Geographical Server Relocation: Opportunities and Challenges

The enormous growth of AI computing has led to a surging demand for electricity. To stem the resulting energy cost and environmental impact, this paper explores opportunities enabled by the increasing hardware heterogeneity and introduces the concept of Geographical Server Relocation (GSR). Specifically, GSR physically balances the available AI servers across geographically distributed data centers subject to AI computing demand and power capacity constraints in each location. The key idea of GSR is to relocate older and less energy-efficient servers to regions with more renewables, better water efficiencies and/or lower electricity prices. Our case study demonstrates that, even with modest flexibility of relocation, GSR can substantially reduce the total operational environmental footprints and operation costs of AI computing. We conclude this paper by discussing major challenges of GSR, including service migration, software management, and algorithms.

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Publication Details

Journal
ACM SIGEnergy Energy Informatics Review
Published
2024-12-01
DOI
https://doi.org/10.1145/3727200.3727207
Citations
2
Primary Topic
Cloud Computing and Resource Management
Type
article
Field-Weighted Citation Impact
1.56
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article

Geographical Server Relocation: Opportunities and Challenges

Daniel Wong, Shaolei Ren, Pengfei Li, Yejia Liu
2 citations
ACM SIGEnergy Energy Informatics Review
Cloud Computing and Resource Management
1.56
article

Geographical Server Relocation: Opportunities and Challenges

Daniel Wong, Shaolei Ren, Pengfei Li, Yejia Liu
article en
2 citations

Abstract

The enormous growth of AI computing has led to a surging demand for electricity. To stem the resulting energy cost and environmental impact, this paper explores opportunities enabled by the increasing hardware heterogeneity and introduces the concept of Geographical Server Relocation (GSR). Specifically, GSR physically balances the available AI servers across geographically distributed data centers subject to AI computing demand and power capacity constraints in each location. The key idea of GSR is to relocate older and less energy-efficient servers to regions with more renewables, better water efficiencies and/or lower electricity prices. Our case study demonstrates that, even with modest flexibility of relocation, GSR can substantially reduce the total operational environmental footprints and operation costs of AI computing. We conclude this paper by discussing major challenges of GSR, including service migration, software management, and algorithms.

ACM SIGEnergy Energy Informatics ReviewVol. 4(5)
University of California, Riverside (US)
Openalex Percentile: Top 11%
Cloud Computing and Resource Management
1.56
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Geographical Server Relocation: Opportunities and Challenges — Daniel Wong, Shaolei Ren, et al. · ACM SIGEnergy Energy Informatics Review (2024) | TGRS Research Map | TGRS