Energy consumption and emission mitigation prediction based on data center traffic and PUE for global data centers

TLDR

Data centers, driven by big data and cloud computing, are rapidly expanding and are projected to become the world’s largest energy users, with consumption rising from 3 % in 2017 to 4.5 % in 2025, while the high‑latitude Pan‑Arctic region is increasingly favored for new sites due to its climate and energy‑saving advantages. This study introduces a method that predicts and analyzes future global data center energy consumption and carbon emissions using traffic growth and power usage effectiveness (PUE) data. The approach models traffic growth from Cisco research, derives dynamic global and high‑latitude PUE values via the Romonet simulation, and quantitatively compares decentralized and centralized scenarios through polynomial fitting. Simulations indicate that by 2030, a centralized Pan‑Arctic scenario could cut global data center energy use by about 301 billion kWh and CO₂ emissions by 720 million tons relative to a decentralized scenario, demonstrating the climate‑mitigation potential of Arctic data center deployment and supporting integrated energy‑information networks.

Abstract

With the rapid development of technologies such as big data and cloud computing, data communication and data computing in the form of exponential growth have led to a large amount of energy consumption in data centers. Globally, data centers will become the world’s largest users of energy consumption, with the ratio rising from 3% in 2017 to 4.5% in 2025. Due to its unique climate and energy-saving advantages, the high-latitude area in the Pan-Arctic region has gradually become a hotspot for data center site selection in recent years. In order to predict and analyze the future energy consumption and carbon emissions of global data centers, this paper presents a new method based on global data center traffic and power usage effectiveness (PUE) for energy consumption prediction. Firstly, global data center traffic growth is predicted based on the Cisco’s research. Secondly, the dynamic global average PUE and the high latitude PUE based on Romonet simulation model are obtained, and then global data center energy consumption with two different scenarios, the decentralized scenario and the centralized scenario, is analyzed quantitatively via the polynomial fitting method. The simulation results show that, in 2030, the global data center energy consumption and carbon emissions are reduced by about 301 billion kWh and 720 million tons CO2 in the centralized scenario compared with that of the decentralized scenario, which confirms that the establishment of data centers in the Pan-Arctic region in the future can effectively relief the climate change and energy problems. This study provides support for global energy consumption prediction, and guidance for the layout of future global data centers from the perspective of energy consumption. Moreover, it provides support of the feasibility of the integration of energy and information networks under the Global Energy Interconnection conception.