ECDX: Energy consumption prediction model based on distance correlation and XGBoost for edge data center

Abstract

The rapid development of artificial intelligence (AI) and edge computing technology has promoted the rapid growth of the number of data centers, but also caused large energy consumption. How to accurately predict the energy consumption of servers is crucial to optimize data centers. Thus, a real-time server energy consumption prediction model that integrates distance correlation and extreme gradient boosting (XGBoost) named ECDX is proposed. First, the distance correlation coefficient method is used to filter essential feature parameters and remove redundant features. Second, the cross-validation method is employed to optimize the hyperparameters. Thereafter, a data center server energy consumption prediction model is constructed using the XGBoost algorithm. Numerous experiments have been conducted to compare the performance of the ECDX model with that of benchmark models. The results show that ECDX can adapt to changes in workload, the prediction accuracy is significantly improved, and the average relative error is reduced by 4.698%.