To evaluate energy use in green clusters, power models take the resource utilization data as the input to predict server power consumption. We propose a novel method in power modeling combining a global linear model and a local approximation model. The new model enjoys high accuracy by compensating the global linear model with local approximation and exhibits robustness with the generalization capability of the global regression model. Empirical evaluation demonstrates that the new approach outperforms the two existing approaches to server power modeling, the linear model and the k-nearest neighbor regression model.

中文翻译：

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在服务器电源建模中将全局回归与局部逼近相结合

为了评估绿色集群中的能源使用，功率模型将资源利用数据作为输入来预测服务器功耗。我们提出了一种将全局线性模型和局部逼近模型相结合的功率建模新方法。新模型通过用局部逼近补偿全局线性模型而享有较高的准确性，并且具有全局回归模型的泛化能力，因此显示出鲁棒性。实证评估表明，新方法优于服务器功率建模的两个现有方法，即线性模型和k最近邻回归模型。