# Enabling Sustainable Clouds: The Case for Virtualizing the Energy System

arXiv: https://arxiv.org/abs/2106.08872 · HTML: https://mlsanity.com/paper/2106.08872/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Jun 2021.

## Built on by (most cited first, 12 of 12)

- [Making AI Less "Thirsty": Uncovering and Addressing the Secret Water Footprint of AI Models](https://mlsanity.com/paper/2304.03271/) (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)
- [CAFE: Carbon-Aware Federated Learning in Geographically Distributed Data Centers](https://mlsanity.com/paper/2311.03615/) (2023)
- [CE-NAS: An End-to-End Carbon-Efficient Neural Architecture Search Framework](https://mlsanity.com/paper/2406.01414/) (2024)
- [Chasing Convex Functions with Long-term Constraints](https://mlsanity.com/paper/2402.14012/) (2024)
- [Carbon-Efficient Neural Architecture Search](https://mlsanity.com/paper/2307.04131/) (2023)
- [Green Federated Learning via Carbon-Aware Client and Time Slot Scheduling](https://mlsanity.com/paper/2509.08980/) (2025)
- [Learning-Augmented Competitive Algorithms for Spatiotemporal Online Allocation with Deadline Constraints](https://mlsanity.com/paper/2408.07831/) (2024)
- [SPIRIT: Short-term Prediction of solar IRradIance for zero-shot Transfer learning using Foundation Models](https://mlsanity.com/paper/2502.10307/) (2025)
- [From Barrier to Bridge: The Case for AI Data Center/Power Grid Co-Design](https://mlsanity.com/paper/2605.03090/) (2026)
- [Towards Environmentally Equitable AI](https://mlsanity.com/paper/2412.16539/) (2024)

## Abstract (arXiv)

Cloud platforms' growing energy demand and carbon emissions are raising concern about their environmental sustainability. The current approach to enabling sustainable clouds focuses on improving energy-efficiency and purchasing carbon offsets. These approaches have limits: many cloud data centers already operate near peak efficiency, and carbon offsets cannot scale to near zero carbon where there is little carbon left to offset. Instead, enabling sustainable clouds will require applications to adapt to when and where unreliable low-carbon energy is available. Applications cannot do this today because their energy use and carbon emissions are not visible to them, as the energy system provides the rigid abstraction of a continuous, reliable energy supply. This vision paper instead advocates for a ``carbon first'' approach to cloud design that elevates carbon-efficiency to a first-class metric. To do so, we argue that cloud platforms should virtualize the energy system by exposing visibility into, and software-defined control of, it to applications, enabling them to define their own abstractions for managing energy and carbon emissions based on their own requirements.