Much of the world’s data are stored, managed, and distributed by data centers. Data centers require a tremendous amount of energy to operate, accounting for around 1.8% of electricity use in the United States. Large amounts of water are also required to operate data centers, both directly for liquid cooling and indirectly to produce electricity. For the first time, we calculate spatially-detailed carbon and water footprints of data centers operating within the United States, which is home to around one-quarter of all data center servers globally. Our bottom-up approach reveals one-fifth of data center servers direct water footprint comes from moderately to highly water stressed watersheds, while nearly half of servers are fully or partially powered by power plants located within water stressed regions. Approximately 0.5% of total US greenhouse gas emissions are attributed to data centers. We investigate tradeoffs and synergies between data center’s water and energy utilization by strategically locating data centers in areas of the country that will minimize one or more environmental footprints. Our study quantifies the environmental implications behind our data creation and storage and shows a path to decrease the environmental footprint of our increasing digital footprint.
This content will become publicly available on May 31, 2025
Towards Environmentally Equitable AI via Geographical Load Balancing
Fueled by the soaring popularity of foundation models, the accelerated growth of artificial intelligence (AI) models’ enormous environmental footprint has come under increased scrutiny. While many approaches have been proposed to make AI more energy-efficient and environmentally friendly, environmental inequity — the fact that AI’s environmental footprint can be disproportionately higher in certain regions than in others — has emerged, raising social-ecological justice concerns. This paper takes a first step toward addressing AI’s environmental inequity by fairly balancing its regional environmental impact. Concretely, we focus on the carbon and water footprints of AI model inference and propose equity-aware geographical load balancing (eGLB) to explicitly minimize AI’s highest environmental cost across all the regions. The consideration of environmental equity creates substantial algorithmic challenges as the optimal GLB decisions require complete offline information that is lacking practice. To address the challenges, we introduce auxiliary variables and optimize GLB decisions online based on dual mirror descent. In addition to analyzing the performance of eGLB theoretically, we run trace-based empirical simulations by considering a set of geographically distributed data centers that serve inference requests for a large language AI model. The results demonstrate that existing GLB approaches may amplify environmental inequity while eGLB can significantly reduce the regional disparity in terms of carbon and water footprints.
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- PAR ID:
- 10544908
- Publisher / Repository:
- ACM International Conference on Future and Sustainable Energy Systems (e-Energy) 2024
- Date Published:
- ISBN:
- 9798400704802
- Page Range / eLocation ID:
- 291 to 307
- Format(s):
- Medium: X
- Location:
- Singapore Singapore
- Sponsoring Org:
- National Science Foundation
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