F/M-CIP: Implementing flash memory cache using conservative insertion and promotion
Date of Original Version
Flash memory SSD has emerged as a promising storage media and fits naturally as a cache between the system RAM and the disk due to its performance/cost characteristics. Managing such an SSD cache is challenging and traditional cache replacements do not work well because of SSDs asymmetric read/write performances and wearing issues. This paper presents a new cache replacement algorithm referred to as F/M-CIP that accelerates disk I/O greatly. The idea is dividing the traditional LRU list into 4 parts: candidate-list, SSD-list, RAM-list and eviction-buffer-list. Upon a cache miss, the metadata of the missed block is conservatively inserted into the candidate-list but the data itself is not cached. The block in the candidate-list is then conservatively promoted to the RAM-list upon the k-th miss. At the bottom of the RAM-list, the eviction-buffer accumulates LRU blocks to be written into the SSD cache in batches to exploit the internal parallelism of SSD. The SSD-list is managed using a combination of regency and frequency replacement policies by means of conservative promotion upon hits. To quantitatively evaluate the performance of F/M-CIP, a prototype has been built on Linux kernel at the generic block layer. Experimental results on standard benchmarks and real world traces have shown that F/M-CIP accelerates disk I/O performance up to an order of magnitude compared to the traditional hard disk storage and up to a factor of 3 compared to the traditional SSD cache algorithm in terms of application execution time. Furthermore, F/M-CIP substantially reduces write operations to the SSD implying prolonged durability.
Publication Title, e.g., Journal
Proceedings - 2015 IEEE/ACM 15th International Symposium on Cluster, Cloud, and Grid Computing, CCGrid 2015
Yang, Jing, and Qing Yang. "F/M-CIP: Implementing flash memory cache using conservative insertion and promotion." Proceedings - 2015 IEEE/ACM 15th International Symposium on Cluster, Cloud, and Grid Computing, CCGrid 2015 (2015): 81-90. doi: 10.1109/CCGrid.2015.119.