Ichnos+: Estimating the Carbon Footprint of Scientific Workflows Using Fitted Power Models
Ichnos+: Fitted Power Modelsを用いた科学ワークフローのカーボンフットプリント推定 (AI 翻訳)
Kathleen West, Youssef Moawad, Philipp Thamm, Vasilis Bountris, Giulio Attenni, Magnus Reid, Yehia Elkhatib, Lauritz Thamsen
🤖 gxceed AI 要約
日本語
本論文は、適合電力モデルを用いて科学ワークフローのカーボンフットプリントを推定するシステムIchnos+を提案する。推定誤差10.8%を達成し、内包排出や水・土地利用にも拡張可能である。
English
Ichnos+ is a novel system for estimating the carbon footprint of scientific workflows, using fitted power models based on node-specific data. It achieves a 10.8% estimation error across three clusters, outperforming existing plugins. The system also estimates embodied emissions, water, and land use, and can be extended to other workflow systems like Apache Airflow.
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
日本の研究機関や企業が運用する大規模計算環境（スパコン、AIトレーニング等）のカーボンフットプリント測定に活用可能。SSBJやTCFDに基づく情報開示において、ICT分野の排出量算定手法として有用。
In the global GX context
Globally, this paper provides a practical tool for quantifying the carbon footprint of scientific workflows, relevant for ISSB and CSRD disclosure. It extends beyond operational carbon to include embodied emissions and other environmental impacts, addressing the full scope of climate disclosure.
👥 読者別の含意
🔬研究者:Researchers in sustainable computing can adopt Ichnos+ to quantify and optimize the environmental impact of their workflows.
🏢実務担当者:Practitioners managing data centers or cloud infrastructure can use Ichnos+ to report carbon emissions from computational workloads for sustainability reporting.
📄 Abstract（原文）
As data-intensive scientific workflows scale to facilitate the automation of analysis of increasing amounts of data, their resource-intensive and long-running execution incurs significant energy consumption and carbon emissions. Given the already significant and rising emissions from the ICT sector, it is crucial to quantify and understand the carbon footprint of scientific workflows. However, existing tooling is commonly not usable in shared, virtualized environments or resorts to power models that are based on only one or two generic data points. To address this gap, this paper presents Ichnos+, a novel system to quantify the environmental footprint of Nextflow scientific workflows. Ichnos+ enables post-hoc footprint estimation based on existing workflow traces, node-specific power models for the computational resources utilized, and carbon intensity data aligned with the execution time. We evaluate Ichnos+ against hardware-level energy measurements obtained using Intel RAPL, and the nf-core co2footprint plugin, which implements the Green Algorithms methodology. We find that Ichnos+ is capable of estimating workflow energy consumption with an estimation error of 10.8% across three compute clusters, significantly outperforming the nf-core plugin. We further show that Ichnos+ extends beyond operational carbon to estimate embodied emissions as well as water and land use. Finally, we demonstrate how Ichnos+ can be extended for another workflow system, Apache Airflow, maintaining a similarly high degree of estimation accuracy.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.48550/arxiv.2607.10586first seen 2026-07-16 05:29:18
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