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A cloud load forecasting model with nonlinear changes using whale optimization algorithm hybrid strategy

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Abstract

Cloud services with the property of elasticity are always bearing different loads over time. The efficiency of cloud management can be improved through the accurate prediction for cloud loads, which can be served as the foundation and recommendation for the future developing of the network scheme. As the basis and premise for management and decision making, a good prediction depends on suitable models and research methods. However, the interaction of users and the cloud network is high variability in the time and spatial dimensions, so traditional linear forecasting models cannot always process cloud computing loads well because of nonlinear changes. In this paper, a novel cloud load prediction model is proposed by improving whale optimization algorithm based on the hybrid strategy (HWOA) and combined with extreme learning machine (ELM) for strong nonlinear mapping ability. The proposed cloud load forecasting model is to employ HWOA optimizer to optimize the ELM model random parameters. Main research work includes that (1) the HWOA optimizer is to solve the whale optimizer local extremum problem; (2) the proposed HWOA optimizer reduces the ELM random parameters on cloud load forecasting; (3) the convergence performance is verified by benchmark testing functions; and (4) three groups of simulation experiments are conducted to evaluate the cloud load forecasting results. The results prove that the HWOA optimizer has a good convergence and outperforms the several conventional popular swarm intelligence optimizers. Also, the prediction results verify that our proposed model is more competitive for providing a solid foundation for efficient resource management and maximized economic benefits in cloud environment. For cloud load research groups and practitioners, this paper provides a new idea of cloud load forecasting.

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Acknowledgements

This study was supported by the National Natural Science Foundation of China (Grant No. 51475136) and Guangdong science and technology plan project of China (Grant No. 2016B030305007).

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Authors and Affiliations

  1. School of Data and Computer Science, Sun Yat-Sen University, Guangzhou, 510006, China

    Hua Peng & Wu-Shao Wen

  2. Institute of Innovation and Circular Economy, Asia University, Wufeng, Taiwan

    Ming-Lang Tseng

  3. Department of Medical, China Medical University Hospital, China Medical University, Taichung, Taiwan

    Ming-Lang Tseng

  4. Faculty of Economics and Management, Universiti Kebangsaan Malaysia, Bangi, Malaysia

    Ming-Lang Tseng

  5. State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin, 300130, China

    Ling-Ling Li

Authors
  1. Hua Peng
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  2. Wu-Shao Wen
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  3. Ming-Lang Tseng
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  4. Ling-Ling Li
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Contributions

H. P. and W.-S. W. contributed to conceptualization; H. P. and W.-S. W. were involved in methodology; L.-L. L. and M.-L. T. provided software; L.-L. L. and M.-L. T. contributed to formal analysis; H.-P., W.-S. W. and M.-L. T. were involved in data curation; and H. P., W.-S. W., M.-L. T. and L.-L. L contributed to writing—original draft preparation. All authors have read and agreed to the published version of the manuscript.

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Correspondence to Ming-Lang Tseng.

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Peng, H., Wen, WS., Tseng, ML. et al. A cloud load forecasting model with nonlinear changes using whale optimization algorithm hybrid strategy. Soft Comput 25, 10205–10220 (2021). https://doi.org/10.1007/s00500-021-05961-5

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