Carbon-Aware Strategies for Predictive Analytics on AWS and GCP
Keywords:
Carbon-aware deployment, predictive analytics, cloud platforms, GCP, AWS, energy efficiency, lifecycle optimization,Carbon-aware machine learning deployment,Green predictive analytics,Sustainable cloud machine learning,Energy-efficient ML models,Carbon-aware workload scheduling,Low-carbon cloud computing,Sustainable AI on cloud platforms,GCP carbon-aware deployment,AWS sustainable machine learning,Green AI infrastructure,Cloud energy optimization for ML,Carbon footprint reduction in ML pipelines,Eco-friendly data science workflows,Environmentally sustainable MLOps,Climate-conscious cloud architecture.Abstract
Achieving energy-efficient cloud-based predictive-analytics deployments with reduced carbon emissions presents an important challenge. Existing approaches focus on optimizing performance and operational costs without considering their long-term impact on the growing carbon footprint. Moreover, green deployments are rarely integrated into decision making. A carbon-aware model-deployment strategy is presented, which incorporates carbon-intensity forecasts to minimize emissions. Carbon-aware scheduling and spatial routing balance latency and grid carbon-intensity while enabling real-time predictions in hybrid edge-cloud deployments. The objective functions and constraints are flexible, supporting trajectory optimization, temporal alignment with renewable-energy-generation patterns, and energy-awareness during model training. Analyses of the Google Cloud Platform and Amazon Web Services elucidate how services and tools can be leveraged to execute carbon-aware deployments. The approach is demonstrated in three carbon-aware case studies: real-time inference, batch training with regional carbon-minimization, and AutoML hyperparameter tuning. The proposed formulations provide decision-support information for directing green predictive-analytics workloads across cloud infrastructure.
Cloud-based predictive-analytics workloads typically follow a life cycle that encompasses model training, hyperparameter tuning, modeling, and inference. The infrastructures provided by cloud-service operators make it possible to elastically allocate these resources according to demand, achieving higher-performance and lower-cost deployments. Some service products allow inference to be geographically and temporally aligned with the request origin, reducing latency and improving user experience. Nonetheless, these predictions are rarely planned on the basis of grid carbon-intensity forecasts to achieve a smaller environmental impact—and carbon footprint—during the exploitation phase.
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