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Elastic edge cloud resource management based on horizontal and vertical scaling

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Abstract

The resources in the edge cloud are numerous and complex, and elastic scaling services can make efficient use of these resources. However, the elastic scaling services need to suspend the user’s application tasks forcibly when carrying out resource redistribution, which brings a poor sense of experience to the user. In view of the above problems, a dynamic elastic scaling model based on load prediction is proposed, which improves resource utilization and reduces scaling costs without affecting user experience. The model is divided into two parts. In terms of load prediction, on the one hand, according to the historical features and current trends of the load, the load prediction model based on the improved cloud model is used to predict the load demand at the next moment. On the other hand, the correlation between CPU and memory is considered. In terms of elastic scaling, integer programming algorithm is proposed to expand and release the corresponding resources with the minimum cost of horizontal scaling (HS) and vertical scaling (VS). In order to verify the superiority of elastic scaling model based on load prediction, corresponding comparative experiments are conducted, which show that the proposed model can improve the accuracy of load prediction and resource utilization with low scaling costs. Especially, the cost of elastic scaling proposed by this paper is lower than horizontal and vertical scaling. Compared with HS, the elastic scaling method proposed in this paper reduces the cost by 14%. Compared with VS, this method reduces the cost by 11%.

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Acknowledgements

The work was supported by the National Natural Science Foundation (NSF) under Grants (Nos. 61672397, 61873341), Application Foundation Frontier Project of WuHan (No. 2018010401011290), open fund of Anhui Province Key Laboratory of Big Data Analysis and Application. Any opinions, findings and conclusions are those of the authors and do not necessarily reflect the views of the above agencies.

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Authors and Affiliations

  1. Department of Computer Science, Wuhan University of Technology, Wuhan, 430063, People’s Republic of China

    Chunlin Li, Jianhang Tang & Youlong Luo

  2. Artificial Intelligence Key Laboratory of Sichuan Province, Sichuan University of Science and Engineering, Zigong, People’s Republic of China

    Chunlin Li

  3. Anhui Province Key Laboratory of Big Data Analysis and Application, Hefei, People’s Republic of China

    Chunlin Li

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  1. Chunlin Li
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  2. Jianhang Tang
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  3. Youlong Luo
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Correspondence to Chunlin Li.

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Li, C., Tang, J. & Luo, Y. Elastic edge cloud resource management based on horizontal and vertical scaling. J Supercomput 76, 7707–7732 (2020). https://doi.org/10.1007/s11227-020-03192-3

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  • DOI: https://doi.org/10.1007/s11227-020-03192-3

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