Ichnos+：使用拟合功率模型估算科学工作流的碳足迹

Jul 16, 2026

Upload by Riley

As data-intensive scientific workflows grow in scale and automation, their long-running execution drives substantial energy use and carbon emissions. With ICT emissions already rising, quantifying scientific workflow carbon footprints becomes essential. Existing tools often fail in shared virtualized environments or rely on power models built from only one or two generic data points. Ichnos+ provides post-hoc footprint estimation for Nextflow using workflow traces, node-specific power models, and time-aligned carbon intensity.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages12

Reading Time30 min

Abstract

Introduction

Motivation and problem with existing tooling

Carbon intensity and energy-to-carbon translation

Ichnos+ approach and evaluation overview

Ichnos+: Estimating the Carbon Footprint of Scientific Workflows Using Fitted Power Models Kathleen West∗ , Youssef Moawad∗ , Philipp Thamm†, Vasilis Bountris†, Giulio Attenni‡, Magnus Reid∗ , Yehia Elkhatib∗ , Lauritz Thamsen∗ ∗ University of Glasgow, United Kingdom † Humboldt-Universität zu Berlin, Germany ‡ Sapienza University of Rome, Italy arXiv :2607 . 10586v1 [ cs .DC] 12 Jul 2026 Abstract—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. Index Terms—scientific workflows, cluster computing, carbon footprint, energy estimation, sustainable computing I. INTRODUCTION Scientists in many fields, including genomics, materials science, and remote sensing, need to analyze increasing amounts of data [1]–[4] . Scientific workflow systems facilitate the automation of such analyzes, enabling scientists to compose pipelines out of black-box tasks with data dependencies between them. Because these workflows are often used to process large quantities of data, they tend to be resource-intensive and long-running, leading to significant energy consumption and, therefore, carbon emissions. Indeed, the growing popularity of big data applications has been identified as a driver of the increasing emissions of the ICT sector [5], [6] . Scientific workflow systems such as Nextflow [7] allow for the design, execution, and monitoring of workflows on heterogeneous clusters. While these systems usually generate detailed performance traces and logs for executed workflows, they do not produce a record of the energy consumed and Consequently, users must manually monitor power consumption with hardware/software power meters or, otherwise, use a methodology like Cloud Carbon Footprint(CCF) 1 or Green Algorithms (GA) [8] . In practice, monitoring power consumption requires the user to obtain physical access to attach a power meter or sufficient privileges to enable a software-based tool like Intel’s Running Average Power Limit (RAPL) prior to executing a workflow. Without this step, power consumption can only be estimated based on coarse-grained utilization averages. This is possible using the CCF and GA methodologies, though at reduced accuracy. The GA methodology relies on vendor-specified Thermal Design Power (TDP) of assigned compute resources, a proprietary metric that does not reflect key processor settings, such as processor frequency, and does not indicate idle power consumption. Meanwhile, the CCF methodology builds alinear power model between th

FAQs

Why is carbon footprint estimation for scientific workflows important?

Scientific workflows often run resource-intensive and for long periods, causing significant energy consumption and carbon emissions, while ICT-sector emissions continue to rise. Quantifying these impacts helps evaluate and reduce emissions.

What inputs does Ichnos+ use for post-hoc estimation?

Ichnos+ uses existing Nextflow workflow traces, fitted node-specific power models for the utilized compute resources, and carbon intensity data aligned with execution time to convert estimated energy into carbon emissions.

How does Ichnos+ evaluate its estimation accuracy?

Ichnos+ is evaluated against hardware-level energy measurements from Intel RAPL and compared with the nf-core co2footprint plugin implementing the Green Algorithms methodology, showing lower estimation error across multiple compute clusters.

Jul 16, 2026

Upload by Riley