SAFE: A Source Deduplication Framework for Efficient Cloud Backup Services

  • Authors:

  • Yujuan Tan;Hong Jiang;Edwin Hsing-Mean Sha;Zhichao Yan;Dan Feng

  • Affiliations:

  • College of Computer Science, Chongqing University, Chongqing, China;Department of Computer Science & Engineering, University of Nebraska-Lincoln, Lincoln, USA;College of Computer Science, Chongqing University, Chongqing, China;Department of Computer Science & Engineering, University of Nebraska-Lincoln, Lincoln, USA;School of Computer Science & Technology, Huazhong University of Science & Technology, Wuhan, China

  • Venue:
  • Journal of Signal Processing Systems
  • Year:
  • 2013

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Abstract

Due to the relatively low bandwidth of WAN that supports cloud backup services and the increasing amount of backed-up data stored at service providers, the deduplication scheme used in the cloud backup environment must remove the redundant data for backup operations to reduce backup times and storage costs and for restore operations to reduce restore times. In this paper, we propose SAFE, a source deduplication framework for efficient cloud backup and restore operations. SAFE consists of three salient features, (1) Hybrid Deduplication, combining the global file-level and local chunk-level deduplication to achieve an optimal tradeoff between the deduplication efficiency and overhead to achieve a short backup time; (2) Semantic-aware Elimination, exploiting file semantics to narrow the search space for the redundant data in hybrid deduplication process to reduce the deduplication overhead; and (3) Unmodified Data Removal, removing the files and data chunks that are kept intact from data transmission for some restore operations. Through extensive experiments driven by real-world datasets, the SAFE framework is shown to maintain a much higher deduplication efficiency/overhead ratio than existing solutions, shortening the backup time by an average of 38.7 %, and reduce the restore time by a ratio of up to 9.7 : 1.