ADAPT: Efficient workload-sensitive flash management based on adaptation, prediction and aggregation

Abstract

Solid-state drives (SSDs) made of flash memory are widely utilized in enterprise servers nowadays. Internally, the management of flash memory resources is done by an embedded software known as the flash translation layer (FTL). One important function of the FTL is to map logical addresses issued by the operating system into physical flash addresses. The efficiency of this address mapping in the FTL directly impacts the performance of SSDs. In this paper, we propose a hybrid mapping FTL scheme, called Aggregated Data movement Augmenting Predictive Transfers (ADAPT). ADAPT observes access behaviors online to handle both sequential and random write requests efficiently. It also takes advantage of locality revealed in the history of recent accesses to avoid unnecessary data movements in the required merge process. More importantly, by these mechanisms, ADAPT can adapt to various workloads to achieve good performance. Experimental results show that ADAPT is as much as 35.4%, 44.2% and 23.5% faster than a state-of-the-art hybrid mapping scheme, a prevalent page-based mapping scheme, and a latest workload-adaptive mapping scheme, respectively, with a small increase in space requirement.