CSR: Small:Taming Small Data Writes to Block Storage Devices for Higher I/O Efficiency

CSR：小：驯服小数据写入块存储设备以提高 I/O 效率

• 批准号: 1664373
• 负责人: Song Jiang
• 金额: $ 44.14万
• 依托单位: University of Texas at Arlington
• 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: 2016-09-01 至 2019-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1664373&HistoricalAwards=false
• 关键词: CSR; Small; Taming; Data; Writes

Storage systems are one of the most critical infrastructures in large-scale data centers. Much effort has been directed to allow data to be scalable and efficiently accessed on storage devices. While big data poses big challenges to storage systems, small data presents equally serious access efficiency challenges and begs for innovative research solutions. Almost all storage devices use block interface, which can hardly be replaced. Accessing small data potentially results in wasted device bandwidth and significantly reduced input/output (I/O) efficiency which leads to substantially higher hardware and energy costs and compromised service quality to end users. This research project, based on preliminary results that have shown consistent effectiveness in various application scenarios, will employ a disruptive process using data compression techniques to hide or remove small data writes. Because of demand on immediate data persistency, writes of the small-data continue to be the Achilles' heel of block devices. There are multiple software layers across the I/O stack interacting with the block interface, where small-data writes can inflict a substantial performance penalty. The layers include virtual block devices, I/O schedulers, and file systems. Instead of relying on special hardware support or demanding interface changes, the proposed solution leverages data compression techniques. It allows small data to efficiently pass through the rigid but necessary block interface adopted by almost all storage devices to provide persistency and atomicity without extra block write and flush operations. The proposed strategy would effectively address the issue with small writes to a great extent and profoundly benefit the industry. The expected software artifacts will be built into Linux and file systems such as Ext3, and be open to the community for sharing.

存储系统是大型数据中心中最关键的基础设施之一。 已经进行了很多努力以允许数据在存储设备上可扩展和有效地访问。虽然大数据对存储系统提出了巨大的挑战，但小数据也提出了同样严重的访问效率挑战，并要求创新的研究解决方案。几乎所有的存储设备都使用块接口，这是很难取代的。小型数据可能导致浪费设备带宽和显著降低的输入/输出（I/O）效率，从而导致硬件和能源成本大幅提高，并损害最终用户的服务质量。该研究项目基于在各种应用场景中显示出一致有效性的初步结果，将采用数据压缩技术来隐藏或删除小数据写入的破坏性过程。由于对即时数据持久化的需求，小数据的写入仍然是块设备的阿喀琉斯之踵。I/O堆栈中有多个软件层与数据块接口交互，其中小数据写入可能会造成严重的性能损失。这些层包括虚拟块设备、I/O缓存器和文件系统。建议的解决方案利用了数据压缩技术，而不是依赖于特殊的硬件支持或苛刻的接口更改。它允许小数据有效地通过几乎所有存储设备所采用的严格但必要的块接口，以提供持久性和原子性，而无需额外的块写入和刷新操作。所提出的策略将在很大程度上有效地解决小写入的问题，并使该行业受益匪浅。预期的软件工件将内置于Linux和Ext 3等文件系统中，并向社区开放以供共享。