Residual Connection based TPA-LSTM Networks for Cluster Node CPU Load Prediction
Zhaojun Yang, Lan Chen, He Zhang, Zhenjie Yao · 2021 IEEE International Conference on Big Data (Big Data) · 2021
Accurate prediction for node CPU load is crucial for resource allocation in cluster. In this paper, we proposed a novel deep learning model named R-TPA-LSTM for the cluster node CPU load prediction. The proposed model is composed of two components, non-linear and linear component. The non-linear component contains residual LSTM-Conv module and attention module. Residual LSTM-Conv module includes two LSTM layers with residual connection and convolutional neural network for the sake of choosing the most informative timestep in the historical window while attention module captures the relationship among different features. The goal of the linear component, which is an AR module, is to catch the drastic changes in the data. The experimental results on a real-world dataset, show that the proposed model achieves better prediction performance for CPU load than conventional models.