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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.

References

  1. Tang, Z., Xiao, Z., Yang, L., et al.: A network load perception-based task scheduler for parallel distributed data processing systems. IEEE Trans. Cloud Comput. 1(1), 1–15 (2021)

    Google Scholar 

  2. Chen, J., Li, K.: A parallel random forest algorithm for big data in a spark cloud computing environment. IEEE Trans. Parallel Distrib. Syst. 28(4), 919–933 (2016)

    Article  Google Scholar 

  3. Tang, Z., Du, L., Zhang, X., et al.: AEML: An acceleration engine for multi-GPU load-balancing in distributed heterogeneous environment. IEEE Trans. Comput. 71(6), 1344–1357 (2021)

    MATH  Google Scholar 

  4. Chen, J., Li, K., Ronga, H., et al.: A periodicity-based parallel time series prediction algorithm in cloud computing environments. Inf. Sci. 496(1), 506–537 (2019)

    Article  Google Scholar 

  5. Chen, J., Li, K., Deng, Q., et al.: Distributed deep learning model for intelligent video surveillance systems with edge computing. IEEE Trans. Industr. Inf. 1(1), 1–9 (2019)

    Google Scholar 

  6. Singh, A., Kumar, J.: Secure and energy aware load balancing framework for cloud data centre networks. Electron. Lett. 55(2), 540–541 (2019)

    Article  Google Scholar 

  7. D. Kirchoff, M. Xavier, J. Mastella, et al. A preliminary study of machine learning workload prediction techniques for cloud applications. Proceedings of Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP), 2019: 222–227.

  8. Bi, J., Li, S., Yuan, H., et al.: Integrated deep learning method for workload and resource prediction in cloud systems. Neurocomputing 424(1), 35–48 (2021)

    Article  Google Scholar 

  9. Maryam, C., Faramarz, S.: ARIMA: predictive consolidation of virtual machines applying ARIMA method. J. Supercomput. 77(3), 2172–2206 (2021)

    Article  Google Scholar 

  10. Dhaval, B., Deshpande, A.: Short-term load forecasting using method of multiple linear regression. New Approaches Eng. Research 14(1), 67–77 (2021)

    Article  Google Scholar 

  11. Kaur, M., Kaur, P., Sood, S.: Energy efficient IoT-based cloud framework for early flood prediction. Nat. Hazards 109(3), 2053–2076 (2021)

    Article  Google Scholar 

  12. Calheiros, R., Masoumi, E., Ranjan, R., et al.: Workload prediction using ARIMA model and its impact on cloud applications’ QoS. IEEE Trans. Cloud Comput. 3(4), 449–458 (2014)

    Article  Google Scholar 

  13. Sanchez, R., Juan, F.: Structural combination of seasonal exponential smoothing forecasts applied to load forecasting. Eur. J. Oper. Res. 275(3), 916–924 (2019)

    Article  MathSciNet  MATH  Google Scholar 

  14. Tan, M., Wong, S., Xu, J., et al.: An aggregation approach to short-term traffic flow prediction. IEEE Trans. Intell. Transp. Syst. 10(1), 60–69 (2009)

    Article  Google Scholar 

  15. Peng, H., Wen, W., Tseng, M., et al.: A cloud load forecasting model with nonlinear changes using whale optimization algorithm hybrid strategy. Soft. Comput. 25(15), 10205–10220 (2021)

    Article  Google Scholar 

  16. Lang, K., Zhao, Y.: Cloud computing resource scheduling based on improved ANN model takeaway order volume forecast. J. Intell. Fuzzy Syst. 40(4), 5905–5915 (2021)

    Article  Google Scholar 

  17. Gong, G., An, X., Mahato, N., et al.: Research on short-term load prediction based on Seq2seq model. Energies 12(16), 3199–3216 (2019)

    Article  Google Scholar 

  18. D. Janardhanan, E. Barrett. CPU workload forecasting of machines in data centers using LSTM recurrent neural networks and ARIMA models. Proc. International Conference for Internet Technology and Secured Transactions, 2017: 55–60.

  19. Fan, J., Zhang, K., Huang, Y., et al.: Parallel spatio-temporal attention-based TCN for multivariate time series prediction. Neural Comput. Appl. 1(1), 1–10 (2021)

    Google Scholar 

  20. Kumar, J., Singh, A., Buyya, R.: Self directed learning based workload forecasting model for cloud resource management. Inf. Sci. 543(1), 345–366 (2021)

    Article  Google Scholar 

  21. Kumar, J., Singh, A., Buyya, R.: Ensemble learning based predictive framework for virtual machine resource request prediction. Neurocomputing 397(1), 20–30 (2020)

    Article  Google Scholar 

  22. Saxena, D., Singh, A.: A proactive autoscaling and energy-efficient VM allocation framework using online multi-resource neural network for cloud data center. Neurocomputing 426(1), 248–264 (2021)

    Article  Google Scholar 

  23. Kumar, J., Singh, A.: Decomposition based cloud resource demand prediction using extreme learning machines. J. Netw. Syst. Manage. 28(4), 1775–1793 (2020)

    Article  Google Scholar 

  24. Bi, J., Yuan, H., Zhou, M.: Temporal prediction of multiapplication consolidated workloads in distributed clouds. IEEE Trans. Autom. Sci. Eng. 16(4), 1763–1773 (2019)

    Article  Google Scholar 

  25. Chen, L., Zhang, W., Ye, H.: Accurate workload prediction for edge data centers: Savitzky-Golay filter, CNN and BiLSTM with attention mechanism. Appl. Intell. 1(1), 1–16 (2022)

    Article  Google Scholar 

  26. Ma, Q., Wang, H., Luo, P., et al.: Ultra-short-term railway traction load prediction based on DWT-TCN-PSO_SVR combined model. Int. J. Electr. Power Energy Syst. 135(1), 107595–107605 (2022)

    Article  Google Scholar 

  27. Wang, D., Li, M.: Stochastic configuration networks: fundamentals and algorithms. IEEE Trans. Cybern. 47(10), 3466–3479 (2017)

    Article  Google Scholar 

  28. Bi, J., Yuan, H., Zhang, L., et al.: Sgw-scn: an integrated machine learning approach for workload forecasting in geo-distributed cloud data centers. Inf. Sci. 481(1), 57–68 (2019)

    Article  MathSciNet  Google Scholar 

  29. Altan, A., Karasu, S., Zio, E.: A new hybrid model for wind speed forecasting combining long short-term memory neural network, decomposition methods and grey wolf optimizer. Appl. Soft Comput. 100(1), 106996–107016 (2021)

    Article  Google Scholar 

  30. Singh, V., Pandey, M., Singh, P., et al.: An empirical mode decomposition (EMD) enabled long sort term memory (LSTM) based time series forecasting framework for web services recommendation. Fuzzy systems and data mining V. IOS Press 1(1), 715–723 (2019)

    Google Scholar 

  31. Zhang, C., Wei, H., Zhao, J., et al.: Short-term wind speed forecasting using empirical mode decomposition and feature selection. Renew. Energy 96(1), 727–737 (2016)

    Article  Google Scholar 

  32. Li, H., Wang, J., Lu, H., et al.: Research and application of a combined model based on variable weight for short term wind speed forecasting. Renew. Energy 116(1), 669684–669720 (2018)

    Google Scholar 

  33. Xie, Y., Jin, M., Xu, G., et al.: Real-time prediction of docker container resource load based on a hybrid model of ARIMA and triple exponential smoothing. IEEE Trans. Cloud Comput. 1(1), 1–17 (2020)

    Google Scholar 

  34. Lang, X., Du, G., Zhu, Z., et al.: Median ensemble empirical mode decomposition. Signal Process. 176(1), 107686–107694 (2020)

    Article  Google Scholar 

  35. Wang, J., Du, G., Zhu, Z., et al.: Fault diagnosis of rotating machines based on the EMD manifold. Mech. Syst. Signal Process. 135(1), 106443–106464 (2020)

    Article  Google Scholar 

  36. Jiang, F., Zhu, Z., Li, W.: An improved VMD with empirical mode decomposition and its application in incipient fault detection of rolling bearing. IEEE Access 6(1), 44483–44493 (2018)

    Article  Google Scholar 

  37. Huang, F., Li, X., Yuan, C., et al.: Attention-emotion-enhanced convolutional LSTM for sentiment analysis. IEEE Trans. Neural Netw. Learn. Syst. 1(1), 1–14 (2021)

    Google Scholar 

  38. Arlitt, M., Williamson, C.: Web server workload characterization: the search for invariants. ACM Sigmetrics Perform. Eval. Rev. 24(1), 126–137 (1996)

    Article  Google Scholar 

  39. Kumar, J., Singh, A.: Workload prediction in cloud using artificial neural network and adaptive differential evolution. Futur. Gener. Comput. Syst. 81(1), 41–52 (2018)

    Article  Google Scholar 

Download references

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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  • DOI: https://doi.org/10.1007/s11760-022-02424-8

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