P324：カーボン対応スケジューリング：Google Cloud研究
P324: Carbon-aware scheduling: Google Cloud study (原題)
Sonu Kumar Singh
🤖 gxceed AI 要約
日本語
本稿はGoogle Cloudにおけるカーボン対応スケジューリングの設計・ガバナンス・評価方法を検討する。クラウド効率をコストと資源消費の両面から捉え、アーキテクチャ選択が挙動・信頼性・コストに与える影響を整理する。デザインサイエンス手法に基づき意思決定フレームワークと検証計画を提示するが、実証実験は未実施で、測定すべき指標を明示するに留まる。
English
This monograph examines how carbon-aware scheduling should be designed, governed, and empirically evaluated for Google Cloud. It frames cloud efficiency as cost and resource consumption per successful workload under service-level constraints, connecting architecture choices to governance, reliability, and cost. Using design-science prototyping, it offers a decision framework and validation plan, but no experiments have yet been run.
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
日本企業のクラウド利用拡大とScope 2・Scope 3算定の精緻化が進む中、カーボン対応スケジューリングはデータセンター由来排出の削減手段として注目される。SSBJや有報でのGHG開示と整合させるには、クラウド事業者の排出係数やワークロード単位の証跡設計が論点となる。
In the global GX context
As CSRD, ISSB, and SEC climate rules push companies to disclose cloud-related Scope 2/3 emissions, carbon-aware scheduling offers an operational lever for reducing data-center footprints. This paper's governance and evidence framework speaks to the disclosure-infrastructure gap between workload-level scheduling decisions and corporate GHG reporting.
👥 読者別の含意
🔬研究者:Provides a design-science template for evaluating carbon-aware scheduling, though empirical validation remains future work.
🏢実務担当者:Offers a decision framework for aligning cloud architecture choices with carbon and cost governance, useful for FinOps and sustainability teams.
🏛政策担当者:Highlights the need for measurable, auditable evidence standards for cloud carbon accounting in disclosure regimes.
📄 Abstract（原文）
Carbon-aware scheduling: Google Cloud study Author: Sonu Kumar Singh (Senior Consultant — Cloud & AI Solutions Architecture, Capgemini US LLC) Professional Credential: Member, IEEE (Membership # 102728576) | ORCID: 0009-0002-9180-4946 Abstract Cloud efficiency must be measured as cost and resource consumption per successful workload outcome under explicit service-level constraints rather than as isolated list prices or peak benchmark scores. Against this backdrop, the paper examines carbon-aware scheduling in Google Cloud. It asks a focused question: How should carbon-aware scheduling be designed, governed, and empirically evaluated for Google Cloud? To keep the discussion testable, carbon-aware scheduling is defined in operational terms. The paper focuses on the points where architecture choices become visible in behavior—how data is represented, how policies are enforced, how workloads fail and recover, and what evidence is left behind. That boundary is intentionally narrower than a feature survey and broad enough to capture the system-level trade-offs. The paper contributes a decision framework and a validation plan. It connects architecture to governance, reliability, cost, and measurable evidence, and it uses design-science prototype and validation as the primary research method. Where no experiment has been run, the paper says so directly and specifies what would have to be measured before an empirical conclusion could be defended. Architectural Research Scope Research Domain / Theme: FinOps, Performance & Sustainability Architectural Scope: Google Cloud Core Research Question: How should carbon-aware scheduling be designed, governed, and empirically evaluated for Google Cloud? Specification Standard: Full 20-page peer-level monograph featuring system topology diagrams, 7 empirical benchmark tables, and failure-mode analyses. Published as part of the Cloud, AI, and Distributed Data Systems: 500-Monograph Engineering Corpus.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.5281/zenodo.23109863first seen 2026-10-04 04:48:03 · last seen 2026-10-04 04:48:21
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