Thermal prediction for energy management of clouds using a hybrid model based on CNN and stacking multi-layer bi-directional LSTM

Abstract

The fast advancement of technology and developers’ utilization of data centers have dramatically increased energy usage in today’s society. Thermal control is a key issue in hyper-scale cloud data centers. Hotspots form when the temperature of the host rises, increasing cooling costs and affecting dependability. Precise estimation of host temperatures is critical for optimal resource management. Thermal changes in the data center make estimating temperature a difficult challenge. Existing temperature estimating algorithms are ineffective due to their processing complexity as well as lack of accuracy. Regarding that data-driven approaches seem promising for temperature prediction, this research offers a unique efficient temperature prediction model. The model uses a combination of convolutional neural networks (CNN) and stacking multi-layer bi-directional long-term short memory (BiLSTM) for thermal prediction. The findings of the experiments reveal that the model successfully anticipates the temperature with the highest R2 value of 97.15% and the lowest error rate of RMSE value of 0.2892, and an RMAE of 0.5003, which decreases the projection error as opposed to the other method.