XPS: FULL: Collaborative Research: Maximizing the Performance Potential and Reliability of Flash-based Solid State Devices for Future Storage Systems

XPS：完整：协作研究：最大限度地提高未来存储系统基于闪存的固态设备的性能潜力和可靠性

• 批准号: 1629291
• 负责人: Feng Chen
• 金额: $ 29万
• 依托单位: Louisiana State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-07-01 至 2020-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1629291&HistoricalAwards=false
• 关键词: XPS; FULL; Collaborative; Research; Maximizing

Solid-state data storage built upon NAND flash memory is fundamentally changing the memory and storage hierarchy for virtually the entire information technology infrastructure. Nevertheless, there have been several fundamental and challenging issues to be addressed before the industry can explore the flash memory to its full potential. First, as flash memory technology scales down, its reliability degradation approaches to an alarming level, leading to serious concerns and skepticism of storage system architects and users in many applications. Second, system and application development of solid-state storage has been independently conducted, resulting in isolation, duplicated operations, and an inefficient management among these layers. Due to the technology scaling and information loss in existing simple interface with storage devices, flash memory has not been efficiently and reliably utilized in practice, and the situation will become worse with the technology scaling. The PIs of this project will apply a holistic system design methodology to cohesively address the challenges preventing wider adoption of flash memory. By innovating well-orchestrated cross-layer information sharing and utilization, this design methodology enables seamless utilization of system-level workload and physical-level device characteristics across the entire software/hardware stack without complicating overall system design. An integrated software and hardware prototyping infrastructure will be developed to demonstrate the potential using major and widely used software systems, such as Hadoop, virtual machines, and database. This project will achieve a high broader impact by transforming basic research results into storage systems, and by training both undergraduate and graduate students with research activities, and by timely integrating new research results to classrooms.Specifically, this project will carry out several closely related tasks: (1) It will develop techniques that can learn and predict the varying characteristics and their correlations of individual flash memory devices. This will provide run-time information that makes it possible to optimize the use of flash memory for alleviating the reliability crisis and adapting to varying system-level workload characteristics. (2) It will develop techniques that enable critical information exchange across the storage hierarchy in order to facilitate cross-layer information sharing. (3) It will further develop a set of techniques across the design hierarchy that can effectively utilize these runtime collections and predictions to improve the overall system reliability and performance.

基于 NAND 闪存的固态数据存储正在从根本上改变几乎整个信息技术基础设施的内存和存储层次结构。尽管如此，在业界充分探索闪存的潜力之前，还有几个基本且具有挑战性的问题需要解决。首先，随着闪存技术规模的缩小，其可靠性下降达到了惊人的水平，导致许多应用中的存储系统架构师和用户的严重担忧和怀疑。其次，固态存储的系统和应用开发都是各自独立进行，导致各层之间孤立、重复操作、管理效率低下。由于现有的与存储设备的简单接口的技术扩展和信息丢失，闪存在实践中并未得到有效、可靠的利用，并且随着技术扩展，情况将变得更糟。该项目的 PI 将应用整体系统设计方法来共同应对阻碍闪存更广泛采用的挑战。通过创新精心策划的跨层信息共享和利用，这种设计方法可以在整个软件/硬件堆栈中无缝利用系统级工作负载和物理级设备特性，而不会使整体系统设计复杂化。将开发集成的软件和硬件原型基础设施，以展示使用主要且广泛使用的软件系统（例如 Hadoop、虚拟机和数据库）的潜力。该项目将通过将基础研究成果转化为存储系统，通过研究活动对本科生和研究生进行培训，并将新的研究成果及时融入课堂，从而产生更广泛的影响。具体来说，该项目将开展几项密切相关的任务：（1）它将开发能够学习和预测各个闪存器件的变化特征及其相关性的技术。这将提供运行时信息，从而可以优化闪存的使用，从而缓解可靠性危机并适应不同的系统级工作负载特征。 (2)将开发能够跨存储层次交换关键信息的技术，以促进跨层信息共享。 (3) 它将进一步开发一套跨设计层次的技术，可以有效地利用这些运行时收集和预测来提高整体系统的可靠性和性能。

项目成果

SlimCache: An Efficient Data Compression Scheme for Flash-based Key-value Caching

SlimCache：一种基于闪存的键值缓存的高效数据压缩方案

• DOI: 10.1145/3383124
• 发表时间: 2020
• 期刊: ACM Transactions on Storage
• 影响因子: 1.7
• 作者: Jia, Yichen;Shao, Zili;Chen, Feng
• 通讯作者: Chen, Feng

Kill Two Birds with One Stone: Auto-tuning RocksDB for High Bandwidth and Low Latency

• DOI: 10.1109/icdcs47774.2020.00113
• 发表时间: 2020-11
• 期刊: 2020 IEEE 40th International Conference on Distributed Computing Systems (ICDCS)
• 作者: Yichen Jia;Feng Chen

From Flash to 3D XPoint: Performance Bottlenecks and Potentials in RocksDB with Storage Evolution

从闪存到 3D XPoint：RocksDB 存储演进的性能瓶颈和潜力

• DOI: 10.1109/ispass48437.2020.00034
• 发表时间: 2020
• 期刊: Proceedings of 2020 IEEE International Symposium on Performance Analysis of Systems and Software (ISPASS
• 作者: Jia, Yichen;Chen, Feng
• 通讯作者: Chen, Feng