Page Type-Aware Full-Sequence Program Scheduling via Reinforcement Learning in High Density SSDs

Full-sequence program (FSP) can program multiple bits simultaneously, and thus complete a multiple-page write at one time for naturally enhancing write performance of high density 3-D solid-state drives (SSDs). This article proposes an FSP scheduling approach for the 3-D quad-level cell (QLC) SSDs, to further boost their read responsiveness. Considering each FSP operation in QLC SSDs spans four different types of QLC pages having dissimilar read latency, we introduce matching four pages of application data to the suited QLC pages and flush them together with the one-shot program of FSP. To this end, we employ reinforcement learning to classify the (cached) application data into four categories on the basis of their historical access frequency and the associating request size. Thus, the frequently read data can be mapped to the QLC pages having less access latency, meanwhile the other data can be flushed onto the slow QLC pages. Then, we can group four different categories of data pages and flush them together into a four-page unit of 3-D QLC SSDs with an FSP operation. In addition, a proactive rewrite method is also triggered for grouping the hot read data with the cached data to form an FSP unit. Through a series of emulation tests on several realistic disk traces, we show that the proposed mechanisms yields notable performance improvement on the read responsiveness.