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基于云预存储技术的Docker在线迁移方法

赵旭 李艳梅 罗建 罗金梅

赵旭, 李艳梅, 罗建, 罗金梅. 基于云预存储技术的Docker在线迁移方法. 自动化学报, 2023, 49(11): 2426−2436 doi: 10.16383/j.aas.c180777
引用本文: 赵旭, 李艳梅, 罗建, 罗金梅. 基于云预存储技术的Docker在线迁移方法. 自动化学报, 2023, 49(11): 2426−2436 doi: 10.16383/j.aas.c180777
Zhao Xu, Li Yan-Mei, Luo Jian, Luo Jin-Mei. Docker online migration method based on cloud pre-storage technology. Acta Automatica Sinica, 2023, 49(11): 2426−2436 doi: 10.16383/j.aas.c180777
Citation: Zhao Xu, Li Yan-Mei, Luo Jian, Luo Jin-Mei. Docker online migration method based on cloud pre-storage technology. Acta Automatica Sinica, 2023, 49(11): 2426−2436 doi: 10.16383/j.aas.c180777

基于云预存储技术的Docker在线迁移方法

doi: 10.16383/j.aas.c180777
基金项目: 国家自然科学基金(61731330), 教育部产学协同育人项目(201801154055, 201801246016, 201802003022), 四川省教育厅自然科学重点项目(18ZA0468, 14ZA0123), 西华师范大学创新团队项目(CXTD2014-11), 西华师范大学博士启动项目(13E005), 西华师范大学英才基金项目(17YC155, 17YC157)资助
详细信息
    作者简介:

    赵旭:西华师范大学计算机学院硕士研究生. 主要研究方向为云计算, 虚拟化平台.E-mail: zhaouxu_xu@163.com

    李艳梅:西华师范大学计算机学院副教授. 2013年获得电子科技大学计算机科学与工程学院博士学位. 主要研究方向为计算机视觉, 云计算与大数据处理. 本文通信作者. E-mail: liyanmei76@126.com

    罗建:西华师范大学计算机学院副教授. 2009年获得电子科技大学计算机科学与工程学院硕士学位. 主要研究方向为计算机视觉, 云计算与大数据处理. E-mail: luojian@cwnu.edu.cn

    罗金梅:西华师范大学计算机学院硕士研究生. 主要研究方向为计算机视觉和云计算. E-mail: luojinmei_w@163.com

Docker Online Migration Method Based on Cloud Pre-Storage Technology

Funds: Supported by National Natural Science Foundation of China (61731330), Cooperative Production and Learning and Collaborative Education Project of the Ministry of Education (201801154055, 201801246016, 201802003022), the Key Natural Science Foundation of the Education Department of Sichuan Province of China (18ZA0468, 14ZA0123), the Innovation Team Project of China West Normal University (CXTD2014-11), the Doctoral Foundation Project (13E005), and the Meritocracy Research Fund of China West Normal University (17YC155, 17YC157)
More Information
    Author Bio:

    ZHAO Xu Master student at the School of Computer Science, China West Normal University. His research interest covers cloud computing and virtualization platform

    LI Yan-Mei Associate professor at the School of Computer Science, China West Normal University. She received her Ph.D. degree from the School of Computer Science and Engineering, University of Electronic Science and Technology of China in 2013. Her research interest covers computer vision, cloud computing, and big data processing. Corresponding author of this paper

    LUO Jian Associate professor at the School of Computer Science, China West Normal University. He received his bachelor degree from the School of Computer Science and Engineering, University of Electronic Science and Technology of China in 2009. His research interest covers computer vision, cloud computing, and big data processing

    LUO Jin-Mei Master student at the School of Computer Science, China West Normal University. Her research interest covers computer vision and cloud computing

  • 摘要: 针对基于Docker容器的分布式云计算下出现负载不均衡问题, 有必要将较高负载服务器中的Docker容器进程迁移到其他相对空闲的服务器上. 而传统的容器迁移算法忽视了容器本身的特征, 从而导致在迁移过程中传输效率低下. 基于此, 利用第三方管理平台和数据预存储阈值机制, 提出一种Docker容器动态迁移预存储算法PF-Docker. 首先将Docker容器内部进程运行相关文件和流动数据预存至云端存储器, 然后通过预存储阈值机制减少流动数据的无效传输, 最后在停机传输阶段将流动数据和冗余数据传输给目的服务器. 实验表明, 该方法在Docker容器迁移中能有效地降低迁移时间, 减少数据传输量, 提高容器的容错率.
  • 图  1  2018年RightScale云计算调查报告

    Fig.  1  RightScale cloud survey report of 2018

    图  2  Stream测试KVM和Docker开销

    Fig.  2  Stream testing for KVM and Docker overheads

    图  3  基于预存储的容器动态迁移框架: PF-Docker

    Fig.  3  Prestorage-based dynamic migration framework for containers: PF-Docker

    图  4  迭代传输开销分析

    Fig.  4  Iterative transmission overhead analysis

    图  5  动态云预存传输流程图

    Fig.  5  Dynamic cloud pre-storage transfer flowchart

    图  6  动态迭代迁移流程图

    Fig.  6  Dynamic iterative migration flowchart

    图  7  读写空闲场景下两种Docker动态迁移方法性能对比

    Fig.  7  Performance comparison of two Docker dynamic migration methods in read-write idle scenarios

    图  8  迁移总时间对比

    Fig.  8  The comparison of total migration time

    图  9  源主机迁移总数据量对比

    Fig.  9  The comparison of total data volume of source host migration

    图  10  Stream测试下PF-Docker与LM-KVM对比

    Fig.  10  The comparison of PF-Docker and LM-KVM under Stream test

    图  11  内核编译负载下Docker与KVM对比

    Fig.  11  The comparison of Docker and KVM under the kernel compilation load

    图  12  多线程内核编译在不同预存条件下对比

    Fig.  12  The comparison of multithreaded kernel compilation under different pre-stored conditions

    表  1  实验负载类型表

    Table  1  Experimental load type table

    负载类型具体配置
    Loop测试基础镜像为Ubuntu14.04.1, 在Ubuntu操作系统下执行永真循环打印程序(低负载写空闲)
    Stream基准测试基础镜像为Ubuntu14.04.1, 通过设置不同的内存读写速率进行测试(高负载写密集)
    多线程内核编译测试在Ubuntu14.04.1上部署内核编译依赖环境, 生成镜像zx/Ubuntu1, 在不同线程下测试(并行程序写密集)
    下载: 导出CSV
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出版历程
  • 收稿日期:  2018-11-21
  • 录用日期:  2019-07-17
  • 网络出版日期:  2023-10-19
  • 刊出日期:  2023-11-22

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