# Let's Wait Awhile: How Temporal Workload Shifting Can Reduce Carbon Emissions in the Cloud

arXiv: https://arxiv.org/abs/2110.13234 · HTML: https://mlsanity.com/paper/2110.13234/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Oct 2021.

## Built on by (most cited first, 14 of 14)

- [FedZero: Leveraging Renewable Excess Energy in Federated Learning](https://mlsanity.com/paper/2305.15092/) (2023)
- [Online Conversion with Switching Costs: Robust and Learning-Augmented Algorithms](https://mlsanity.com/paper/2310.20598/) (2023)
- [Chasing Convex Functions with Long-term Constraints](https://mlsanity.com/paper/2402.14012/) (2024)
- [Uncertainty-Aware Decarbonization for Datacenters](https://mlsanity.com/paper/2407.02390/) (2024)
- [CarbonX: An Open-Source Tool for Computational Decarbonization Using Time Series Foundation Models](https://mlsanity.com/paper/2510.01521/) (2025)
- [LACS: Learning-Augmented Algorithms for Carbon-Aware Resource Scaling with Uncertain Demand](https://mlsanity.com/paper/2404.15211/) (2024)
- [Ecomap: Sustainability-Driven Optimization of Multi-Tenant DNN Execution on Edge Servers](https://mlsanity.com/paper/2503.04148/) (2025)
- [PowerSlider: Exploiting Phase Asymmetry for LLM Serving under Demand Response](https://mlsanity.com/paper/2608.21719/) (2026)
- [Learning-Augmented Competitive Algorithms for Spatiotemporal Online Allocation with Deadline Constraints](https://mlsanity.com/paper/2408.07831/) (2024)
- [Signal-Aware Workload Shifting Algorithms with Uncertainty-Quantified Predictors](https://mlsanity.com/paper/2509.26511/) (2025)
- [FM-CAC: Carbon-Aware Control for Battery-Buffered Edge AI via Time-Series Foundation Models](https://mlsanity.com/paper/2604.16448/) (2026)
- [Hierarchical Multi-Agent Reinforcement Learning for Carbon-Aware AI Data Centers in Power Distribution Systems](https://mlsanity.com/paper/2607.03324/) (2026)
- [AgentDecarbonizer: Carbon-Aware Execution for AI Agents](https://mlsanity.com/paper/2608.20566/) (2026)
- [Dissecting the Carbon-SLO Tension in Geo-Distributed LLM Serving](https://mlsanity.com/paper/2610.14571/) (2026)

## Abstract (arXiv)

Depending on energy sources and demand, the carbon intensity of the public power grid fluctuates over time. Exploiting this variability is an important factor in reducing the emissions caused by data centers. However, regional differences in the availability of low-carbon energy sources make it hard to provide general best practices for when to consume electricity. Moreover, existing research in this domain focuses mostly on carbon-aware workload migration across geo-distributed data centers, or addresses demand response purely from the perspective of power grid stability and costs. In this paper, we examine the potential impact of shifting computational workloads towards times where the energy supply is expected to be less carbon-intensive. To this end, we identify characteristics of delay-tolerant workloads and analyze the potential for temporal workload shifting in Germany, Great Britain, France, and California over the year 2020. Furthermore, we experimentally evaluate two workload shifting scenarios in a simulation to investigate the influence of time constraints, scheduling strategies, and the accuracy of carbon intensity forecasts. To accelerate research in the domain of carbon-aware computing and to support the evaluation of novel scheduling algorithms, our simulation framework and datasets are publicly available.