A Cross-Cluster Real-Time Migration Method for Stateful Container

Wu Xian; Tang Hongbo; Zhao Yu; Xu Mingyan

doi:10.7544/issn1000-1239.202220602

Journal of Computer Research and Development > 2024 > 61(2): 494-502. > DOI: 10.7544/issn1000-1239.202220602

Wu Xian, Tang Hongbo, Zhao Yu, Xu Mingyan. A Cross-Cluster Real-Time Migration Method for Stateful Container[J]. Journal of Computer Research and Development, 2024, 61(2): 494-502. DOI: 10.7544/issn1000-1239.202220602

Citation:

PDF (1119 KB)

A Cross-Cluster Real-Time Migration Method for Stateful Container

Information Engineering University, Zhengzhou 450001

More Information

Author Bio:
Wu Xian: born in 1989. Master candidate. His main research interest includes mobile communication networks and security

Tang Hongbo: born in 1968. Professor, PhD supervisor. His main research interest includes mobile communication networks and security

Zhao Yu: born in 1984. Assistant professor. His main research interest includes mobile communication networks and security

Xu Mingyan: born in 1974. Associate professor. Her main research interest includes mobile communication networks and security
Received Date: June 28, 2022
Revised Date: April 13, 2023
Available Online: November 09, 2023

Graphical Abstract

Abstract

Abstract

The real-time migration technology of cross-cluster stateful virtual containers is an important technology in the cloud application of network services. The optimization of stateful container real-time migration technology can achieve the purpose of shortening the downtime of cloud services, which is of great practical significance in practical applications. At present, there are relatively few studies on cross-cluster container real-time migration optimization, of which iterative migration is a migration method that can effectively reduce downtime, but there are still many problems to be solved in practical applications of this technology. Firstly, a container real-time migration method is proposed for the impact of network latency on the service downtime in cross-cluster scenarios, which focuses on the network latency between clusters, and a mathematical model is established accordingly. Secondly, The influence of network latency on migration performance during cross-cluster migration is analyzed by mathematical model theory, which determines the starting conditions and the termination conditions for enabling the iterative migration process, and gives the method to calculate the number of iterations that satisfy the termination condition. The proposed model fully considers the impact of network latency on cross-cluster container real-time migration, and experimental results show that the proposed method can effectively reduce service downtime.
- container real-time migration,
- network latency,
- service downtime,
- iterative migration,
- cross-cluster

FullText(HTML)

References (15)

References

[1]	吴松,王坤,金海. 操作系统虚拟化的研究现状与展望[J]. 计算机研究与发展,2019,56(1):58−68 doi: 10.7544/issn1000-1239.2019.20180720 Wu Song, Wang Kun, Jin Hai. Research situation and prospects of operating system virtualization[J]. Journal of Computer Research and Development, 2019, 56(1): 58−68 (in Chinese) doi: 10.7544/issn1000-1239.2019.20180720
[2]	Ogbuachi M C, Reale A, Suskovics P, et al. Context-aware kubernetes scheduler for edge-native applications on 5G[J]. Journal of Communications Software and Systems, 2020, 16(1): 85−94 doi: 10.24138/jcomss.v16i1.1027
[3]	祖家琛,胡谷雨,严佳洁,等. 网络功能虚拟化下服务功能链的资源管理研究综述[J]. 计算机研究与发展,2021,58(1):137−152 Zu Jiachen, Hu Guyu, Yan Jiajie, et al. Resource management of service function chain in NFV enabled network: A survey[J]. Journal of Computer Research and Development, 2021, 58(1): 137−152 (in Chinese)
[4]	Lane-walsh S, Stillerman J, Santoro F, et al. Introduction to MDSplus using docker[J]. Fusion Engineering and Design, 2021, 165: 112121 doi: 10.1016/j.fusengdes.2020.112121
[5]	Pickartz S, Eiling N, Lankes S, et al. Migrating Linux containers using CRIU[C]// LNCS 9945: Proc of the 2016 ISC High Performance Workshops. Berlin: Springer, 2016: 674–684
[6]	Red Hat. CRIU - Checkpoint/Restore in user space[EB/OL]. [2022-06-12].https://access.redhat.com/articles/2455211
[7]	Yin Luxiu, Li Pengfei, Luo Juan. Smart contract service migration mechanism based on container in edge computing[J]. Journal of Parallel and Distributed Computing, 2021, 152(1): 157−166
[8]	Venkatesh R S, Smejkal T, Milojicic D S, et al. Fast in-memory CRIU for docker containers[C] //Proc of the Int Symp on Memory Systems. New York: ACM, 2019: 53−65
[9]	Xu Bo, Wu Song, Xiao Jiang, et al. Sledge: Towards efficient live migration of docker containers[C]//Proc of the 13th IEEE Int Conf on Cloud Computing (CLOUD). Piscataway, NJ: IEEE, 2020: 321-328
[10]	Lei Zhou, Sun Exiong, Chen Shengbo, et al. A novel hybrid-copy algorithm for live migration of virtual machine[J]. Future Internet, 2017, 9(3): 37−50 doi: 10.3390/fi9030037
[11]	Lv Liang, Zhang Yuchao, Li Yusen, et al. Communication-aware container placement and reassignment in large-scale internet data centers[J]. IEEE Journal on Selected Areas in Communications, 2019, 37((3): ): 540−555 doi: 10.1109/JSAC.2019.2895473
[12]	Di Zhanyuan, Shao En, Tan Guangming. High-performance migration tool for live container in a workflow[J]. International Journal of Parallel Programming, 2021, 49(5): 658−670 doi: 10.1007/s10766-021-00697-z
[13]	赵倩,谢上钦,韩轲,等. 远程直接内存访问与检查点相结合的容器迁移[J]. 计算机科学与探索,2019,13(12):1995−2007 doi: 10.3778/j.issn.1673-9418.1808054 Zhao Qian, Xie Shangqin, Han Ke, et al. Container migration based on combination of remote direct memory access and check point[J]. Journal of Frontiers of Computer Science and Technology, 2019, 13(12): 1995−2007 (in Chinese) doi: 10.3778/j.issn.1673-9418.1808054
[14]	Stoyanov R, Kollingbaum M J. Efficient live migration of Linux containers[C]// Proc of the 2018 ISC High Performance Workshops. Berlin: Springer, 2018: 184−193
[15]	Junior P S, Miorandi D, Pierre G. Stateful container migration in geo-distributed environments[C/OL]//Proc of the 12th IEEE Int Conf on Cloud Computing Technology and Science (CloudCom). Piscataway, NJ: IEEE, 2020: 49−56

[1]	Zhang Naizhou, Cao Wei, Zhang Xiaojian, Li Shijun. Conversation Generation Based on Variational Attention Knowledge Selection and Pre-trained Language Model[J]. Journal of Computer Research and Development. DOI: 10.7544/issn1000-1239.202440551
[2]	Wang Honglin, Yang Dan, Nie Tiezheng, Kou Yue. Attributed Heterogeneous Information Network Embedding with Self-Attention Mechanism for Product Recommendation[J]. Journal of Computer Research and Development, 2022, 59(7): 1509-1521. DOI: 10.7544/issn1000-1239.20210016
[3]	Cheng Yan, Yao Leibo, Zhang Guanghe, Tang Tianwei, Xiang Guoxiong, Chen Haomai, Feng Yue, Cai Zhuang. Text Sentiment Orientation Analysis of Multi-Channels CNN and BiGRU Based on Attention Mechanism[J]. Journal of Computer Research and Development, 2020, 57(12): 2583-2595. DOI: 10.7544/issn1000-1239.2020.20190854
[4]	Wei Zhenkai, Cheng Meng, Zhou Xiabing, Li Zhifeng, Zou Bowei, Hong Yu, Yao Jianmin. Convolutional Interactive Attention Mechanism for Aspect Extraction[J]. Journal of Computer Research and Development, 2020, 57(11): 2456-2466. DOI: 10.7544/issn1000-1239.2020.20190748
[5]	Chen Yanmin, Wang Hao, Ma Jianhui, Du Dongfang, Zhao Hongke. A Hierarchical Attention Mechanism Framework for Internet Credit Evaluation[J]. Journal of Computer Research and Development, 2020, 57(8): 1755-1768. DOI: 10.7544/issn1000-1239.2020.20200217
[6]	Li Mengying, Wang Xiaodong, Ruan Shulan, Zhang Kun, Liu Qi. Student Performance Prediction Model Based on Two-Way Attention Mechanism[J]. Journal of Computer Research and Development, 2020, 57(8): 1729-1740. DOI: 10.7544/issn1000-1239.2020.20200181
[7]	Zhang Yingying, Qian Shengsheng, Fang Quan, Xu Changsheng. Multi-Modal Knowledge-Aware Attention Network for Question Answering[J]. Journal of Computer Research and Development, 2020, 57(5): 1037-1045. DOI: 10.7544/issn1000-1239.2020.20190474
[8]	Zhang Yixuan, Guo Bin, Liu Jiaqi, Ouyang Yi, Yu Zhiwen. app Popularity Prediction with Multi-Level Attention Networks[J]. Journal of Computer Research and Development, 2020, 57(5): 984-995. DOI: 10.7544/issn1000-1239.2020.20190672
[9]	Liu Ye, Huang Jinxiao, Ma Yutao. An Automatic Method Using Hybrid Neural Networks and Attention Mechanism for Software Bug Triaging[J]. Journal of Computer Research and Development, 2020, 57(3): 461-473. DOI: 10.7544/issn1000-1239.2020.20190606
[10]	Zhang Zhichang, Zhang Zhenwen, Zhang Zhiman. User Intent Classification Based on IndRNN-Attention[J]. Journal of Computer Research and Development, 2019, 56(7): 1517-1524. DOI: 10.7544/issn1000-1239.2019.20180648