Uncertainty-Aware Decarbonization for Datacenters
<|reference_start|>Uncertainty-Aware Decarbonization for Datacenters: This paper represents the first effort to quantify uncertainty in carbon intensity forecasting for datacenter decarbonization. We identify and analyze two types of uncertainty -- temporal and spatial -- and discuss their system implications. To address the temporal dynamics in quantifying uncertainty for carbon intensity forecasting, we introduce a conformal prediction-based framework. Evaluation results show that our technique robustly achieves target coverages in uncertainty quantification across various significance levels. We conduct two case studies using production power traces, focusing on temporal and spatial load shifting respectively. The results show that incorporating uncertainty into scheduling decisions can prevent a 5% and 14% increase in carbon emissions, respectively. These percentages translate to an absolute reduction of 2.1 and 10.4 tons of carbon emissions in a 20 MW datacenter cluster.<|reference_end|>
@article{li2024uncertainty-aware,
    title={Uncertainty-Aware Decarbonization for Datacenters},
    author={Amy Li, Sihang Liu, Yi Ding},
    journal={arXiv preprint arXiv:2407.02390},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.02390},
    primaryClass={cs.DC cs.LG}
}