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Communication Dans Un Congrès Année : 2021

Towards Optimizing Deduplication on Persistent Memory

Yichen Li
  • Fonction : Auteur
  • PersonId : 1161059
Kewen He
  • Fonction : Auteur
  • PersonId : 1161060
Gang Wang
  • Fonction : Auteur
  • PersonId : 1161061
Xiaoguang Liu
  • Fonction : Auteur
  • PersonId : 1161062

Résumé

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on-disk fingerprint lookup, and therefore traditional locality-aware approaches designed for slow devices are likely no longer valid.In this paper, we model the traditional locality-aware approaches and analyze their performance on PM.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.
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hal-03768757 , version 1 (04-09-2022)

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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⟩
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