# Uncertainty-Aware Decarbonization for Datacenters

arXiv: https://arxiv.org/abs/2407.02390 · HTML: https://mlsanity.com/paper/2407.02390/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 22 references that are in the map.
- First posted on arXiv 2 Jul 2024.

## Builds on (most cited first, 7 of 7)

- [Sequential Predictive Conformal Inference for Time Series](https://mlsanity.com/paper/2212.03463/) (2022)
- [Carbon-Aware Computing for Datacenters](https://mlsanity.com/paper/2106.11750/) (2021)
- [Carbon Explorer: A Holistic Approach for Designing Carbon Aware Datacenters](https://mlsanity.com/paper/2201.10036/) (2022)
- [CarbonScaler: Leveraging Cloud Workload Elasticity for Optimizing Carbon-Efficiency](https://mlsanity.com/paper/2302.08681/) (2023)
- [Let's Wait Awhile: How Temporal Workload Shifting Can Reduce Carbon Emissions in the Cloud](https://mlsanity.com/paper/2110.13234/) (2021)
- [Ecovisor: A Virtual Energy System for Carbon-Efficient Applications](https://mlsanity.com/paper/2210.04951/) (2022)
- [The Green Mirage: Impact of Location- and Market-based Carbon Intensity Estimation on Carbon Optimization Efficacy](https://mlsanity.com/paper/2402.03550/) (2024)

## Built on by (most cited first, 3 of 3)

- [EnsembleCI: Ensemble Learning for Carbon Intensity Forecasting](https://mlsanity.com/paper/2505.01959/) (2025)
- [CarbonX: An Open-Source Tool for Computational Decarbonization Using Time Series Foundation Models](https://mlsanity.com/paper/2510.01521/) (2025)
- [Signal-Aware Workload Shifting Algorithms with Uncertainty-Quantified Predictors](https://mlsanity.com/paper/2509.26511/) (2025)

## Abstract (arXiv)

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.