ECDX: Energy consumption prediction model based on distance correlation and XGBoost for edge data center
ECDX: Energy consumption prediction model based on distance correlation and XGBoost for edge data center
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DOI：
10.1016/j.ins.2023.119218
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发表时间：
2023-05
期刊：
影响因子：
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通讯作者：
C. Li;Dan Zhu;Chunhua Hu;Xiaolong Li;Suqin Nan;Hua Huang
中科院分区：
文献类型：
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作者：
C. Li;Dan Zhu;Chunhua Hu;Xiaolong Li;Suqin Nan;Hua Huang
人工智能（AI）和边缘计算技术的快速发展，推动了数据中心数量的快速增长，但也造成了大量的能源消耗。如何准确预测服务器的能耗对优化数据中心至关重要。因此，提出了一种集成距离相关和极端梯度提升（XGBoost）的实时服务器能耗预测模型ECDX。首先，采用距离相关系数法过滤关键特征参数，去除冗余特征;其次，采用交叉验证方法优化超参数。之后，使用XGBoost算法构建数据中心服务器能耗预测模型。大量的实验已经进行了比较的ECDX模型与基准模型的性能。结果表明，ECDX能够适应工作量的变化，预测精度显著提高，平均相对误差降低了4.698%.
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%.