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Boosted regression for predicting CPU utilization in the cloud with periodicity

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Abstract

Predicting CPU usage is crucial to cloud resource management. Precise CPU prediction, however, is a tough challenge due to the variable and dynamic nature of CPUs. In this paper, we introduce TrAdaBoost.WLP, a novel regression transfer boosting method that employs Long Short-Term Memory (LSTM) networks for CPU consumption prediction. Concretely, a dedicated Periodicity-aware LSTM (PA-LSTM) model is specifically developed to take into account the use of periodically repeated patterns in time series data while making predictions. To adjust for variations in CPU demands, multiple PA-LSTMs are trained and concatenated in TrAdaBoost.WLP using a boosting mechanism. TrAdaBoost.WLP and benchmarks have been thoroughly evaluated on two datasets: 160 Microsoft Azure VMs and 8 Google cluster traces. The experimental results show that TrAdaBoost.WLP can produce promising performance, improving by 32.4% and 59.3% in terms of mean squared error compared to the standard Probabilistic LSTM and ARIMA.

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Data Availability Statement

This research uses the Azure Public Dataset V1 [14] and Google cluster traces [15]. The processed data is publicly available on this GitHub repository [43].

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Acknowledgments

This research is funded by Hanoi University of Science and Technology (HUST) under the project number T2022-PC-048.

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Author notes

  1. Van Tong, Cuong Dao, Tuyen Ngoc Le, and Duc Tran contributed equally to this work.

Authors and Affiliations

  1. Hanoi University of Science and Technology, Hanoi, 100000, Vietnam

    Khanh Nguyen Quoc, Van Tong & Duc Tran

  2. Hanoi University of Civil Engineering, Hanoi, 100000, Vietnam

    Cuong Dao

  3. Department of Electronic Engineering, Ming Chi University of Technology, New Taipei, 24301, Taiwan, ROC

    Tuyen Ngoc Le

  4. Center for Reliability Engineering, Ming Chi University of Technology, New Taipei, 24301, Taiwan, ROC

    Tuyen Ngoc Le

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  1. Khanh Nguyen Quoc
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Contributions

All authors participated in the writing and review of the manuscript. Van Tong and Duc Tran proposed ideas for the manuscript. Khanh Nguyen Quoc and Cuong Dao generated all the figures and tables.

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Correspondence to Duc Tran.

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Quoc, K.N., Tong, V., Dao, C. et al. Boosted regression for predicting CPU utilization in the cloud with periodicity. J Supercomput 80, 26036–26060 (2024). https://doi.org/10.1007/s11227-024-06451-9

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