High-Performance Computing in Edge Computing Networks

Wanqing Tu, Florin Pop, Weijia Jia, Jie Wu, Mauro Iacono

Abstract: Establishing an accurate edge server power model is helpful for resource providers to predict and optimize power consumption within edge data centers. Considering the fact that the accuracy of the previous energy consumption model is easily affected by the workload types, this paper develops an edge server power model based on BP (back propagation) neural network and feature selection, which is denoted by DSBF. For different task types, DSBF leverages “principal component analysis (PCA)” to analyze the contribution of each energy consumption parameter and selects “representative parameter”, and then builds a power model based on BP neural network. In contrast to other power models, DSBF can effectively handle the variable workload. To measure the effectiveness of the DSBF model, a series of experiments were conducted. The results suggest that compared with other energy consumption models, DSBF can better adapt to the changing workload and has advantages in predicting the accuracy of the energy consumption model.