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CDAC: Content-Driven Deduplication-Aware Storage Cache

  • Yujuan Tan
  • , Wen Xia
  • , Jing Xie
  • , Congcong Xu
  • , Zhichao Yan
  • , Hong Jiang
  • , Yajun Zhao
  • , Min Fu
  • , Xianzhang Chen
  • , Duo Liu
  • Chongqing University
  • HPE
  • University of Texas at Arlington
  • Sangfor

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Data deduplication, as a proven technology for effective data reduction in backup and archive storage systems, also demonstrates the promise in increasing the logical space capacity of storage caches by removing redundant data. However, our in-depth evaluation of the existing deduplication-aware caching algorithms reveals that they do improve the hit ratios compared to the caching algorithms without deduplication, especially when the cache block size is set to 4KB. But when the block size is larger than 4KB, a clear trend for modern storage systems, their hit ratios are significantly reduced. A slight increase in hit ratios due to deduplicationmay not be able to improve the overall storage performance because of the high overhead created by deduplication. To address this problem, in this paper we propose CDAC, a Content-driven Deduplication-Aware Cache, which focuses on exploiting the blocks' content redundancy and their intensity of content sharing among source addresses in cache management strategies. We have implemented CDAC based on LRU and ARC algorithms, called CDAC-LRU and CDAC-ARC respectively. Our extensive experimental results show that CDACLRU and CDAC-ARC outperform the state-of-the-art deduplication-aware caching algorithms, D-LRU and DARC, by up to 19.49X in read cache hit ratio, with an average of 1.95X under real-world traces when the cache size ranges from 20% to 80% of the working set size and the block size ranges from 4KB to 64 KB.

Original languageEnglish
Title of host publicationProceedings - 2019 35th Symposium on Mass Storage Systems and Technologies, MSST 2019
PublisherIEEE Computer Society
Pages282-291
Number of pages10
ISBN (Electronic)9781728139203
DOIs
StatePublished - May 2019
Event35th Symposium on Mass Storage Systems and Technologies, MSST 2019 - Santa Clara, United States
Duration: 20 May 201924 May 2019

Publication series

NameIEEE Symposium on Mass Storage Systems and Technologies
Volume2019-May
ISSN (Print)2160-1968

Conference

Conference35th Symposium on Mass Storage Systems and Technologies, MSST 2019
Country/TerritoryUnited States
CitySanta Clara
Period20/05/1924/05/19

Keywords

  • Content
  • Data deduplication
  • SSD Cache

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