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Fault-tolerant feedback virtual machine deployment based on user-personalized requirements

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Abstract

A key requirement of the cloud platform is the reasonable deployment of its large-scale virtual machine infrastructure. The mapping relation between the virtual node and the physical node determines the specific resource distribution strategy and reliability of the virtual machine deployment. Resource distribution strategy has an important effect on performance, energy consumption, and guarantee of the quality of service of the computer, and serves an important role in the deployment of the virtual machine. To solve the problem of meeting the fault-tolerance requirement and guarantee high reliability of the application system based on the full use of the cloud resource under the prerequisite of various demands, the deployment framework of the feedback virtual machine in cloud platform facing the individual user’s demands of fault-tolerance level and the corresponding deployment algorithm of the virtual machine are proposed in this paper. Resource distribution strategy can deploy the virtual machine in the physical nodes where the resource is mutually complementary according to the users’ different requirements on virtual resources. The deployment framework of the virtual machine in this paper can provide a reliable computer configuration according to the specific fault-tolerance requirements of the user while considering the usage rate of the physical resources of the cloud platform. The experimental result shows that the method proposed in this paper can provide flexible and reliable select permission of fault-tolerance level to the user in the virtual machine deployment process, provide a pertinent individual fault-tolerant deployment method of the virtual machine to the user, and guarantee to meet the user service in a large probability to some extent.

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Acknowledgements

This work was supported in part by the National Basic Research Program of China (2015CB352401), Natural Science Foundation of Hunan Province (2018JJ2193), National Natural Science Foundation of China (Grant No. 61532013), Scientific Research Fund of Hunan Provincial Education Department (16A115), and the 12th five-year plan of Hunan province education science project (XKJ013AXX002). The work was also supported by the research project of teaching reform in ordinary universities of Hunan province in 2017 (No. 571). We would like to express our thanks to the editors and reviewers for their valuable comments and suggestions to improve the presentation of our manuscript.

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Authors and Affiliations

  1. Department of Information Technology, Hunan Women’s University, Changsha, 410004, China

    Shukun Liu & Xianmin Pan

  2. Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China

    Weijia Jia

Authors
  1. Shukun Liu
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  2. Weijia Jia
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  3. Xianmin Pan
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Additional information

Shukun Liu received his PhD degree in computer science and technology from Central South University, China in 2016. His major research interests include cloud computing, virtualization technology, performance analysis, computer networks, database technology, data mining, and software engineering. He has published nearly 20 papers in related journals, and he is a member of CCF and ACM.

Weijia Jia is a Zhiyuan Chair Professor in the Department of Computer Science and Engineering, Shanghai Jiaotong University, China. He joined the German National Research Center for Information Science (GMD) in Bonn (St. Augustine) from 1993 to 1995 as a research fellow. During 1995 to 2013, he joined the Department of Computer Science, City University of Hong Kong, China as a professor. He has served as an editor of the IEEE TPDS and PC chair, and a member/keynote speaker for various prestigious international conferences. He is also a senior member of the IEEE and a member of ACM.

Xianmin Pan is a professor in the Department of Information Technology, Hunan Women’s University, China. His major research interests include cloud computing, computer networks, data mining, and software engineering.

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Liu, S., Jia, W. & Pan, X. Fault-tolerant feedback virtual machine deployment based on user-personalized requirements. Front. Comput. Sci. 12, 682–693 (2018). https://doi.org/10.1007/s11704-017-6422-0

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  • DOI: https://doi.org/10.1007/s11704-017-6422-0

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