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Efficient resource scaling based on load fluctuation in edge-cloud computing environment

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Abstract

With the rapid development of information technology, edge computing has grown rapidly by pushing large amounts of computing to the edge of the network. However, due to the rapid growth of edge access devices and limited edge storage space, the edge cloud faces many challenges in addressing the workloads. In this paper, a cost-optimized resource scaling strategy is proposed based on load fluctuation. Firstly, the load prediction model is built based on DBN with supervised learning to predict the workloads of edge cloud. Then, a cost-optimized resource scaling strategy is presented, which comprehensively considers reservation planning and on-demand planning. In the reservation phase, the long-term resource reservation problem is planned as a two-stage stochastic programming problem, which is transformed into a deterministic integer programming problem. In the on-demand phase, the on-demand resource scaling problem planning is solved as an integer programming problem. Finally, extensive experiments are conducted to evaluate the performance of the proposed cost-optimized resource scaling strategy based on load fluctuation.

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Acknowledgments

The work was supported by the National Natural Science Foundation (NSF) under grants (Nos 61871352, 61672397), Application Foundation Frontier Project of WuHan (No. 2018010401011290), Open fund of Chongqing Engineering and Technology Research Center for Big Data of Public Transportation Operation (No. 2019JTDSJ-ZD02), the Fundamental Research Funds for the Central Universities (No. 2019-YB-028). 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. Chongqing Engineering and Technology Research Center for Big Data of Public Transportation Operation, Chongqing Jiaotong University, Chongqing, People’s Republic of China

    Chunlin Li

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

    Chunlin Li, Jingpan Bai & Youlong Luo

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  1. Chunlin Li
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  2. Jingpan Bai
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Correspondence to Chunlin Li.

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Li, C., Bai, J. & Luo, Y. Efficient resource scaling based on load fluctuation in edge-cloud computing environment. J Supercomput 76, 6994–7025 (2020). https://doi.org/10.1007/s11227-019-03134-8

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