Towards a Systematic Survey for Carbon Neutral Data Centers

• URL: http://arxiv.org/abs/2110.09284v3
• Date: Fri, 28 Jan 2022 03:26:42 GMT
• Title: Towards a Systematic Survey for Carbon Neutral Data Centers
• Authors: Zhiwei Cao, Xin Zhou, Han Hu, Zhi Wang, Yonggang Wen
• Abstract summary: Data centers are carbon-intensive enterprises due to their massive energy consumption. It is estimated that data center industry will account for 8% of global carbon emissions by 2030. This survey paper proposes a roadmap towards carbon-neutral data centers that takes into account both policy instruments and technological methodologies.
• Score: 23.339102377319833
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Data centers are carbon-intensive enterprises due to their massive energy consumption, and it is estimated that data center industry will account for 8\% of global carbon emissions by 2030. However, both technological and policy instruments for reducing or even neutralizing data center carbon emissions have not been thoroughly investigated. To bridge this gap, this survey paper proposes a roadmap towards carbon-neutral data centers that takes into account both policy instruments and technological methodologies. We begin by presenting the carbon footprint of data centers, as well as some insights into the major sources of carbon emissions. Following that, carbon neutrality plans for major global cloud providers are discussed to summarize current industrial efforts in this direction. In what follows, we introduce the carbon market as a policy instrument to explain how to offset data center carbon emissions in a cost-efficient manner. On the technological front, we propose achieving carbon-neutral data centers by increasing renewable energy penetration, improving energy efficiency, and boosting energy circulation simultaneously. A comprehensive review of existing technologies on these three topics is elaborated subsequently. Based on this, a multi-pronged approach towards carbon neutrality is envisioned and a digital twin-powered industrial artificial intelligence (AI) framework is proposed to make this solution a reality. Furthermore, three key scientific challenges for putting such a framework in place are discussed. Finally, several applications for this framework are presented to demonstrate its enormous potential.

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