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A cloud computing load prediction hybrid model with adaptive weight strategy

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Abstract

In recent years, cloud computing has revolutionized computing. The resource usage prediction of machine workloads has been one of the most important and challenging problems. Load prediction in cloud computing is challenging due to the inefficiency of feature extraction and complex loading environment. Attention mechanism based Multi-level Feature Extraction (MFE) model has been developed. Empirical mode decomposition has been selected to extract the sequence surface features. Multi-level LSTM has been used to extract multi-level features of the decomposed load in the empirical mode decomposition. Attention mechanism has been adopted to get different weights for the extracted features. In order to further get causal relationship of time series data, Empirical mode decomposition based Temporal Convolutional Network (ETCN) model has been developed. Multiple temporal convolutional network residual blocks are stacked to improve the historical memory ability and increase the local receptive field. An adaptive weight strategy has been proposed to assign different weights to the developed MFE and ETCN. The weights are updated adaptively according to the errors in MFE and ETCN, respectively. The proposed method has excellent performance in cloud computing on some challenging datasets by comparison with some state-of-the-art ones. The proposed method achieves an average improvement of 30.23% and 41.5% on the NASA datasets and Saskatchewan datasets, respectively.

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Data availability

All datasets available in the Internet Traffic Archive. http://ita.ee.lbl.gov/html/ traces.html.

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Acknowledgements

This work is supported in part by National Key R&D Program of China (Grant no. 2020YFC1523004).

Funding

This work is supported in part by National Key R&D Program of China (Grant no. 2020YFC1523004).

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Authors and Affiliations

  1. School of Communication and Information Engineering, Shanghai University, Shanghai, 200444, China

    Chao Xiong & Yepeng Guan

  2. Key Laboratory of Advanced Display and System Application, Ministry of Education, Shanghai, 200072, China

    Yepeng Guan

Authors
  1. Chao Xiong
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  2. Yepeng Guan
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Contributions

C.X. conceived of the presented idea. C.X. developed the theory and performed the computations. C.X. wrote the main manuscript version and completed all experiments, and Y.G. revised the manuscript. C.X. and Y.G. verified the analytical methods. Y.G. encouraged C.X. to investigate and supervised the findings of this work. All authors discussed the results and contributed to the final manuscript.

Corresponding author

Correspondence to Yepeng Guan.

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Xiong, C., Guan, Y. A cloud computing load prediction hybrid model with adaptive weight strategy. SIViP 17, 2101–2109 (2023). https://doi.org/10.1007/s11760-022-02424-8

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