Recent advances in flash memory technology have reduced the cost-per-bit of flash storage devices such as solid-state drives (SSDs), thereby enabling the development of large-capacity SSDs for enterprise-scale storage. However, two major concerns arise in designing SSDs. First, the size of the address mapping table is increasing in proportion to the capacity of the SSD. The SSD-internal firmware, called flash translation layer (FTL), must maintain the address mapping table in the internal DRAM. Although the previously proposed demand map loading technique uses a small size of cached map table, the technique aggravates poor random performance. Second, there are many redundant writes in storage workloads, which have an adverse effect on the performance and lifetime of the SSD. For example, many transaction-supporting applications use the write-ahead-log (WAL) scheme, which writes the same data twice. To resolve these problems, we propose a novel transaction-supporting SSD, called WAL-SSD, which logs transaction data at the internally-managed WAL area and relocates the data atomically via the FTL-level remap operation at the transaction checkpointing. It can also be used to transform random write requests to sequential requests. We implemented a prototype of WAL-SSD with a real SSD device. Experiments demonstrate the performance improvement by WAL-SSD with three use cases: remap-journaling, atomic multi-block update, and random write logging.

Although much research efforts have been devoted to reducing the size of address mapping table which consumes DRAM space in solid state drives (SSDs), most SSDs still use page-level mapping for high performance in their firmware called flash translation layer (FTL). In this paper, we propose a novel FTL scheme, called StreamFTL. In order to reduce the size of the mapping table in SSDs, StreamFTL maintains a mapping entry for each stream, which consists of several logical pages written at contiguous physical pages. Unlike extent, which is used by previous FTL schemes, the logical pages in a stream do not need to be contiguous. We show that StreamFTL can reduce the size of the mapping table by up to 90% compared to page-level mapping scheme.