Evaluation of non-linear power estimation models in a computing cluster

We prove that a multiple-variable linear regression approach is more precise than a CPU-only linear approach. The neural network approaches have a slight advantage - their mean root mean square error is at most 15% less than that of the multiple-variable linear model. The neural network models have worse portability when the models generated on a node are applied on other homogeneous nodes. Gaussian Mixture Model has the highest accuracy but requires the longest training time. In the end, we prove that models trained using the system-level full features have the highest accuracy comparing to only use part of features.