IT Equipment Automatic Configuration Method Based on Energy Consumption Prediction

IT equipment is the largest electricity consumer in data centers. However, existing IT equipment energy consumption prediction methods can only capture temporal dependencies between features and cannot uncover spatial dependencies between features. Furthermore, these methods cannot dynamically predict energy consumption based on the type of task, which leads to inaccurate predictions. To address these problems, this paper proposes an IT equipment energy consumption prediction method based on Long Short-Term Memory Network (LSTM) and Graph Convolutional Neural Network (GCN). In this method, the LSTM is first used to capture the temporal dependencies of IT equipment energy consumption features. And then a graph structure is constructed and the spatial dependencies between features are uncovered through GCN, and periodically captures the dynamic energy consumption patterns of IT equipment. Next, an attention module is used to weight the features for different importance levels, and the final energy consumption prediction is obtained. Experimental results show that the proposed energy consumption prediction method achieves MAPE of 1. 48% and RMSE of 1. 55 , which are much better than other existing methods. IT equipment can be configured and scheduled based on energy consumption prediction results, which can achieve energy saving and emission reduction in the data center.