E2LG: a multiscale ensemble of LSTM/GAN deep learning architecture for multistep-ahead cloud workload prediction
Author:
Publisher
Springer Science and Business Media LLC
Subject
Hardware and Architecture,Information Systems,Theoretical Computer Science,Software
Link
https://link.springer.com/content/pdf/10.1007/s11227-021-03723-6.pdf
Reference41 articles.
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2. Zhang Q, Yang LT, Yan Z, Chen Z, Li P (2018) An efficient deep learning model to predict cloud workload for industry informatics. IEEE Trans Ind Inform 14:3170–3178. https://doi.org/10.1109/TII.2018.2808910
3. Mozo A, Ordozgoiti B, Gómez-Canaval S (2018) Forecasting short-term data center network traffic load with convolutional neural networks. PLoS ONE. https://doi.org/10.1371/journal.pone.0191939
4. Kumar J, Singh AK, Buyya R (2020) Ensemble learning based predictive framework for virtual machine resource request prediction. Neurocomputing. https://doi.org/10.1016/j.neucom.2020.02.014
5. Jeddi S, Sharifian S (2020) A hybrid wavelet decomposer and GMDH-ELM ensemble model for network function virtualization workload forecasting in cloud computing. Appl Soft Comput J. https://doi.org/10.1016/j.asoc.2019.105940
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