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

Wayne State University, Detroit MI

Investigators

Abstract

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.