An Adaptive Time Series Prediction Framework for IoT-Enabled Green Data Center Networks

The consumption of energy by data center networks has increased markedly in recent times, becoming a matter of concern for all sections of society. It is essential to develop comprehensive models and optimize energy consumption in data centers. As the Internet of Things (IoT) industry experiences accelerated growth, predictive modeling of data centers utilizing IoT devices to gather real-time data and incorporating datadriven deep learning techniques has demonstrated an enhanced degree of accuracy. The functionality of existing deep learning models is limited in real data center scenarios, where concept drift is a common occurrence. This results in a reduction in the predictive accuracy of the models. In this paper, we propose a time series prediction framework for data center scenarios. Under this framework, online prediction and offline training can be implemented to effectively address the frequent concept drift phenomenon in data center scenarios and enhance the stability of prediction. Extensive experiments are conducted in this study using real datasets obtained from IoT device measurements. The experiment results show that the proposed time series prediction framework can achieve good performance in data center scenarios when facing the concept drift.