Adaptive Prediction of Resources and Workloads for Cloud Computing Systems with Attention-based and Hybrid LSTM-Reference-Cited by-同舟云学术

Adaptive Prediction of Resources and Workloads for Cloud Computing Systems with Attention-based and Hybrid LSTM

Published:2022-10-09 Issue: Volume: Page:
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Container-title:2022 IEEE International Conference on Systems, Man, and Cybernetics (SMC)
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Author:

Bi Jing¹,Ma Haisen¹,Yuan Haitao²,Xu Kangyuan¹,Zhou MengChu³

Affiliation:

1. Beijing University of Technology,Faculty of Information Technology,Beijing,China,100124

2. Beihang University,School of Automation Science and Electrical Engineering,Beijing,China,100191

3. New Jersey Institute of Technology,Department of Electrical and Computer Engineering,Newark,USA,07102

Funder

National Natural Science Foundation of China

Fundamental Research Funds for the Central Universities

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9945068/9945069/09945419.pdf?arnumber=9945419

Reference21 articles.

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4. Smoothing and Differentiation of Data by Simplified Least Squares Procedures.

5. A Discrete Curvature Estimation Based Low-Distortion Adaptive Savitzky–Golay Filter for ECG Denoising

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