A Clustered Page-Level Flash Translation Layer Algorithm Based on Classification Strategy

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.