基于德尔塔压缩的存储系统的快速德尔塔编码设计

IF 2.1 3区 计算机科学 Q3 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE
Haoliang Tan, Wen Xia, Xiangyu Zou, Cai Deng, Qing Liao, Zhaoquan Gu
{"title":"基于德尔塔压缩的存储系统的快速德尔塔编码设计","authors":"Haoliang Tan, Wen Xia, Xiangyu Zou, Cai Deng, Qing Liao, Zhaoquan Gu","doi":"10.1145/3664817","DOIUrl":null,"url":null,"abstract":"<p>Delta encoding is a data reduction technique capable of calculating the differences (i.e., delta) among very similar files and chunks. It is widely used for various applications, such as synchronization replication, backup/archival storage, cache compression, etc. However, delta encoding is computationally costly due to its time-consuming word-matching operations for delta calculation. Existing delta encoding approaches either run at a slow encoding speed, such as Xdelta and Zdelta, or at a low compression ratio, such as Ddelta and Edelta. In this paper, we propose Gdelta, a fast delta encoding approach with a high compression ratio. The key idea behind Gdelta is the combined use of five techniques: (1) employing an improved Gear-based rolling hash to replace Adler32 hash for fast scanning overlapping words of similar chunks, (2) adopting a quick array-based indexing for word-matching, (3) applying a sampling indexing scheme to reduce the cost of traditional building full indexes for base chunks’ words, (4) skipping unmatched words to accelerate delta encoding through non-redundant areas, and (5) last but not least, after word-matching, further batch compressing the remainder to improve the compression ratio. Our evaluation results driven by seven real-world datasets suggest that Gdelta achieves encoding/decoding speedups of 3.5X ∼ 25X over the classic Xdelta and Zdelta approaches while increasing the compression ratio by about 10% ∼ 240%.</p>","PeriodicalId":49113,"journal":{"name":"ACM Transactions on Storage","volume":null,"pages":null},"PeriodicalIF":2.1000,"publicationDate":"2024-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"The Design of Fast Delta Encoding for Delta Compression Based Storage Systems\",\"authors\":\"Haoliang Tan, Wen Xia, Xiangyu Zou, Cai Deng, Qing Liao, Zhaoquan Gu\",\"doi\":\"10.1145/3664817\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>Delta encoding is a data reduction technique capable of calculating the differences (i.e., delta) among very similar files and chunks. It is widely used for various applications, such as synchronization replication, backup/archival storage, cache compression, etc. However, delta encoding is computationally costly due to its time-consuming word-matching operations for delta calculation. Existing delta encoding approaches either run at a slow encoding speed, such as Xdelta and Zdelta, or at a low compression ratio, such as Ddelta and Edelta. In this paper, we propose Gdelta, a fast delta encoding approach with a high compression ratio. The key idea behind Gdelta is the combined use of five techniques: (1) employing an improved Gear-based rolling hash to replace Adler32 hash for fast scanning overlapping words of similar chunks, (2) adopting a quick array-based indexing for word-matching, (3) applying a sampling indexing scheme to reduce the cost of traditional building full indexes for base chunks’ words, (4) skipping unmatched words to accelerate delta encoding through non-redundant areas, and (5) last but not least, after word-matching, further batch compressing the remainder to improve the compression ratio. Our evaluation results driven by seven real-world datasets suggest that Gdelta achieves encoding/decoding speedups of 3.5X ∼ 25X over the classic Xdelta and Zdelta approaches while increasing the compression ratio by about 10% ∼ 240%.</p>\",\"PeriodicalId\":49113,\"journal\":{\"name\":\"ACM Transactions on Storage\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":2.1000,\"publicationDate\":\"2024-05-14\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"ACM Transactions on Storage\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://doi.org/10.1145/3664817\",\"RegionNum\":3,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"COMPUTER SCIENCE, HARDWARE & ARCHITECTURE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"ACM Transactions on Storage","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1145/3664817","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"COMPUTER SCIENCE, HARDWARE & ARCHITECTURE","Score":null,"Total":0}
引用次数: 0

摘要

三角洲编码是一种数据缩减技术,能够计算非常相似的文件和数据块之间的差异(即三角洲)。它被广泛用于各种应用,如同步复制、备份/存档存储、缓存压缩等。然而,由于 delta 编码的计算需要耗费大量时间进行单词匹配操作,因此计算成本很高。现有的 delta 编码方法要么编码速度慢,如 Xdelta 和 Zdelta,要么压缩率低,如 Ddelta 和 Edelta。在本文中,我们提出了一种具有高压缩比的快速三角编码方法 Gdelta。Gdelta 背后的关键理念是综合利用五种技术:(1) 采用改进的基于 Gear 的滚动哈希取代 Adler32 哈希,以快速扫描相似数据块的重叠词;(2) 采用基于数组的快速索引进行词匹配;(3) 采用抽样索引方案,以降低为基础数据块的词建立完整索引的传统成本;(4) 跳过不匹配的词,通过非冗余区域加速 delta 编码;(5) 最后但并非最不重要的是,在词匹配后,进一步批量压缩剩余部分,以提高压缩率。我们通过七个真实世界数据集得出的评估结果表明,与经典的 Xdelta 和 Zdelta 方法相比,Gdelta 的编码/解码速度提高了 3.5 倍 ∼ 25 倍,同时压缩率提高了约 10% ∼ 240%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
The Design of Fast Delta Encoding for Delta Compression Based Storage Systems

Delta encoding is a data reduction technique capable of calculating the differences (i.e., delta) among very similar files and chunks. It is widely used for various applications, such as synchronization replication, backup/archival storage, cache compression, etc. However, delta encoding is computationally costly due to its time-consuming word-matching operations for delta calculation. Existing delta encoding approaches either run at a slow encoding speed, such as Xdelta and Zdelta, or at a low compression ratio, such as Ddelta and Edelta. In this paper, we propose Gdelta, a fast delta encoding approach with a high compression ratio. The key idea behind Gdelta is the combined use of five techniques: (1) employing an improved Gear-based rolling hash to replace Adler32 hash for fast scanning overlapping words of similar chunks, (2) adopting a quick array-based indexing for word-matching, (3) applying a sampling indexing scheme to reduce the cost of traditional building full indexes for base chunks’ words, (4) skipping unmatched words to accelerate delta encoding through non-redundant areas, and (5) last but not least, after word-matching, further batch compressing the remainder to improve the compression ratio. Our evaluation results driven by seven real-world datasets suggest that Gdelta achieves encoding/decoding speedups of 3.5X ∼ 25X over the classic Xdelta and Zdelta approaches while increasing the compression ratio by about 10% ∼ 240%.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
ACM Transactions on Storage
ACM Transactions on Storage COMPUTER SCIENCE, HARDWARE & ARCHITECTURE-COMPUTER SCIENCE, SOFTWARE ENGINEERING
CiteScore
4.20
自引率
5.90%
发文量
33
审稿时长
>12 weeks
期刊介绍: The ACM Transactions on Storage (TOS) is a new journal with an intent to publish original archival papers in the area of storage and closely related disciplines. Articles that appear in TOS will tend either to present new techniques and concepts or to report novel experiences and experiments with practical systems. Storage is a broad and multidisciplinary area that comprises of network protocols, resource management, data backup, replication, recovery, devices, security, and theory of data coding, densities, and low-power. Potential synergies among these fields are expected to open up new research directions.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信