2016 USENIX Annual Technical Conference (USENIX ATC '16)

We apply an extent-based clustering technique to the problem of identifying “hot” or frequently-written data in an SSD, allowing such data to be segregated for improved cleaning performance. We implement and evaluate this technology in simulation, using a page-mapped FTL with Greedy cleaning and separate hot and cold write frontiers. We compare it with two recently proposed hot data identification algorithms, Multiple Hash Functions and Multiple Bloom Filters, keeping the remainder of the FTL / cleaning algorithm unchanged. In almost all cases write amplification was lower with the extent-based algorithm; although in some cases the improvement was modest, in others it was as much as 20%. These gains are achieved with very small amounts of memory, e.g. roughly 10KB for the implementation tested, an important factor for SSDs where most DRAMis dedicated to address maps and data buffers.