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Green Ai in the Cloud: Strategies for Carbon-aware Resource Management

Year: 2025

Type: article

Abstract: As AI workloads proliferate in cloud environments, their growing energy consumption contributes significantlyto carbon emissions. This paper presents a comprehensive framework for carbon-aware resource managementin cloud platforms, integrating real-time emissions data, workload scheduling, and energy-efficient autoscalingpolicies. We propose a hybrid architecture combining carbon telemetry ingestion, predictive modeling ofworkload carbon intensity, and closed-loop controllers that optimize VM placement and serverless functionprovisioning for minimal carbon footprint. A prototype on AWS and Azure using real-world energy grid datademonstrates a 30% reduction in carbon emissions and 20% cost savings under mixed batch and streamingworkloads. We detail design patterns, algorithmic strategies, and evaluation metrics, offering a blueprint foreco-friendly AI deployments

Source: Zenodo (CERN European Organization for Nuclear Research)

DOI: 10.5281/zenodo.15690628

Author: Priyanka Deshpande

Language: English

Topic: Cloud Computing and Resource Management

Subfield: Information Systems

Field: Computer Science

Domain: Physical Sciences

SDGs: Climate action, Industry, innovation and infrastructure, Responsible consumption and production

Open Access status: green

OpenAlex ID: W6912075290

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