AI 中文总结
研究针对数据密集型科学工作流能耗大、碳排放高的问题，提出Ichnos+系统，基于工作流跟踪、节点功率模型和碳强度数据估算碳足迹，经评估其能耗估计误差低，还能扩展到其他系统，精度高。
AI 中文摘要
随着数据密集型科学工作流规模扩大以促进对大量数据的自动化分析，其资源密集和长时间运行会产生大量能源消耗和碳排放。鉴于信息通信技术部门已产生大量且不断上升的排放，量化和理解科学工作流的碳足迹至关重要。现有工具通常不适用于共享、虚拟化环境，或采用仅基于一两个通用数据点的功率模型。本文提出Ichnos+，一种用于量化Nextflow科学工作流环境足迹的新系统。Ichnos+基于现有工作流跟踪、计算资源的特定节点功率模型以及与执行时间对齐的碳强度数据实现事后足迹估计。通过与使用英特尔RAPL获得的硬件级能源测量以及实现绿色算法方法的nf-core co2footprint插件进行评估，发现Ichnos+在三个计算集群上能够以10.8%的估计误差估算工作流能耗，显著优于nf-core插件。还表明Ichnos+不仅能估计运营碳排放，还能估计隐含排放以及水和土地使用。最后展示了Ichnos+可扩展用于另一个工作流系统Apache Airflow并保持相似的高估计精度。
英文摘要
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.
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