›› 2016, Vol. 31 ›› Issue (4): 820-835.doi: 10.1007/s11390-016-1665-z

Special Issue: Computer Architecture and Systems; Artificial Intelligence and Pattern Recognition

• Computer Architecture and Systems • Previous Articles     Next Articles

A Data Deduplication Framework of Disk Images with Adaptive Block Skipping

Bing Zhou, and Jiang-Tao Wen, Fellow, IEEE   

  1. 1 State Key Laboratory on Intelligent Technology and Systems, Tsinghua University, Beijing 100084, China;
    2 Tsinghua National Laboratory for Information Science and Technology, Tsinghua University Beijing 100084, China;
    3 Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China
  • Received:2015-06-30 Revised:2016-01-27 Online:2016-07-05 Published:2016-07-05
  • About author:Bing Zhou Please refer to P. 819.text
  • Supported by:

    This work is supported by the National Science Fund for Distinguished Young Scholars of China under Grant No. 61125102 and the Key Program of National Natural Science Foundation of China under Grant No. 61133008.

We describe an efficient and easily applicable data deduplication framework with heuristic prediction based adaptive block skipping for the real-world dataset such as disk images to save deduplication related overheads and improve deduplication throughput with good deduplication efficiency maintained. Under the framework, deduplication operations are skipped for data chunks determined as likely non-duplicates via heuristic prediction, in conjunction with a hit and matching extension process for duplication identification within skipped blocks and a hysteresis mechanism based hash indexing process to update the hash indices for the re-encountered skipped chunks. For performance evaluation, the proposed framework was integrated and implemented in the existing data domain and sparse indexing deduplication algorithms. The experimental results based on a real-world dataset of 1.0 TB disk images showed that the deduplication related overheads were significantly reduced with adaptive block skipping, leading to a 30% 80% improvement in deduplication throughput when deduplication metadata were stored on the disk for data domain, and 25% 40% RAM space saving with a 15% 20% improvement in deduplication throughput when an in-RAM sparse index was used in sparse indexing. In both cases, the corresponding deduplication ratios reduced were below 5%.

[1] Zhu B, Li K, Patterson H. Avoiding the disk bottleneck in the data domain deduplication file system. In Proc. the 6th USENIX Conference on File and Storage Technologies (FAST), February 2008, pp.269-282.

[2] Lillibridge M, Eshghi K, Bhagwat D, Deolalikar V, Trezise G, Camble P. Sparse Indexing:Large scale, inline deduplication using sampling and locality. In Proc. the 7th FAST, February 2009, pp.111-123.

[3] Srinivasan K, Bisson T, Goodson G, Voruganti K. iDedup:Latency-aware, inline data deduplication for primary storage. In Proc. the 10th FAST, February 2012, pp.299-312.

[4] Wildani A, Miller E, Rodeh O. HANDS:A heuristically arranged non-backup inline deduplication system. In Proc. the 29th IEEE International Conference on Data Engineering (ICDE), April 2013, pp.446-457.

[5] Rabin M O. Fingerprinting by random polynomials. Technical Report, TR-15-81, Center for Research in Computing Technology, Harvard University, 1981.

[6] Black J. Compare-by-hash:A reasoned analysis. In Proc. the USENIX Annual Technical Conference (ATC), May 2006, pp.85-90.

[7] Meister D, Kaiser J, Brinkmann A, Cortes T, Kuhn M, Kunkel J. A study on data deduplication in HPC storage systems. In Proc. the International Conference for High Performance Computing, Networking, Storage and Analysis, November 2012.

[8] Bloom B H. Space/time trade-offs in hash coding with allowable errors. Commun. ACM, 1970, 13(7):422-426.

[9] Jin K, Miller E L. The effectiveness of deduplication on virtual machine disk images. In Proc. the 2nd Annual International Systems and Storage Conference (SYSTOR), May 2009, pp.7:1-7:12.

[10] Muthitacharoen A, Chen B, Maziéres D. A low-bandwidth network file system. In Proc. the 18th ACM Symposium on Operating Systems Principles (SOSP), October 2001, pp.174-187.

[11] RomarÍski B, Heldt L, Kilian W et al. Anchor-driven subchunk deduplication. In Proc. the 4th Annual International Conference on Systems and Storage (SYSTOR), May 2011, pp.16:1-16:13.

[12] Bhagwat D, Eshghi K, Long D D E, Lillibridge M. Extreme Binning:Scalable, parallel deduplication for chunk-based file backup. In Proc. the IEEE International Symposium on Modeling, Analysis & Simulation of Computer and Telecommunication Systems (MASCOTS), September 2009.

[13] Tanenbaum A S. Modern Operating Systems (2nd edition). Prentice Hall PTR, 2001.

[14] Zhou B, Wen J. Hysteresis re-chunking based metadata harnessing deduplication of disk images. In Proc. the 42nd IEEE International Conference on Parallel Processing (ICPP), October 2013, pp.389-398.

[15] Fan L, Cao P, Almeida J, Broder A. Summary cache:A scalable wide-area web cache sharing protocol. IEEE/ACM Transactions on Networking, June 2000, 8(3):281-293.

[16] Guo F, Efstathopoulos P. Building a high-performance deduplication system. In Proc. the USENIX Annual Technical Conference (ATC), June 2011, Article No. 25.

[17] Botelho F C, Shilane P, Garg N, Hsu W. Memory efficient sanitization of a deduplicated storage system. In Proc. the 11th FAST, February 2013, pp.81-94.

[18] Debnath B, Sengupta S, Li J. ChunkStash:Speeding up inline storage deduplication using flash memory. In Proc. the USENIX Annual Technical Conference (ATC), June 2010, Article No. 16.

[19] Meister D, Brinkmann A. dedupv1:Improving deduplication throughput using solid state drives (SSD). In Proc. the 26th IEEE Symposium on Mass Storage Systems and Technologies (MSST), May 2010.

[20] Dal Bianco G, Galante R, Heuser C A. A fast approach for parallel deduplication on multicore processors. In Proc. the ACM Symposium on Applied Computing (SAC), March 2011, pp.1027-1032.

[21] Bobbarjung D R, Jagannathan S, Dubnicki C. Improving duplicate elimination in storage systems. ACM Transactions on Storage, November 2006, 2(4):424-448.

[22] Kruus E, Ungureanu C, Dubnicki C. Bimodal content defined chunking for backup streams. In Proc. the 8th FAST, February 2010, Article No. 18.

[23] Lu G, Jin Y, Du D. Frequency based chunking for data de-duplication. In Proc. IEEE International Symposium on Modeling, Analysis Simulation of Computer and Telecommunication Systems (MASCOTS), August 2010, pp.287-296.

[24] Meister D, Brinkmann A, Süβ T. File recipe compression in data deduplication systems. In Proc. the 11th FAST, February 2013, pp.175-182.

[25] Balachandran S, Constantinescu C. Sequence of hashes compression in data deduplication. In Proc. the Data Compression Conference (DCC), March 2008, p.505.

[26] Harnik D, Margalit O, Naor D, Sotnikov D, Vernik G. Estimation of deduplication ratios in large datasets. In Proc. the 28th IEEE Symposium on Mass Storage Systems and Technologies (MSST), April 2012.

[27] Xie F, Condict M, Shete S. Estimating duplication by content-based sampling. In Proc. the USENIX Conference on Annual Technical Conference (ATC), June 2013, pp.181-186.

[28] Constantinescu C, Lu M. Quick estimation of data compression and deduplication for large storage systems. In Proc. the 1st International Conference on Data Compression, Communications and Processing (CCP), June 2011, pp.98-102.

[29] Fu M, Feng D, Hua Y, He X, Chen Z, Xia W, Zhang Y, Tan Y. Design tradeoffs for data deduplication performance in backup workloads. In Proc. the 13th FAST, February 2015, pp.331-344.

[30] Fu M, Feng D, Hua Y, He X, Chen Z, Xia W, Huang F, Liu Q. Accelerating restore and garbage collection in deduplicationbased backup systems via exploiting historical information. In Proc. the USENIX Annual Technical Conference (ATC), June 2014, pp.181-192.

[31] Tang Y, Yang J. Secure deduplication of general computations. In Proc. the USENIX Annual Technical Conference (ATC), July 2015, pp.319-331.

[32] Zhang W, Yang T, Narayanasamy G, Tang H. Low-cost data deduplication for virtual machine backup in cloud storage. In Proc. the 5th USENIX Workshop on Hot Topics in Storage and File Systems (HotStorage), June 2013, Article No. 12.

[33] Lin X, Lu G, Douglis F, Shilane P, Wallace G. Migratory compression:Coarse-grained data reordering to improve compressibility. In Proc. the 12th FAST, February 2014, pp.257-271.
No related articles found!
Full text



[1] Bing Zhou, Jiang-Tao Wen. Metadata Feedback and Utilization for Data Deduplication Across WAN[J]. , 2016, 31(3): 604 -623 .

ISSN 1000-9000(Print)

CN 11-2296/TP

Editorial Board
Author Guidelines
Journal of Computer Science and Technology
Institute of Computing Technology, Chinese Academy of Sciences
P.O. Box 2704, Beijing 100190 P.R. China
E-mail: jcst@ict.ac.cn
  Copyright ©2015 JCST, All Rights Reserved