摘要:
提出一种基于分类策略的聚簇页级闪存转换层算法——CPFTL.1)CPFTL将地址映射缓存分为热映射表缓存、冷映射表缓存和连续映射表缓存，分别用来缓存访问频繁的请求的映射项、访问不频繁的请求的映射项和高空间本地性的连续请求的映射项，有效提升各类请求的处理能力;2)为利用连续请求的空间本地性，CPFTL的连续映射表缓存预取多个连续的映射项，提高它对连续请求的响应性能;3)为减少页级映射算法的转换页读写开销，CPFTL的冷映射表缓存采用聚簇策略，即将属于同一转换页中的映射项进行聚簇，按簇进行LRU管理，当冷映射表缓存满时，根据簇的映射项个数和LRU选取合适的簇剔除到闪存.实验结果显示，相比经典的页级DFTL算法和最新的SDFTL算法，CPFTL的缓存命中率、平均响应时间、地址转换页操作次数和闪存块擦除次数都有显著提升.
Abstract:
This paper proposes a novel clustered page-level flash translation layer (CPFTL) algorithm which is based on classification strategy. Firstly, CPFTL divides RAM into hot cached mapping table (H-CMT), cold cached mapping table (C-CMT) and sequential cached mapping table (S-CMT), which are responsible for buffering map entries of requests with high temporal locality, low temporal locality and high spatial locality, respectively. Secondly, in order to benefit from the spatial locality of sequential requests, CPFTL prefetches multiple sequential map entries into S-CMT, and thus it can improve the response time of sequential requests. Finally, in order to reduce the read and write overhead of translation pages, CPFTL clusters the map entries which belong to the same translation page in C-CMT together, and manage these clusters by LRU (least recently used)strategy. When C-CMT is full, according to the map entry number and LRU of clusters, CPFTL chooses an appropriate cluster to evict into Flash. CPFTL has been extensively evaluated under various realistic workloads. Compared with the state-of-art FTL schemes such as classic DFTL and the latest SDFTL, our benchmark results show that CPFTL can improve cache hit ratio, operation counts of translation pages, response time and erase counts.
下载:
