DEFT-Cache: A Cost-Effective and Highly Reliable SSD Cache for RAID Storage
Document Type
Conference Proceeding
Date of Original Version
6-30-2017
Abstract
This paper proposes a new SSD cache architecture, DEFT-cache, Delayed Erasing and Fast Taping, that maximizes I/O performance and reliability of RAID storage. First of all, DEFT-Cache exploits the inherent physical properties of flash memory SSD by making use of old data that have been overwritten but still in existence in SSD to minimize small write penalty of RAID5/6. As data pages being overwritten in SSD, old data pages are invalidated and become candidates for erasure and garbage collections. Our idea is to selectively delay the erasure of the pages and let these otherwise useless old data in SSD contribute to I/O performance for parity computations upon write I/Os. Secondly, DEFT-Cache provides inexpensive redundancy to the SSD cache by having one physical SSD and one virtual SSD as a mirror cache. The virtual SSD is implemented on HDD but using log-structured data layout, i.e. write data are quickly logged to HDD using sequential write. The dual and redundant caches provide a cost-effective and highly reliable write-back SSD cache. We have implemented DEFT-Cache on Linux system. Extensive experiments have been carried out to evaluate the potential benefits of our new techniques. Experimental results on SPC and Microsoft traces have shown that DEFT-Cache improves I/O performance by 26.81% to 56.26% in terms of average user response time. The virtual SSD mirror cache can absorb write I/Os as fast as physical SSD providing the same reliability as two physical SSD caches without noticeable performance loss.
Publication Title, e.g., Journal
Proceedings - 2017 IEEE 31st International Parallel and Distributed Processing Symposium, IPDPS 2017
Citation/Publisher Attribution
Wan, Jiguang, Wei Wu, Ling Zhan, Qing Yang, Xiaoyang Qu, and Changsheng Xie. "DEFT-Cache: A Cost-Effective and Highly Reliable SSD Cache for RAID Storage." Proceedings - 2017 IEEE 31st International Parallel and Distributed Processing Symposium, IPDPS 2017 (2017): 102-111. doi: 10.1109/IPDPS.2017.54.