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Performance and isolation analysis of RunC, gVisor and Kata Containers runtimes

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Abstract

Containers are resource-efficient and most IT industries are adopting container-based infrastructure. However, the security and isolation of the container is rather weak. In this work, we aim to conduct an in-depth quantitative analysis of the performance characteristics of containerization technologies that strengthen container isolation and security, and discuss the applicable scenarios of various containerization technologies. We evaluate multiple cloud resource management dimensions of RunC, gVisor, and Kata Containers runtimes, including performance, system call, startup time, density, and isolation. Experimental results show that RunC and Kata Containers have less performance overhead, while gVisor suffers significant performance degradation in I/O and system call, although its isolation is the best. Our work deepens the understanding of the container performance characteristics and may help cloud computing practitioners in making proper decisions on platform selection, system maintenance and/or design.

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Data available on request from the authors.

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Acknowledgements

This work is partially supported by a grant from the National Natural Science Foundation of China (No.62032017), the National Key R&D Program of China (2018YFB1003605), the Key Industrial Innovation Chain Project in Industrial Domain of Shaanxi Province (Nos. 2021ZDLGY03-09, 2021ZDLGY07-02), and the Youth Innovation Team of Shaanxi Universities.

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  1. School of Computer Science and Technology, Xidian University, Xi ’an, China

    Xingyu Wang, Junzhao Du & Hui Liu

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  1. Xingyu Wang
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  2. Junzhao Du
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All authors contributed equally to this work.

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Correspondence to Junzhao Du or Hui Liu.

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This paper does not contain any studies involving human participants or animals performed by any of the authors. Xingyu Wang carried out the experiment and wrote the manuscript.

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Wang, X., Du, J. & Liu, H. Performance and isolation analysis of RunC, gVisor and Kata Containers runtimes. Cluster Comput 25, 1497–1513 (2022). https://doi.org/10.1007/s10586-021-03517-8

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