面向云存储的数据流行度去重方案

何欣枫; 杨琴琴

doi:10.19665/j.issn1001-2400.20230205

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面向云存储的数据流行度去重方案

网络空间安全 | 更新时间：2024-04-03

- 面向云存储的数据流行度去重方案
- Deduplication scheme with data popularity for cloud storage
- 西安电子科技大学学报 2024年51卷第1期页码：187-200
- 作者机构：
  
  1. 河北大学网络空间安全与计算机学院,河北保定 071002
  2. 河北省高可信信息系统重点实验室,河北保定 071002
- 作者简介：
  
  [ "何欣枫(1976—),男,副教授,E-mail:[email protected]" ]
  杨琴琴(1995—),女,河北大学硕士研究生,E-mail:[email protected]
- 基金信息：
  
  河北省自然科学基金(F2021201049)
- DOI：10.19665/j.issn1001-2400.20230205
  中图分类号： TP309.2
- 收稿日期：2022-10-25，
  
  网络出版日期：2023-08-30，
  
  纸质出版日期：2024-01-20
- 稿件说明：
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何欣枫, 杨琴琴. 面向云存储的数据流行度去重方案[J]. 西安电子科技大学学报, 2024,51(1):187-200.

Xinfeng HE, Qinqin YANG. Deduplication scheme with data popularity for cloud storage[J]. Journal of xidian university, 2024, 51(1): 187-200.
何欣枫, 杨琴琴. 面向云存储的数据流行度去重方案[J]. 西安电子科技大学学报, 2024,51(1):187-200. DOI： 10.19665/j.issn1001-2400.20230205.

Xinfeng HE, Qinqin YANG. Deduplication scheme with data popularity for cloud storage[J]. Journal of xidian university, 2024, 51(1): 187-200. DOI： 10.19665/j.issn1001-2400.20230205.

摘要

随着云计算的发展

企业和个人倾向于把数据外包给云存储服务器来缓解本地存储压力

导致云端存储压力成为一个日益突出的问题。为了提高云存储效率

降低通信成本

数据去重技术得到了广泛应用。现有的数据去重技术主要包括基于哈希表的相同数据去重和基于布隆过滤器的相似数据去重

但都很少考虑数据流行度的影响。实际应用中

用户外包给云服务器的数据分布是不均匀的

根据访问频率可以划分为流行数据和非流行数据。流行数据访问频繁

在云服务器中会存在大量的副本和相似数据

需要执行高精度的数据去重;而非流行数据访问频率低

云存储服务器中的副本数量和相似数据较少

低精度的去重即可满足要求。针对上述问题

将数据流行度和布隆过滤器相结合

提出一种基于数据流行度的动态布隆过滤器;同时

提出一种基于数据流行度的动态布隆过滤器的数据去重方案

可以根据数据流行度动态调整去重精度。仿真结果表明

该方案在时间消耗、空间消耗和误判率之间取得了良好的平衡。

Abstract

With the development of cloud computing

more enterprises and individuals tend to outsource their data to cloud storage providers to relieve the local storage pressure

and the cloud storage pressure is becoming an increasingly prominent issue.To improve the storage efficiency and reduce the communication cost

data deduplication technology has been widely used.There are identical data deduplication based on the hash table and similar data deduplication based on the bloom filter

but both of them rarely consider the impact of data popularity.In fact

the data outsourced to the cloud storage can be divided into popular and unpopular data according to their popularity.Popular data refer to the data which are frequently accessed

and there are numerous duplicate copies and similar data in the cloud

so high-accuracy deduplication is required.Unpopular data

which are rarely accessed

have fewer duplicate copies and similar data in the cloud

and low-accuracy deduplication can meet the demand.In order to address this problem

a novel bloom filter variant named PDBF(popularity dynamic bloom filter) is proposed

which incorporates data popularity into the bloom filter.Moreover

a PDBF-based deduplication scheme is constructed to perform different degrees of deduplication depending on how popular a datum is.Experiments demonstrate that the scheme makes an excellent tradeoff among the computational time

the memory consumption

and the deduplication efficiency.

关键词

Keywords

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