performance and reduce battery consumption is very time consuming. This project aims to optimize computer systems by (1) automatically exploring many parameter combinations and (2) helping humans see visual indications of how these parameters work and better understand complex systems.This project will: (1) develop techniques to optimize storage systems, because they are the slowest part of any computer; (2) combine features from existing optimization and machine learning techniques; (3) improve the search for optimal settings by deciding when to stop and restart searching as well as considering the cost of changing system settings; (4) develop human driven visual techniques to explore extremely large sets of option combinations to better understand them and further direct the optimization process; and (5) evaluate all these techniques on real world storage systems.Computer storage systems are so complex that no human can fully optimize them, particularly when circumstances change. This project will help automate the optimization of storage systems, improving their performance and energy use; advance the state of the art in hybrid optimization and machine learning techniques; develop and release interactive visualization systems that let humans understand, view, and direct a search process to promising directions; train and educate graduate and undergraduate students; and produce results that are applicable to other computer system optimization problems.The project's artifacts such as software, source code, data sets, and results are part of a system called "Spectra". These artifacts will be made public through the project Website: https://www.filesystems.org/spectra. Results will be disseminated in peer-reviewed publications and on arxiv.org. The data will be maintained for at least ten years following the end of the project.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.

计算机系统包含的软件正变得越来越复杂。复杂的软件通常包括许多可配置的参数或旋钮，可以调整这些参数或旋钮来调整性能和能耗。例如，智能手机的设置包括许多开/关功能和可以调整的滑块；但尝试所有可能的组合来提高性能和降低电池消耗非常耗时。该项目旨在通过(1)自动探索许多参数组合和(2)帮助人类看到这些参数如何工作的视觉指示并更好地理解复杂系统来优化计算机系统。该项目将：(1)开发技术来优化存储系统，因为它们是任何计算机中最慢的部分；(2)结合现有优化和机器学习技术的特征；(3)通过决定何时停止和重新开始搜索以及考虑改变系统设置的成本来改进对最佳设置的搜索；(4)开发人类驱动的视觉技术来探索极大的选项组合以更好地理解它们并进一步指导优化过程；以及(5)在现实世界的存储系统上评估所有这些技术。计算机存储系统是如此复杂，没有人能完全优化它们，特别是在环境变化的时候。这个项目将帮助自动化存储系统的优化，提高它们的性能和能源使用；推进混合优化和机器学习技术的最新水平；开发和发布交互式可视化系统，使人类能够理解、查看并将搜索过程引导到有前途的方向；培训和教育研究生和本科生；并产生适用于其他计算机系统优化问题的结果。该项目的人工制品，如软件、源代码、数据集和结果是名为“Spectra”的系统的一部分。这些文物将通过项目网站https://www.filesystems.org/spectra.公布。结果将在同行评议的出版物和arxiv.org上传播。这些数据将在项目结束后保留至少十年。这一奖项反映了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。

项目成果

PC-Expo: A Metrics-Based Interactive Axes Reordering Method for Parallel Coordinate Displays

PC-Expo：一种用于并行坐标显示的基于度量的交互式轴重新排序方法

• DOI: 10.1109/tvcg.2022.3209392
• 发表时间: 2022
• 期刊: IEEE Transactions on Visualization and Computer Graphics
• 影响因子: 5.2
• 作者: Tyagi, Anjul;Estro, Tyler;Kuenning, Geoff;Zadok, Erez;Mueller, Klaus
• 通讯作者: Mueller, Klaus

F3: Serving Files Efficiently in Serverless Computing

F3：在无服务器计算中高效地提供文件服务

• DOI: 10.1145/3579370.3594771
• 发表时间: 2023
• 期刊: The 16th ACM International Systems and Storage Conference (SYSTOR '23
• 作者: Merenstein, Alex;Tarasov, Vasily;Anwar, Ali;Guthridge, Scott;Zadok, Erez
• 通讯作者: Zadok, Erez

NAS-Navigator: Visual Steering for Explainable One-Shot Deep Neural Network Synthesis

NAS-Navigator：用于可解释的一次性深度神经网络合成的视觉引导

• DOI: 10.1109/tvcg.2022.3209361
• 发表时间: 2023
• 期刊: IEEE Transactions on Visualization and Computer Graphics
• 影响因子: 5.2
• 作者: Tyagi, Anjul;Xie, Cong;Mueller, Klaus
• 通讯作者: Mueller, Klaus

Desperately Seeking ... Optimal Multi-Tier Cache Configurations

• 发表时间: 2020
• 作者: Tyler Estro;Pranav Bhandari;Avani Wildani;E. Zadok

CNSBench: A cloud native storage benchmark native storage

CNSBench：云原生存储基准原生存储

• 发表时间: 2021
• 期刊: Proceedings of the 19th USENIX Conference on File and Storage Technologies (FAST '20
• 作者: Alex Merenstein, Vasily Tarasov