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SLA-driven container consolidation with usage prediction for green cloud computing

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Abstract

Since service level agreement (SLA) is essentially used to maintain reliable quality of service between cloud providers and clients in cloud environment, there has been a growing effort in reducing power consumption while complying with the SLA by maximizing physical machine (PM)-level utilization and load balancing techniques in infrastructure as a service. However, with the recent introduction of container as a service by cloud providers, containers are increasingly popular and will become the major deployment model in the cloud environment and specifically in platform as a service. Therefore, reducing power consumption while complying with the SLA at virtual machine (VM)-level becomes essential. In this context, we exploit a container consolidation scheme with usage prediction to achieve the above objectives. To obtain a reliable characterization of overutilized and underutilized PMs, our scheme jointly exploits the current and predicted CPU utilization based on local history of the considered PMs in the process of the container consolidation. We demonstrate our solution through simulations on real workloads. The experimental results show that the container consolidation scheme with usage prediction reduces the power consumption, number of container migrations, and average number of active VMs while complying with the SLA.

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Acknowledgements

The work presented in this paper was supported by the NSFC (Grant Nos. 61472047 and 61602054), and Beijing Natural Science Foundation (4174100).

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Authors and Affiliations

  1. State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing, 100029, China

    Jialei Liu, Shangguang Wang, Ao Zhou, Jinliang Xu & Fangchun Yang

  2. School of Computer and Communication Engineering, Zhengzhou University of Light Industry, Zhengzhou, 450002, China

    Jialei Liu

Authors
  1. Jialei Liu
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  2. Shangguang Wang
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  3. Ao Zhou
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  4. Jinliang Xu
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  5. Fangchun Yang
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Corresponding author

Correspondence to Shangguang Wang.

Additional information

Jialei Liu is an assistant professor at School of Computer and Communication Engineering, Zhengzhou University of Light Industry, China. He received his PhD degree at Beijing University of Posts and Telecommunications, China in 2018. He received his ME in computer science and technology from Henan Polytechnic University, China in 2008. His research interests include cloud computing and Edge/Fog computing.

Shangguang Wang received his PhD degree at Beijing University of Posts and Telecommunications (BUPT), China in 2011. He is an associate professor at the State Key Laboratory of Networking and Switching Technology, BUPT. He has published more than 100 papers, and played a key role at many international conferences, such as general chair and PC chair. His research interests include service computing, cloud computing, and mobile edge computing. He is a senior member of the IEEE, and the Editor-in-Chief of the International Journal of Web science.

Ao Zhou is an assistant professor at the State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications (BUPT), China. She received her PhD degree in computer science at BUPT in 2015. Her research interests include cloud computing and service reliability.

Jinliang Xu received the bachelor’s degree in electronic information science and technology from Beijing University of Posts and Telecommunications (BUPT), China in 2014. Currently, he is a PhD candidate in computer science at the State Key Laboratory of Networking and Switching Technology, BUPT. His research interests include Mobile Cloud Computing, Service Computing, Information Retrieval, and Crowdsourcing.

Fangchun Yang received his PhD in communications and electronic systems from the Beijing University of Posts and Telecommunication (BUPT), China in 1990. He is currently a professor at BUPT, China. He has published six books and more than 80 papers. His current research interests include network intelligence, service computing, communications software, soft-switching technology, and network security. He is a fellow of the IET.

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Liu, J., Wang, S., Zhou, A. et al. SLA-driven container consolidation with usage prediction for green cloud computing. Front. Comput. Sci. 14, 42–52 (2020). https://doi.org/10.1007/s11704-018-7172-3

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  • DOI: https://doi.org/10.1007/s11704-018-7172-3

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