R&D: NV-Dedup: High-Performance Inline De-Dupe for Non-Volatile Memory

IEEE Transactions on Computers has published an article written by Chundong Wang, Singapore University of Technology and Design, Singapore, Qingsong Wei, Jun Yang, Cheng Chen, Yechao Yang, and Mingdi Xue, Data Storage Institute, A*STAR, Singapore.

Abstract: “The byte-addressable non-volatile memory (NVM) is a promising medium for data storage. NVM-oriented file systems have been designed to explore NVM’s performance potential. Meanwhile, applications may write considerable duplicate data. For NVM, a removal of duplicate data can promote space efficiency, improve write endurance, and potentially improve the performance by avoidance of repeatedly writing the same data. However, we have observed severe performance degradations when implementing a state-of-the-art inline deduplication algorithm in an NVM-oriented file system. A quantitative analysis reveals that, with NVM, 1) the conventional way to manage deduplication metadata for block devices, particularly in light of consistency, is inefficient, and, 2) the performance with deduplication becomes more subject to fingerprint calculations. We hence propose a deduplication algorithm called NV-Dedup . NV-Dedup manages deduplication metadata in a fine-grained, CPU and NVM-favored way, and preserves the metadata consistency with a lightweight transactional scheme. It also does workload-adaptive fingerprinting based on an analytical model and a transition scheme among fingerprinting methods to reduce calculation penalties. We have built a prototype of NV-Dedup in the Persistent Memory File System (PMFS). Experiments show that, NV-Dedup not only substantially saves NVM space, but also boosts the performance of PMFS by up to 2.1 ×.“