Collaborative Research: CNS Core: Medium: Optimizing Storage Caches via Adaptive and Reconfigurable Tiering

协作研究：CNS 核心：中：通过自适应和可重新配置分层优化存储缓存

• 批准号: 2106359
• 负责人: Vaidy Sunderam
• 金额: $ 26.67万
• 依托单位: Emory University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-10-01 至 2025-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2106359&HistoricalAwards=false
• 关键词: Collaborative; Research; CNS; Core; Medium

New types of data storage and memory devices are being developed and released, but they have very different properties, such as speeds, costs, sizes, reliability, and energy. With these new options, there is an opportunity to reduce the cost or environmental impact of storage while improving its reliability and performance. To realize these benefits, this project explores techniques to use new storage devices together. By exploring when and how to move data between “tiers” of new storage, as well as automatically determining what devices to use in data storage tiers, the project dramatically improves storage for both providers and the end users.This project analyzes methods to detect optimal reconfiguration points using machine-learning and time-series techniques via three interrelated thrusts. In the first thrust, accurate, analytical, multi-tier tail latency models are developed using queuing theory. Then, the project builds an efficient platform to simulate configurations and investigates methods to estimate tiered-cache reconfiguration costs. Finally, lightweight, low-overhead, accurate sampling techniques are explored for running systems, to quickly detect significant input/output and cache behavior changes. In response, this project further builds techniques to reconfigure a tiered-cache on running systems with minimal interference. Empirical case studies are applied to Memcached and Kubernetes containers.The storage community benefits from multiple artifacts this project produces: an open-source versatile multi-tier cache simulator, workload and analytical latency models, several case studied systems, a database of metrics from empirically evaluated devices, and publications reporting unexpected or counter-intuitive results. Storage consumers (e.g., companies) can simulate many “what-if” scenarios before actually purchasing any hardware, so as to avoid under- or over-provisioning. This project develops new course modules including short video tutorials. Several students, including women and members of underrepresented groups, are mentored and trained in research techniques.The project's artifacts—software, source code, data sets, multi-tier cache simulator, traces, and results—are all embodied in a system we call "MTCache: Multi-Tier Cache". Results will be disseminated using peer-reviewed publications and arxiv.org. All artifacts will be made public through the project Website: https://www.filesystems.org/mtcache. The project plans to maintain that site 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.

新型的数据存储和内存设备正在开发和发布，但是它们具有截然不同的属性，例如速度，成本，尺寸，可靠性和能源。有了这些新选择，就有机会降低存储的成本或环境影响，同时提高其可靠性和性能。为了实现这些好处，该项目探讨了一起使用新的存储设备的技术。通过探索何时以及如何在新存储的“层”之间移动数据，以及自动确定在数据存储层中使用哪些设备，该项目会大大改善提供商和最终用户的存储。此项目分析方法可检测最佳重新配置点使用机器学习和时间表技术，并通过三个审查的诉讼方法来检测最佳重新配置点。在第一个推力，准确，分析，多层尾延迟模型中是使用排队理论开发的。然后，该项目建立了一个有效的平台，以模拟配置并研究方法以估算层次的重新配置成本。最后，探索了轻巧的，低空的，准确的采样技术，以快速检测重要的输入/输出和缓存行为变化。作为响应，该项目进一步构建了在运行系统上重新配置层次调查的技术，并以最小的干扰。实证案例研究应用于备忘录和Kubernetes容器。存储社区受益于该项目产生的多个工件：开放源代码多功能多层缓存模拟器，工作量和分析延迟模型，几个案例研究系统，研究系统，来自经验评估的设备和出版物的元素数据库，以及报告的意外或反式的结果。存储消费者（例如，公司）可以在实际购买任何硬件之前模拟许多“假设”方案，以免避免使用不足或过度提供。该项目开发了新的课程模块，包括简短的视频教程。几名学生，包括妇女和人数不足的群体的成员，接受了研究技术的指导和培训。该项目的工件 - 软件，源代码，数据集，多层缓存模拟器，痕迹和结果 - 都体现在我们称为“ mtcache：MTCACHE：多层缓存的系统中”。结果将使用同行评审的出版物和arxiv.org传播。所有工件将通过项目网站公开：https：//www.filesystems.org/mtcache。该项目计划在项目结束后至少维持该网站至少十年。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评估标准，被视为通过评估而被视为珍贵的支持。