Leverage Similarity and Locality to Enhance Fingerprint Prefetching of Data Deduplication

Yongtao Zhou, Yuhui Deng, Junjie XieDepartment of Computer Science, Jinan University, Guangzhou, 510632,

P. R.China

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Agenda• Introduction

• Related work

• Motivation

• System overview

• Evaluation

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Introduction• IDC: 95% redundant data in the backup systems; 75% redundant data across the

digital world Consumes IT resources and expensive network bandwidth

• Data deduplication: eliminate redundant data by storing only one data copy

Files

MD5

Data blocks

Chunk algorithm

Fingerprint

Ċ Ċ

Ċ Ċ

B

Ċ Ċ Ċ Ċ Ċ

Ċ Ċ

GA

Ċ Ċ Ċ

E C

Hash Table

B+ Tree

Fingerprints to large too store all fingerprints in memory

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Introduction• Querying fingerprints incurs disk bottleneck The size of fingerprints are too large too be cached in memory

Cache hit ration very low (lack temporal locality)

The IOPS of disk drivers is limited

800TB unique data

MD5 signature Avg.8KB Chunk

A large portion of fingerprints have to be stored on disk drives

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Locality based strategies: DDFS• Locality: segments tend to reappear in the same or very similar sequences with other

segments. This is because most data from previous backup has a slight modifications.

A B C AA B CD E FH I J

A E I A

RAM

...Bloom Filter

...Index

?

Poor deduplication performance when there is little or no locality in datasets

Prefetching!!!!

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Similarity based method: Extreme Binning

Files

C1 C2 Cn

RAM

Similarity Index

DISK

Bin(C1)

Bin(C2)

Bin(Cn)Fail to identify and thus remove significant amounts of redundant data when there is a lack of similarity among files

It uses a two-leve lindex structure made up of similarity characteristic value and the granularity of bin. Extreme Bining stores the similarity characteristics value in RAM. Extreme Bining only identifies the redundant data in the same bin, even though neighbouring binsmay have identical data blocks. This results in some redundant data blocks so as to degrade the deduplication ratio. the deduplication ration of Extreme Bining heavily relies on the similarity degree of data streams.

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SSD based approach• Fingerprint lookup disk bottleneck The IOPS of disk drive is limited

• Some studies alleviate disk bottleneck by using SSD Dedupv1, ChunkStash

• SDD is still very expensive in contrast to disk drives.

• The performance of random and small writes becomes a new bottleneck of SSD

HDD VS SSD

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Our approach• A fingerprint prefetching approach by using the file similarity to enhance the

deduplication performance

• The locality of fingerprints are maintained by arranging the fingerprints in terms of the sequence of the backup data stream

• The overhead of different similarity identification algorithms are investigated, and the impacts of those algorithms on data deduplication are evaluated in contrast to previous studies Extreme Binning, Silo, FPP

• This approach does not impact the deduplication ration

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System architecture

Implementation in LessFS Implementation in Tokyo Cabinet

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Storage structure for fingerprints

Loss the locality of fingerprints

The locality of fingerprints are maintained by arranging the fingerprints in terms of the sequence of the backup data stream

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The process of fingerprints prefetching

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Evaluation• Implement a real prototype based on LessFS and Tokyo Cabinet

• Three similarity identification algorithms FPP, PAS and Simhash are implemented in the Similar File Identification Module

• Ubuntu operation system(Kernel version is 3.5.0-17) ,1GB memory, 2:4GHz Intel(R) Xeon(R) CPU

• We take four full backups to evaluate the system like what DDFS does.

• Four data sets backup1, backup2, backup3 and backup4 to perform the evaluation

10GB, 15GB, 20GB and 25GB, and the numbers of files are 3073, 4694, 6539 and 9910,

respectively.

We choose fixed-size chunk algorithm. The chunk size is 4KB, 8KB, 16KB, 32KB, 64KB and 128KB

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FPP and PAS

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Simhash• Simhash is a member of the local sensitive hash

• Simhash has the property that the fingerprints of similar files differ in a small number of bit positions

• Actual runs at Google web search engine

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Data sets The file size distribution matches the previous studies.

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Deduplication ratio• We measure the size of unique data blocks by using three different similarity

identification algorithms including FPP, PAS and Simhash with four full backups

• When the chunk size is 4KB, the unique data blocks are 14GB, and the data deduplication ratios are 3.93 across the three cases.

• The performance is the same as that of the baseline system LessFS.

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Time overhead of fingerprint lookup• the time of similarity detection

• : the time of fingerprint prefetch

• : the time of fingerprint lookup

• The overall overhead of fingerprint lookup

• For Base has

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Time overhead of fingerprint lookup

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CPU utilization

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Memory utilization

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Conclusion• Proposes a fingerprint prefetching approach by preserving the locality of fingerprint in

the form of backup data stream as well as taking advantage of file similarity

• The proposed method can effectively alleviate the disk bottleneck with acceptable overhead of CPU, memory, and storage when performing fingerprint lookup, thus improving the throughput of data deduplication

• Does not impact the data deduplication ratio

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Analysis & Simulation of Computer and Telecommunication Systems, 2009. MASCOTS’09. IEEE International Symposium on. IEEE, 2009, pp. 1–9.• W. Xia, H. Jiang, D. Feng, and Y. Hua, “Silo: a similarity-locality based near-exact deduplication scheme with low ram overhead and high throughput,” in

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Thank you!

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