# Carbon-Aware Computing for Datacenters

arXiv: https://arxiv.org/abs/2106.11750 · HTML: https://mlsanity.com/paper/2106.11750/

## Key facts

- Cited by 41 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Jun 2021.

## Built on by (most cited first, 30 of 41)

- [Sustainable AI: Environmental Implications, Challenges and Opportunities](https://mlsanity.com/paper/2111.00364/) (2021)
- [A Survey on Inference Optimization Techniques for Mixture of Experts Models](https://mlsanity.com/paper/2412.14219/) (2024)
- [The Cost of Dynamic Reasoning: Demystifying AI Agents and Test-Time Scaling from an AI Infrastructure Perspective](https://mlsanity.com/paper/2506.04301/) (2025)
- [Data-Centric Green AI: An Exploratory Empirical Study](https://mlsanity.com/paper/2204.02766/) (2022)
- [Hybrid Heterogeneous Clusters Can Lower the Energy Consumption of LLM Inference Workloads](https://mlsanity.com/paper/2407.00010/) (2024)
- [FedZero: Leveraging Renewable Excess Energy in Federated Learning](https://mlsanity.com/paper/2305.15092/) (2023)
- [Towards Environmentally Equitable AI via Geographical Load Balancing](https://mlsanity.com/paper/2307.05494/) (2023)
- [Online Conversion with Switching Costs: Robust and Learning-Augmented Algorithms](https://mlsanity.com/paper/2310.20598/) (2023)
- [Beyond Efficiency: Scaling AI Sustainably](https://mlsanity.com/paper/2406.05303/) (2024)
- [A Green(er) World for A.I](https://mlsanity.com/paper/2301.11581/) (2023)
- [A Survey on the Green Development of Large Models: From Resource-Efficient Architectures to Hardware-Software Co-Design](https://mlsanity.com/paper/2607.09084/) (2026)
- [Chasing Convex Functions with Long-term Constraints](https://mlsanity.com/paper/2402.14012/) (2024)
- [Carbon Footprint Reduction for Sustainable Data Centers in Real-Time](https://mlsanity.com/paper/2403.14092/) (2024)
- [Sustainable AIGC Workload Scheduling of Geo-Distributed Data Centers: A Multi-Agent Reinforcement Learning Approach](https://mlsanity.com/paper/2304.07948/) (2023)
- [Uncertainty-Aware Decarbonization for Datacenters](https://mlsanity.com/paper/2407.02390/) (2024)
- [Tu(r)ning AI Green: Exploring Energy Efficiency Cascading with Orthogonal Optimizations](https://mlsanity.com/paper/2506.18289/) (2025)
- [Learning-Augmented Decentralized Online Convex Optimization in Networks](https://mlsanity.com/paper/2306.10158/) (2023)
- [Anytime-Competitive Reinforcement Learning with Policy Prior](https://mlsanity.com/paper/2311.01568/) (2023)
- [Turning AI Data Centers into Grid-Interactive Assets: Results from a Field Demonstration in Phoenix, Arizona](https://mlsanity.com/paper/2507.00909/) (2025)
- [XWind: A Cross-site Router for Large Language Model Inference Serving at Renewable Energy Farms](https://mlsanity.com/paper/2605.23348/) (2026)
- [Power-Flexible AI Data Centers: A New Paradigm for Grid-Responsive Compute](https://mlsanity.com/paper/2606.25098/) (2026)
- [Sustainability of Data Center Digital Twins with Reinforcement Learning](https://mlsanity.com/paper/2404.10786/) (2024)
- [Carbon Connect: An Ecosystem for Sustainable Computing](https://mlsanity.com/paper/2405.13858/) (2024)
- [Building Socially-Equitable Public Models](https://mlsanity.com/paper/2406.02790/) (2024)
- [Forecasting Multivariate Urban Data via Decomposition and Spatio-Temporal Graph Analysis](https://mlsanity.com/paper/2505.22474/) (2025)
- [Green AI: A systematic review and meta-analysis of its definitions, lifecycle models, hardware and measurement attempts](https://mlsanity.com/paper/2511.07090/) (2025)
- [Efficiency Will Not Lead to Sustainable Reasoning AI](https://mlsanity.com/paper/2511.15259/) (2025)
- [A Cherry-Picking Approach to Large Load Shaping for More Effective Carbon Reduction](https://mlsanity.com/paper/2601.17990/) (2026)
- [A Microservice-Based Platform for Sustainable and Intelligent SLO Fulfilment and Service Management](https://mlsanity.com/paper/2602.12875/) (2026)
- [Carbon-Aware Governance Gates: An Architecture for Sustainable GenAI Development](https://mlsanity.com/paper/2602.19718/) (2026)

## Abstract (arXiv)

The amount of CO2 emitted per kilowatt-hour on an electricity grid varies by time of day and substantially varies by location due to the types of generation. Networked collections of warehouse scale computers, sometimes called Hyperscale Computing, emit more carbon than needed if operated without regard to these variations in carbon intensity. This paper introduces Google's system for Carbon-Intelligent Compute Management, which actively minimizes electricity-based carbon footprint and power infrastructure costs by delaying temporally flexible workloads. The core component of the system is a suite of analytical pipelines used to gather the next day's carbon intensity forecasts, train day-ahead demand prediction models, and use risk-aware optimization to generate the next day's carbon-aware Virtual Capacity Curves (VCCs) for all datacenter clusters across Google's fleet. VCCs impose hourly limits on resources available to temporally flexible workloads while preserving overall daily capacity, enabling all such workloads to complete within a day. Data from operation shows that VCCs effectively limit hourly capacity when the grid's energy supply mix is carbon intensive and delay the execution of temporally flexible workloads to "greener" times.