Li-MSA: Power Consumption Prediction of Servers Based on Few-Shot Learning

Abstract

Power consumption prediction is one of the keys to optimize the energy consumption of servers. Existing traditional regression-based methods are too simple and poorly generalized, while popular deep learning methods require too much data. Therefore, they are difficult to be widely generalized. In this study, we propose a framework of linear interpolation multi-head sparse temporal pattern attention (Li-MSA) based on few-shot learning for power consumption prediction of servers with small-scale datasets in environments such as cloud data centers or edge computing. First, the interpolation reconstruction module extends and smooths the data. Then, the embedding learning module is used to narrow the scope of the hypothesis space. Finally, the multi-head sparse temporal pattern attention module emphasizes features and predicts power consumption. The results of the experiments show that Li-MSA outperforms the best results among the other methods for two datasets with different time steps in the RMSE metric by 15.34%, 17.35%, 18.18%, 6.28%, 4.05%, 7.73%.