ARS: Reducing F2FS Fragmentation for Smartphones using Decision Trees

Lihua Yang, F. Wang, Zhipeng Tan, D. Feng, Jiaxing Qian, Shiyun Tu
{"title":"ARS: Reducing F2FS Fragmentation for Smartphones using Decision Trees","authors":"Lihua Yang, F. Wang, Zhipeng Tan, D. Feng, Jiaxing Qian, Shiyun Tu","doi":"10.23919/DATE48585.2020.9116318","DOIUrl":null,"url":null,"abstract":"As we all know, file and free space fragmentation negatively affect file system performance. F2FS is a file system designed for flash memory. However, it suffers from severe fragmentation due to its out-of-place updates and the highly synchronous, multi-threaded writing behaviors of mobile applications. We observe that the running time of fragmented files is 2.36× longer than that of continuous files and that F2FS’s in-place update scheme is incapable of reducing fragmentation. A fragmented file system leads to a poor user experience.(p)(/p)Reserving space to prevent fragmentation is an intuitive approach. However, reserving space for all files wastes space since there are a large number of files. To deal with this dilemma, we propose an adaptive reserved space (ARS) scheme to choose some specific files to update in the reserved space. How to effectively select reserved files is critical to performance. We collect file characteristics associated with fragmentation to construct data sets and use decision trees to accurately pick reserved files. Besides, adjustable reserved space and dynamic reservation strategy are adopted. We implement ARS on a HiKey960 development platform and a commercial smartphone with slight space and file creation time overheads. Experimental results show that ARS reduces file and free space fragmentation dramatically, improves file I/O performance and reduces garbage collection overhead compared to traditional F2FS and F2FS with in-place updates. Furthermore, ARS delivers up to 1.26× transactions per second under SQLite than traditional F2FS and reduces running time of realistic workloads by up to 41.72% than F2FS with in-place updates.","PeriodicalId":289525,"journal":{"name":"2020 Design, Automation & Test in Europe Conference & Exhibition (DATE)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"10","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 Design, Automation & Test in Europe Conference & Exhibition (DATE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.23919/DATE48585.2020.9116318","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 10

Abstract

As we all know, file and free space fragmentation negatively affect file system performance. F2FS is a file system designed for flash memory. However, it suffers from severe fragmentation due to its out-of-place updates and the highly synchronous, multi-threaded writing behaviors of mobile applications. We observe that the running time of fragmented files is 2.36× longer than that of continuous files and that F2FS’s in-place update scheme is incapable of reducing fragmentation. A fragmented file system leads to a poor user experience.(p)(/p)Reserving space to prevent fragmentation is an intuitive approach. However, reserving space for all files wastes space since there are a large number of files. To deal with this dilemma, we propose an adaptive reserved space (ARS) scheme to choose some specific files to update in the reserved space. How to effectively select reserved files is critical to performance. We collect file characteristics associated with fragmentation to construct data sets and use decision trees to accurately pick reserved files. Besides, adjustable reserved space and dynamic reservation strategy are adopted. We implement ARS on a HiKey960 development platform and a commercial smartphone with slight space and file creation time overheads. Experimental results show that ARS reduces file and free space fragmentation dramatically, improves file I/O performance and reduces garbage collection overhead compared to traditional F2FS and F2FS with in-place updates. Furthermore, ARS delivers up to 1.26× transactions per second under SQLite than traditional F2FS and reduces running time of realistic workloads by up to 41.72% than F2FS with in-place updates.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
ARS:使用决策树减少智能手机的F2FS碎片
众所周知,文件和空闲空间碎片会对文件系统的性能产生负面影响。F2FS是为闪存设计的文件系统。然而,由于它的错位更新和移动应用程序的高度同步、多线程编写行为,它遭受了严重的碎片化。我们观察到碎片文件的运行时间比连续文件的运行时间长2.36倍,并且F2FS的就地更新方案无法减少碎片。(p)(/p)为防止文件系统碎片化而预留空间是一种直观的方法。但是,为所有文件保留空间会浪费空间,因为文件数量很多。为了解决这一难题,我们提出了一种自适应保留空间(ARS)方案,在保留空间中选择一些特定的文件进行更新。如何有效地选择保留文件对性能至关重要。我们收集与碎片相关的文件特征来构建数据集,并使用决策树来准确地选择保留文件。采用可调预留空间和动态预留策略。我们在HiKey960开发平台和商用智能手机上实现ARS,具有轻微的空间和文件创建时间开销。实验结果表明,与传统的F2FS和具有就地更新的F2FS相比,ARS显著减少了文件和空闲空间碎片,提高了文件I/O性能,减少了垃圾收集开销。此外,与传统的F2FS相比,ARS在SQLite下每秒提供高达1.26个事务,并且与具有就地更新的F2FS相比,实际工作负载的运行时间减少了41.72%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
In-Memory Resistive RAM Implementation of Binarized Neural Networks for Medical Applications Towards Formal Verification of Optimized and Industrial Multipliers A 100KHz-1GHz Termination-dependent Human Body Communication Channel Measurement using Miniaturized Wearable Devices Computational SRAM Design Automation using Pushed-Rule Bitcells for Energy-Efficient Vector Processing PIM-Aligner: A Processing-in-MRAM Platform for Biological Sequence Alignment
×
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
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 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 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1