Application Load prediction model based on SA-IPSO-BiLSTM

Xinghu Jin; Yanhong Liu; Shaopei Ji; Weibing Zhu; Liang Jin; Xinyu Ming

doi:10.1117/12.3004203

11 October 2023 Application Load prediction model based on SA-IPSO-BiLSTM

Xinghu Jin, Yanhong Liu, Shaopei Ji, Weibing Zhu, Liang Jin, Xinyu Ming

Proceedings Volume 12800, Sixth International Conference on Computer Information Science and Application Technology (CISAT 2023); 1280060 (2023) https://doi.org/10.1117/12.3004203
Event: 6th International Conference on Computer Information Science and Application Technology (CISAT 2023), 2023, Hangzhou, China

Abstract

In order to solve the problem of low accuracy in application load prediction, this paper proposes an application load prediction model based on SA-IPSO-BiLSTM. Firstly, the model puts the data into the BiLSTM neural network for training and uses the adaptive algorithms to automatically adjust the parameters of the BiLSTM neural network. Then, an improved particle swarm optimization algorithm is used to optimize the parameters of the BiLSTM neural network. Finally, the optimized BiLSTM is used for the application load prediction. Comparison with the existed prediction models, the result demonstrates that the SA-IPSO-BiLSTM model has a higher accuracy and strong applicability in application load prediction.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Xinghu Jin, Yanhong Liu, Shaopei Ji, Weibing Zhu, Liang Jin, and Xinyu Ming "Application Load prediction model based on SA-IPSO-BiLSTM", Proc. SPIE 12800, Sixth International Conference on Computer Information Science and Application Technology (CISAT 2023), 1280060 (11 October 2023); https://doi.org/10.1117/12.3004203

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