通过动态迁移计算任务，降低AI训练碳排放
On the Effectiveness of the 'Follow-the-Sun' Strategy in Mitigating the Carbon Footprint of AI in Cloud Instances
- 根据清洁能源供应情况动态迁移计算任务
- 平均减排14.6%，峰值达16.3%
- 适合关注绿色AI与可持续计算的研究者
‘Follow-the-Sun’（FtS）是一种旨在降低计算工作负载碳足迹的理论模型，通过在需求增加、化石燃料依赖上升时，将工作负载动态迁移到清洁能源更丰富的地区。鉴于人工智能（AI）训练的高能耗引发广泛关注，本文提出将FtS作为降低AI工作负载碳足迹的策略。然而，现有文献缺乏对其有效性的科学验证。为此，本研究在部分模拟场景下开展实验，对比了四种异常检测算法在四种策略下的碳排放表现：无策略、FtS，以及两种已有最优策略——灵活启停与暂停恢复。实验基于2021年欧洲七座城市的碳强度历史数据。结果表明，FtS不仅平均实现14.6%的碳排放下降（峰值达16.3%），且能有效维持训练时间。
原文摘要 · Abstract (English)
'Follow-the-Sun' (FtS) is a theoretical computational model aimed at minimizing the carbon footprint of computer workloads. It involves dynamically moving workloads to regions with cleaner energy sources as demand increases and energy production relies more on fossil fuels. With the significant power consumption of Artificial Intelligence (AI) being a subject of extensive debate, FtS is proposed as a strategy to mitigate the carbon footprint of training AI models. However, the literature lacks scientific evidence on the advantages of FtS to mitigate the carbon footprint of AI workloads. In this paper, we present the results of an experiment conducted in a partial synthetic scenario to address this research gap. We benchmarked four AI algorithms in the anomaly detection domain and measured the differences in carbon emissions in four cases: no strategy, FtS, and two strategies previously introduced in the state of the art, namely Flexible Start and Pause and Resume. To conduct our experiment, we utilized historical carbon intensity data from the year 2021 for seven European cities. Our results demonstrate that the FtS strategy not only achieves average reductions of up to 14.6% in carbon emissions (with peaks of 16.3%) but also helps in preserving the time needed for training.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。