ESTIMATION OF POWER CONSUMPTION OF MOBILE DEVICES IN CLOUD COMPUTING

Abstract

Modern computing tasks require an increase in computing power. This necessitates the creation and production of new equipment for cloud computing. At the same time, the number of personal mobile devices is already measured in billions, and even their partial use could reduce production requirements. In addition, mobile hardware is more energy efficient, which contributes to significant energy savings. The article investigates the issue of qualitative and quantitative assessment of the efficiency of using mobile devices for computing compared to traditional stationary solutions. The purpose of the work is to substantiate the following hypothesis: computing in the cloud based on mobile devices significantly reduces energy consumption than computing on stationary equipment. For this purpose, we show that computing on a mobile GPU is more energy efficient than computing on a stationary processor. Public sources and benchmarks were analyzed to determine the qualitative advantage. On the basis of the studied data, efficiency indicators for various mobile and desktop GPUs are calculated. It is argued that in most cases, mobile solutions consume significantly less energy compared to desktop solutions. To calculate the quantitative advantage, an experiment was conducted on the basis of two platforms: mobile and desktop. The same computational task was implemented using Apple Metal and NVidia CUDA. Based on this task, the energy efficiency indicators of the mobile and stationary graphic professor were calculated. According to the results of the study, a significant advantage of the mobile GPU in terms of energy efficiency has been determined. This result is relevant because the platforms were released in the same year with a difference of several months, so they can be considered peers of each other. The approaches presented here do not take into account the consumption of all other parts of the system, except for the GPUs. This means that the consumption of the motherboard, power supply, etc. can tilt the balance in favor of the mobile processor even more. But for distributed computing, the network connection is very important, and it can consume a significant amount of power on a mobile device. Further research will focus on a more comprehensive accounting of the energy consumption of various computer subsystems.