Olsync: Object-level tiering and coordination in tiered storage systems based on software-defined network

IF 6.2 2区 计算机科学 Q1 COMPUTER SCIENCE, THEORY & METHODS Future Generation Computer Systems-The International Journal of Escience Pub Date : 2024-09-04 DOI:10.1016/j.future.2024.107521
Zhike Li , Yong Wang , Shiqiang Nie , Jinyu Wang , Chi Zhang , Fangxing Yu , Zhankun Zhang , Song Liu , Weiguo Wu
{"title":"Olsync: Object-level tiering and coordination in tiered storage systems based on software-defined network","authors":"Zhike Li ,&nbsp;Yong Wang ,&nbsp;Shiqiang Nie ,&nbsp;Jinyu Wang ,&nbsp;Chi Zhang ,&nbsp;Fangxing Yu ,&nbsp;Zhankun Zhang ,&nbsp;Song Liu ,&nbsp;Weiguo Wu","doi":"10.1016/j.future.2024.107521","DOIUrl":null,"url":null,"abstract":"<div><p>With the adoption of new storage technologies like NVMs, tiered storage has gained popularity in large-scale, hyper-converged clusters. The storage back-end of hyper-converged systems supports data storage on devices such as SSDs and HDDs, yet lacks fine-grained tiered storage solutions. For example, Ceph selects storage nodes based primarily on limited criteria, such as node storage capacity, disregarding the diverse performance characteristics of various storage media. In this study, we introduce Olsync, an object-level tiering and coordination system designed to enhance storage resource utilization and data access performance. Specifically, Olsync employs PIPO (Packet-In-Packet-Out), an innovative network communication framework based on Software-defined Networking (SDN), to collaboratively optimize both the network control plane and underlying data plane. Additionally, Olsync can offer efficient object-level tiering and coordination services using the global views obtained by PIPO (e.g., data access patterns and interfering object requests) to make tiered storage and performance optimization decisions. We incorporated the Olsync prototype into Ceph and performed a thorough comparison with contemporary state-of-the-art systems. The evaluation results demonstrate that Olsync significantly enhances system response time (up to 68%), I/O throughput (up to 24%), and 99th percentile latency (up to 16%) in various environments.</p></div>","PeriodicalId":55132,"journal":{"name":"Future Generation Computer Systems-The International Journal of Escience","volume":"163 ","pages":"Article 107521"},"PeriodicalIF":6.2000,"publicationDate":"2024-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Future Generation Computer Systems-The International Journal of Escience","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0167739X24004850","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, THEORY & METHODS","Score":null,"Total":0}
引用次数: 0

Abstract

With the adoption of new storage technologies like NVMs, tiered storage has gained popularity in large-scale, hyper-converged clusters. The storage back-end of hyper-converged systems supports data storage on devices such as SSDs and HDDs, yet lacks fine-grained tiered storage solutions. For example, Ceph selects storage nodes based primarily on limited criteria, such as node storage capacity, disregarding the diverse performance characteristics of various storage media. In this study, we introduce Olsync, an object-level tiering and coordination system designed to enhance storage resource utilization and data access performance. Specifically, Olsync employs PIPO (Packet-In-Packet-Out), an innovative network communication framework based on Software-defined Networking (SDN), to collaboratively optimize both the network control plane and underlying data plane. Additionally, Olsync can offer efficient object-level tiering and coordination services using the global views obtained by PIPO (e.g., data access patterns and interfering object requests) to make tiered storage and performance optimization decisions. We incorporated the Olsync prototype into Ceph and performed a thorough comparison with contemporary state-of-the-art systems. The evaluation results demonstrate that Olsync significantly enhances system response time (up to 68%), I/O throughput (up to 24%), and 99th percentile latency (up to 16%) in various environments.

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
Olsync:基于软件定义网络的分层存储系统中的对象级分层与协调
随着 NVM 等新存储技术的采用,分层存储在大规模超融合集群中越来越受欢迎。超融合系统的存储后端支持 SSD 和 HDD 等设备上的数据存储,但缺乏细粒度的分层存储解决方案。例如,Ceph 主要根据节点存储容量等有限标准选择存储节点,而忽略了各种存储介质的不同性能特点。在本研究中,我们介绍了 Olsync,这是一个对象级分层和协调系统,旨在提高存储资源利用率和数据访问性能。具体来说,Olsync 采用基于软件定义网络(SDN)的创新网络通信框架 PIPO(包入包出),协同优化网络控制平面和底层数据平面。此外,Olsync 还能利用 PIPO 获得的全局视图(如数据访问模式和干扰对象请求)提供高效的对象级分层和协调服务,从而做出分层存储和性能优化决策。我们将 Olsync 原型纳入了 Ceph,并与当代最先进的系统进行了全面比较。评估结果表明,Olsync 能在各种环境中显著提高系统响应时间(高达 68%)、I/O 吞吐量(高达 24%)和第 99 百分位数延迟(高达 16%)。
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
CiteScore
19.90
自引率
2.70%
发文量
376
审稿时长
10.6 months
期刊介绍: Computing infrastructures and systems are constantly evolving, resulting in increasingly complex and collaborative scientific applications. To cope with these advancements, there is a growing need for collaborative tools that can effectively map, control, and execute these applications. Furthermore, with the explosion of Big Data, there is a requirement for innovative methods and infrastructures to collect, analyze, and derive meaningful insights from the vast amount of data generated. This necessitates the integration of computational and storage capabilities, databases, sensors, and human collaboration. Future Generation Computer Systems aims to pioneer advancements in distributed systems, collaborative environments, high-performance computing, and Big Data analytics. It strives to stay at the forefront of developments in grids, clouds, and the Internet of Things (IoT) to effectively address the challenges posed by these wide-area, fully distributed sensing and computing systems.
期刊最新文献
Editorial Board AIHO: Enhancing task offloading and reducing latency in serverless multi-edge-to-cloud systems DSDM-TCSE: Deterministic storage and deletion mechanism for trusted cloud service environments Energy management in smart grids: An Edge-Cloud Continuum approach with Deep Q-learning Service migration with edge collaboration: Multi-agent deep reinforcement learning approach combined with user preference adaptation
×
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