Green Cloud Computing for the Metaverse: Powering the Future of the Internet with Renewable Energy

The emergence of the Metaverse is expected to significantly increase the energy demands on cloud computing infrastructure and data centres. Data centres heavily rely heavily on non-renewable energy sources like fossil fuels, resulting in high carbon emissions. This paper explores Green Cloud Computing (GCC) techniques to enable sustainable, energy-efficient cloud architecture for metaverse platforms. A multifaceted model is proposed, encompassing smart grid deployment, on-site renewable energy integration, energy storage solutions and real-time analytics. Specific strategies include advanced metering infrastructure, microgrids, solar panels, wind turbines, batteries, compressed air, and thermal storage. The model aims to minimise fossil fuel dependence and carbon emissions by transitioning to renewable energy sources. Smart grid optimisation can potentially maximise the utilisation of green power by 10-15%. On-site solar may provide 20-60% of data center energy needs, and wind turbines over 25%. Storage solutions, including batteries, compressed air and thermal, can meet 10-25% of demand fluctuations. Optimising workloads based on renewable availability can reduce non-renewable energy by 20-40%. The model has the potential to enable over 90% renewable energy usage for metaverse data centres, compared to the current 20% levels. The paper provides a framework and analysis of GCC techniques designed with the large-scale, energy-intensive infrastructure requirements of developing metaverse ecosystems in mind. The proposed strategies focus on applying advanced metering infrastructure, microgrids, various on-site generation solutions, and real-time analytics to create a dynamic, optimised and responsive energy supply tailored to the unique demands of metaverse workloads. Outlining a projected sustainability impact provides actionable insights into greening the foundational cloud architecture to power immersive virtual worlds.