mlsanityData to 7 Oct 2026

Carbon Explorer: A Holistic Approach for Designing Carbon Aware Datacenters

arXiv 2201.10036 ↗First posted 25 Jan 2022cs.DC cs.SY eess.SYcited by 31

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Abstract (arXiv)

Technology companies have been leading the way to a renewable energy transformation, by investing in renewable energy sources to reduce the carbon footprint of their datacenters. In addition to helping build new solar and wind farms, companies make power purchase agreements or purchase carbon offsets, rather than relying on renewable energy every hour of the day, every day of the week (24/7). Relying on renewable energy 24/7 is challenging due to the intermittent nature of wind and solar energy. Inherent variations in solar and wind energy production causes excess or lack of supply at different times. To cope with the fluctuations of renewable energy generation, multiple solutions must be applied. These include: capacity sizing with a mix of solar and wind power, energy storage options, and carbon aware workload scheduling. However, depending on the region and datacenter workload characteristics, the carbon-optimal solution varies. Existing work in this space does not give a holistic view of the trade-offs of each solution and often ignore the embodied carbon cost of the solutions. In this work, we provide a framework, Carbon Explorer, to analyze the multi-dimensional solution space by taking into account operational and embodided footprint of the solutions to help make datacenters operate on renewable energy 24/7. The solutions we analyze include capacity sizing with a mix of solar and wind power, battery storage, and carbon aware workload scheduling, which entails shifting the workloads from times when there is lack of renewable supply to times with abundant supply.

Key facts. Cited by 31 papers in the map (data through 7 Oct 2026). First posted on arXiv 25 Jan 2022.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 5; 2024: 12; 2025: 4; 2026: 10.

31 papers.

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  1. 2023LLMCarbon: Modeling the end-to-end Carbon Footprint of Large Language Modelscited by 71
  2. 2024Characterization of Large Language Model Development in the Datacentercited by 46
  3. 2023Making AI Less "Thirsty": Uncovering and Addressing the Secret Water Footprint of AI Modelscited by 44
  4. 2024MLPerf Power: Benchmarking the Energy Efficiency of Machine Learning Systems from Microwatts to Megawatts for Sustainable AIcited by 15
  5. 2024Toward Sustainable GenAI using Generation Directives for Carbon-Friendly Large Language Model Inferencecited by 13
  6. 2023Towards Environmentally Equitable AI via Geographical Load Balancingcited by 10
  7. 2023Online Conversion with Switching Costs: Robust and Learning-Augmented Algorithmscited by 10
  8. 2024Beyond Efficiency: Scaling AI Sustainablycited by 8
  9. 2024SustainDC: Benchmarking for Sustainable Data Center Controlcited by 8
  10. 2023CAFE: Carbon-Aware Federated Learning in Geographically Distributed Data Centerscited by 6
  11. 2025EnsembleCI: Ensemble Learning for Carbon Intensity Forecastingcited by 5
  12. 2024Chasing Convex Functions with Long-term Constraintscited by 4
19 more
  1. 2024Carbon Footprint Reduction for Sustainable Data Centers in Real-Timecited by 4
  2. 2024Uncertainty-Aware Decarbonization for Datacenterscited by 3
  3. 2025CarbonX: An Open-Source Tool for Computational Decarbonization Using Time Series Foundation Modelscited by 3
  4. 2025Green Federated Learning via Carbon-Aware Client and Time Slot Schedulingcited by 2
  5. 2026PowerSlider: Exploiting Phase Asymmetry for LLM Serving under Demand Responsecited by 2
  6. 2026PALS: Power-Aware LLM Serving for Mixture-of-Experts Modelscited by 1
  7. 2026XWind: A Cross-site Router for Large Language Model Inference Serving at Renewable Energy Farmscited by 1
  8. 2024Sustainability of Data Center Digital Twins with Reinforcement Learningcited by 0
  9. 2024Carbon Connect: An Ecosystem for Sustainable Computingcited by 0
  10. 2024Learning-Augmented Competitive Algorithms for Spatiotemporal Online Allocation with Deadline Constraintscited by 0
  11. 2024Towards Environmentally Equitable AIcited by 0
  12. 2025Green AI: A systematic review and meta-analysis of its definitions, lifecycle models, hardware and measurement attemptscited by 0
  13. 2026GaiaFlow: Semantic-Guided Diffusion Tuning for Carbon-Frugal Searchcited by 0
  14. 2026Distributed LLM Pretraining During Renewable Curtailment Windows: A Feasibility Studycited by 0
  15. 2026Green AI Carbon Optimizer: Carbon-Efficient Training Location Recommendation and Global AI Energy Demand Forecastingcited by 0
  16. 2026Hierarchical Multi-Agent Reinforcement Learning for Carbon-Aware AI Data Centers in Power Distribution Systemscited by 0
  17. 2026SLA-Constrained Carbon-Aware Routing in Geo-Distributed Serverless Cloudscited by 0
  18. 2026AgentDecarbonizer: Carbon-Aware Execution for AI Agentscited by 0
  19. 2026Beyond Energy: When Sustainability Dimensions Reshape LLM Serving Decisionscited by 0