Ichnos+: Estimating the Carbon Footprint of Scientific Workflows Using Fitted Power Models

2026-07-12

AI Summary

This work addresses the challenge of accurately quantifying the carbon footprint of scientific workflows in shared virtualized environments, where existing tools rely on oversimplified power models and lack precision. We propose the first high-fidelity carbon footprint estimation framework that supports multi-cluster deployments and is extensible across diverse workflow systems, including Nextflow and Apache Airflow. Our approach integrates workflow execution traces, node-level fitted power models, hardware-level energy measurements via Intel RAPL, and time-aligned grid carbon intensity data, while accounting for operational emissions, embodied carbon, and water–land resource consumption. Experimental evaluation across three clusters demonstrates an average energy estimation error of only 10.8%, substantially outperforming current tools such as nf-core co2footprint, and confirms successful cross-platform deployment.

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.

Problem

• carbon footprint
• scientific workflows
• power models
• energy consumption
• ICT emissions

Innovation

• carbon footprint estimation
• fitted power models
• scientific workflows
• embodied emissions
• post-hoc analysis