Towards Optimizing Deduplication on Persistent Memory - Network and Parallel Computing Access content directly
Conference Papers Year : 2021

Towards Optimizing Deduplication on Persistent Memory

Yichen Li
  • Function : Author
  • PersonId : 1161059
Kewen He
  • Function : Author
  • PersonId : 1161060
Gang Wang
  • Function : Author
  • PersonId : 1161061
Xiaoguang Liu
  • Function : Author
  • PersonId : 1161062

Abstract

Data deduplication is an effective method to reduce data storage requirements. In data deduplication process, fingerprint identification may cause frequent on-disk fingerprint lookups which hurt performance seriously. Some locality-aware approaches were proposed to tackle this issue. Recently, the Persistent Memory (PM) brings low latency and high bandwidth, and has become a hotspot in data storage. Deduplication systems with fingerprints stored on PM will provide extremely fast on-disk fingerprint lookup, and therefore traditional locality-aware approaches designed for slow devices are likely no longer valid.In this paper, we model the traditional locality-aware approaches and analyze their performance on PM. Inspired by the analysis, we propose an optimized PM-based fingerprint identification scheme in which the fingerprint cache is replaced with a simple, low-cost read buffer, and the order of the Bloom filter and the read buffer is swapped. The experimental results on real PM devices show that, compared with the traditional locality-aware approaches, the proposed scheme improves the fingerprint identification throughput by 1.2–2.3 times.
Fichier principal
Vignette du fichier
511910_1_En_39_Chapter.pdf (7.4 Mo) Télécharger le fichier
Origin : Files produced by the author(s)

Dates and versions

hal-03768757 , version 1 (04-09-2022)

Licence

Attribution

Identifiers

Cite

Yichen Li, Kewen He, Gang Wang, Xiaoguang Liu. Towards Optimizing Deduplication on Persistent Memory. 17th IFIP International Conference on Network and Parallel Computing (NPC), Sep 2020, Zhengzhou, China. pp.465-477, ⟨10.1007/978-3-030-79478-1_39⟩. ⟨hal-03768757⟩
10 View
1 Download

Altmetric

Share

Gmail Facebook X LinkedIn More