Boosting Multi-Block Repair in Cloud Storage Systems with Wide-Stripe Erasure Coding

Qi Yu, Lin Wang, Yuchong Hu, Yumeng Xu, D. Feng, Jie Fu, Xia Zhu, Zhen Yao, Wenjia Wei
{"title":"Boosting Multi-Block Repair in Cloud Storage Systems with Wide-Stripe Erasure Coding","authors":"Qi Yu, Lin Wang, Yuchong Hu, Yumeng Xu, D. Feng, Jie Fu, Xia Zhu, Zhen Yao, Wenjia Wei","doi":"10.1109/IPDPS54959.2023.00036","DOIUrl":null,"url":null,"abstract":"Cloud storage systems have commonly used erasure coding that encodes data in stripes of blocks as a low-cost redundancy method for data reliability. Relative to traditional erasure coding, wide-stripe erasure coding that increases the stripe size has been recently proposed and explored to achieve lower redundancy. We observe that wide-stripe erasure coding makes multi-block failures occur much more frequently than traditional erasure coding in cloud storage systems.However, how to efficiently repair multiple blocks in wide-stripe erasure-coded storage systems remains unexplored. The conventional multi-block repair method sends available blocks from surviving nodes to one single new node to repair all failed blocks in a centralized way, which may cause the new node to be the bottleneck; recent multi-block repair methods follow pipelined single-block repair methods and the former are simply built on the latter in an independent way, which may cause the surviving nodes with limited bandwidth to be bottlenecks.In this paper, we first analyze the effects of both centralized and independent ways on the multi-block repair and then propose HMBR, a hybrid multi-block repair mechanism that combines centralized and independent multi-block repairs to tradeoff the bandwidth bottlenecks caused by the new and surviving nodes, thus optimizing the multi-block repair performance. We further extend HMBR for hierarchical network topology and multi-node failures. We prototype HMBR and show via Amazon EC2 that the repair time of a multi-block failure can be reduced by up to 64.8% over state-of-the-art schemes.","PeriodicalId":343684,"journal":{"name":"2023 IEEE International Parallel and Distributed Processing Symposium (IPDPS)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2023 IEEE International Parallel and Distributed Processing Symposium (IPDPS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IPDPS54959.2023.00036","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

Cloud storage systems have commonly used erasure coding that encodes data in stripes of blocks as a low-cost redundancy method for data reliability. Relative to traditional erasure coding, wide-stripe erasure coding that increases the stripe size has been recently proposed and explored to achieve lower redundancy. We observe that wide-stripe erasure coding makes multi-block failures occur much more frequently than traditional erasure coding in cloud storage systems.However, how to efficiently repair multiple blocks in wide-stripe erasure-coded storage systems remains unexplored. The conventional multi-block repair method sends available blocks from surviving nodes to one single new node to repair all failed blocks in a centralized way, which may cause the new node to be the bottleneck; recent multi-block repair methods follow pipelined single-block repair methods and the former are simply built on the latter in an independent way, which may cause the surviving nodes with limited bandwidth to be bottlenecks.In this paper, we first analyze the effects of both centralized and independent ways on the multi-block repair and then propose HMBR, a hybrid multi-block repair mechanism that combines centralized and independent multi-block repairs to tradeoff the bandwidth bottlenecks caused by the new and surviving nodes, thus optimizing the multi-block repair performance. We further extend HMBR for hierarchical network topology and multi-node failures. We prototype HMBR and show via Amazon EC2 that the repair time of a multi-block failure can be reduced by up to 64.8% over state-of-the-art schemes.
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
利用宽条擦除编码促进云存储系统的多块修复
云存储系统通常采用擦除编码(erasure coding),将数据以条状块的形式编码,作为一种低成本的冗余方式,提高数据可靠性。相对于传统的擦除编码,近年来人们提出并探索了增加条带大小的宽条带擦除编码,以达到较低的冗余。我们观察到,在云存储系统中,宽条擦除编码比传统的擦除编码更频繁地发生多块故障。然而,如何有效地修复宽条擦除编码存储系统中的多个块仍然是一个未探索的问题。传统的多块修复方法是将幸存节点的可用块发送到单个新节点,集中修复所有故障块,这可能导致新节点成为瓶颈;目前的多块修复方法都遵循流水线式单块修复方法,且多块修复方法都是在单块修复方法的基础上独立构建的,这可能导致带宽有限的幸存节点成为瓶颈。本文首先分析了集中式和独立式两种方式对多块修复的影响,然后提出了一种混合多块修复机制HMBR,该机制结合了集中式和独立式多块修复,以权衡新节点和幸存节点带来的带宽瓶颈,从而优化多块修复性能。我们进一步扩展了HMBR用于分层网络拓扑和多节点故障。我们对HMBR进行了原型设计,并通过Amazon EC2显示,与最先进的方案相比,多块故障的修复时间最多可减少64.8%。
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
GPU-Accelerated Error-Bounded Compression Framework for Quantum Circuit Simulations Generalizable Reinforcement Learning-Based Coarsening Model for Resource Allocation over Large and Diverse Stream Processing Graphs Smart Redbelly Blockchain: Reducing Congestion for Web3 QoS-Aware and Cost-Efficient Dynamic Resource Allocation for Serverless ML Workflows Fast Sparse GPU Kernels for Accelerated Training of Graph Neural Networks
×
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