Ecovisor: A Virtual Energy System for Carbon-Efficient Applications
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Abstract (arXiv)
Cloud platforms' rapid growth is raising significant concerns about their carbon emissions. To reduce emissions, future cloud platforms will need to increase their reliance on renewable energy sources, such as solar and wind, which have zero emissions but are highly unreliable. Unfortunately, today's energy systems effectively mask this unreliability in hardware, which prevents applications from optimizing their carbon-efficiency, or work done per kilogram of carbon emitted. To address this problem, we design an "ecovisor", which virtualizes the energy system and exposes software-defined control of it to applications. An ecovisor enables each application to handle clean energy's unreliability in software based on its own specific requirements. We implement a small-scale ecovisor prototype that virtualizes a physical energy system to enable software-based application-level i) visibility into variable grid carbon-intensity and renewable generation and ii) control of server power usage and battery charging/discharging. We evaluate the ecovisor approach by showing how multiple applications can concurrently exercise their virtual energy system in different ways to better optimize carbon-efficiency based on their specific requirements compared to a general system-wide policy.
Key facts. Cited by 7 papers in the map (data through 14 Oct 2026). First posted on arXiv 10 Oct 2022.
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Citing papers per year: 2023: 2; 2024: 3; 2025: 0; 2026: 2.
7 papers.
- 2024Toward Sustainable GenAI using Generation Directives for Carbon-Friendly Large Language Model Inferencecited by 13
- 2023FedZero: Leveraging Renewable Excess Energy in Federated Learningcited by 11
- 2023Online Conversion with Switching Costs: Robust and Learning-Augmented Algorithmscited by 10
- 2024Uncertainty-Aware Decarbonization for Datacenterscited by 3
- 2024LACS: Learning-Augmented Algorithms for Carbon-Aware Resource Scaling with Uncertain Demandcited by 2
- 2026PowerSlider: Exploiting Phase Asymmetry for LLM Serving under Demand Responsecited by 2
- 2026Dissecting the Carbon-SLO Tension in Geo-Distributed LLM Servingcited by 0
- 2026Dissecting the Carbon-SLO Tension in Geo-Distributed LLM Serving10 Oct 2026
- 2026PowerSlider: Exploiting Phase Asymmetry for LLM Serving under Demand Response22 Aug 2026
- 2024Uncertainty-Aware Decarbonization for Datacenters2 Jul 2024
- 2024LACS: Learning-Augmented Algorithms for Carbon-Aware Resource Scaling with Uncertain Demand29 Mar 2024
- 2024Toward Sustainable GenAI using Generation Directives for Carbon-Friendly Large Language Model Inference19 Mar 2024
- 2023Online Conversion with Switching Costs: Robust and Learning-Augmented Algorithms31 Oct 2023
- 2023FedZero: Leveraging Renewable Excess Energy in Federated Learning24 May 2023