Energy savings in processor based on prediction technique

Abstract

Green computing has become one of the hottest trends in recent years. In this research area, the major purpose is to reduce the energy consumption as well as the CO2 emission. Obviously, this topic has been the important issue in the field of electronic and computer engineering. In fact, energy factor might be considered to be a significant cost when running any computing system. Basically, energy savings can be obtained in many parts of the system including memory, peripheral devices, hard disk drive and processor. In processor or CPU level, there is a number of solutions to handle the power consumption. However, most of them based on reactive model which engages the thresholds. Obviously, these techniques are not accuracy and limited to save the power. In this research, a proactive solution based on prediction technique is proposed. Firstly, the utilization of each core of processor is anticipated by using Gaussian process regression. Subsequently, a migration mechanism can be used to migrate the system-level processes between these cores. Finally, the idle cores can be turned off to save the power while still maintaining an acceptable performance. © 2016 IEEE.