A Prediction Model of Cloud Server Performance Based on Temporal Convolutional Network

Most of the current deep learning-based models for host performance prediction do not have universal applicability, cannot produce fair experimental data or accurately predict the energy consumption or performance peak point, and consume too much time. In order to solve these problems, an adaptive performance prediction mo-del based on Temporal Convolutional Network (ATCN Model) is proposed. In this model, CPU utilization is regarded as the measurement standard for host overload, N+1-dimension energy consumption vector is constructed with multi-dimensional performance index, and the relationship between the input vector and prediction standard is established. The convolution kernel size in TCN is adjusted and the expansion factor is increased continuously to achieve a long-term memory effect. The experimental results based on Alibaba cloud open-source data set show that the ATCN model has strong self-adaptability. Under different hardware configurations and resource usages, the prediction accuracy and efficiency are improved by about 20% compared with the LSTM prediction model.