Low‐overhead inline deduplication for persistent memory

Abstract

AbstractPersistent memory (PM) can play an important role in storing the edge data of IoT devices, but their capacities are restrained by the embedded system, which only provides the limited host space and energy resource. Therefore, a deduplication system is required to improve the PM utilization and energy consumption. According to our observations, the current inline deduplication algorithms incur nonnegligible latency when applied to the PM file systems (PMFSs), mainly because (i) the low tolerance to the extra deduplication I/O path, (ii) the long fingerprint calculation, and (iii) the unnecessary duplication detection when writing content with low duplication ratio. To improve this issue, we propose LO‐Dedup, a novel and low‐overhead deduplication system for PM. LO‐Dedup adopts PM‐friendly design, both in fine‐grained data load/store and fast hash scheme to reduce overhead. Moreover, LO‐Dedup can adaptively sample duplication detection according to the recent contents. We implement a prototype of LO‐Dedup in PMFS, which is a well‐known PMFS. The experimental results show that the write performance only has a slight drop when writing data and saves up to 45% space in PMFS.