• Computer Science > Distributed, Parallel, and Cluster Computing [Submitted on 20 Aug 2025 (v1), last revised 20 Feb 2026 (this version, v3)] Title:A Systematic Evaluation of the Potential of Carbon-Aware Execution for Scientific Workflows View PDF HTML (experimental)Abstract:Scientific workflows are widely used to automate scientific data analysis and often involve computationally intensive processing of large datasets on compute clusters. • As such, their execution tends to be long-running and resource-intensive, resulting in substantial energy consumption and, depending on the energy mix, carbon emissions. • Meanwhile, a wealth of carbon-aware computing methods have been proposed, yet little work has focused specifically on scientific workflows, even though they present a substantial opportunity for carbon-aware computing because they are often significantly delay tolerant, efficiently interruptible, highly scalable and widely heterogeneous. • In this study, we first exemplify the problem of carbon emissions associated with running scientific workflows, and then show the potential for carbon-aware workflow execution. • For this, we estimate the carbon footprint of seven real-world Nextflow workflows executed on different cluster infrastructures using both average and marginal carbon intensity data. • Furthermore, we systematically evaluate the impact of carbon-aware temporal shifting, and the pausing and resuming of the workflow.

Article Summaries:

  • A recent study evaluates how carbon‑aware strategies can reduce the environmental impact of scientific workflows. Researchers measured the carbon footprint of seven real‑world Nextflow workflows run on various cluster infrastructures, using both average and marginal carbon‑intensity data. They then tested two mitigation techniques: temporal shifting of execution to lower‑intensity periods and dynamic resource scaling. Results show that shifting alone can cut emissions by over 80 %, while scaling can reduce them by 67 %. The work highlights the untapped potential of carbon‑aware computing in large, delay‑tolerant scientific analyses.

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