AI対応クラウドインフラのエネルギー・炭素フットプリント削減のための運用戦略
Operational Strategies for Reducing the Energy and Carbon Footprint of AI-Enabled Cloud Infrastructure (原題)
Atul Khanna, Abhishek Shukla
🤖 gxceed AI 要約
日本語
AIワークロードの増大に伴い、データセンターの電力需要と炭素排出が急増する。本論文はハードウェア設計ではなく、運用視点から、ワークロード配置、エネルギー・炭素認識スケジューリング、適正化、インシデント管理、ガバナンス等の運用プラクティスが環境影響を大幅に削減できると論じる。企業の持続可能性戦略に示唆を与える。
English
As AI workloads grow, data center electricity demand and carbon emissions rise sharply. This paper argues from an operations perspective—not hardware design—that workload placement, energy- and carbon-aware scheduling, right-sizing, incident management, and governance can materially reduce environmental impact. Offers practical insights for corporate sustainability strategies.
Unofficial AI-generated summary based on the public title and abstract. Not an official translation.
📝 gxceed 編集解説 — Why this matters
日本のGX文脈において
日本ではデータセンターの電力消費増加が課題。GX投資促進策や省エネ法の観点から、運用効率化は企業の脱炭素戦略に直結する。SSBJ開示でのエネルギー使用量・排出量削減にも寄与。
In the global GX context
Globally, data center energy demand is a growing concern for climate targets. This paper provides operational strategies that complement hardware innovations, relevant for companies facing CSRD, SEC climate rules, and investor pressure to reduce Scope 2 emissions.
👥 読者別の含意
🔬研究者:Provides a framework for operational interventions in AI infrastructure that can inform future research on carbon-aware computing.
🏢実務担当者:Offers actionable operational practices to reduce energy and carbon footprint of cloud infrastructure, aiding sustainability reporting and cost savings.
🏛政策担当者:Highlights the role of operational efficiency in mitigating AI's energy demand, relevant for energy policy and grid planning.
📄 Abstract（原文）
As workloads in artificial intelligence continue to grow in scale, demand for electricity in data centers and cloud infrastructure is increasing, together with associated carbon emissions and water consumption. Recent studies by organizations such as the International Energy Agency and MIT indicate that AI-related electricity demand may rise sharply through 2030, creating important implications for energy systems and corporate sustainability strategies. This paper examines those implications from a cloud operations perspective rather than from the standpoint of hardware design or model research. The analysis argues that operational practices such as workload placement, energy-aware and carbon-aware scheduling, architectural right-sizing, stronger incident and capacity management, and cross-functional governance can materially reduce the environmental impact of AI-enabled infrastructure.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.63412/zvwmpz36first seen 2026-09-02 04:56:09
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