Improved dynamic frequency-scaling approach for energy-saving-based radial basis function neural network

Abstract

As dynamic voltage and frequency scaling (DVFS) does not consider predicting system behaviour in the future stage, to improve efficiency of DVFS in fine-grained, the authors propose a central processing unit (CPU) utilisation prediction model based on radial basis function neural network. Their model first collects five typical system characteristics related to CPU utilisation during system running, then they use radial basis neural network to fit the non-linear relationship between these system characteristics and CPU utilisation in the next period. According to the predicted CPU utilisation, specific frequency scaling is performed to change frequency in real time. Finally, they evaluate their model with classical DVFS by means of different task sets. Experimental results show that their model could predict CPU utilisation in more fine-grained compared with other models, and changes frequency-scaling effect of traditional DVFS.

Keywords

• radial basis function networks
• power aware computing
• energy conservation
• frequency-scaling effect
• dynamic voltage and frequency-scaling approach
• classical dvfs
• cpu utilisation
• central processing unit utilisation prediction model
• energy-saving-based radial basis function neural network