Sustainable AI Computing through Dynamic Workload Scheduling and Carbon-Aware Neural Network Optimization

Abstract

 The rapid growth of artificial intelligence training and inference workloads has produced a corresponding rise in data center energy consumption and associated carbon emissions, raising concerns about the environmental sustainability of continued AI scaling. This paper proposes a Sustainable AI Computing framework that combines carbon-aware dynamic workload scheduling with neural network optimization to reduce the operational carbon footprint of AI systems without proportionally sacrificing model performance. The proposed scheduler performs both spatial carbon shifting, routing deferrable workloads to data center regions with lower real-time grid carbon intensity, and temporal carbon shifting, delaying flexible jobs to lower-carbon time windows within deadline constraints, while a complementary neural network optimization module applies adaptive pruning, quantization, mixed-precision computation, and early-exit inference to reduce per-job energy consumption. The framework was evaluated across a simulated multi-region data center testbed spanning five regions with heterogeneous grid carbon intensity profiles, using representative training and inference workloads including convolutional and transformer-based models. Experimental results show that the proposed hybrid carbon-aware scheduler and optimizer combination reduces relative carbon emissions to 44% of a static round-robin baseline, compared to 71% for spatial shifting alone and 68% for temporal shifting alone, while cluster GPU utilization improves from an average of 52% under baseline scheduling to 78% under the proposed scheduler. Accuracy-energy trade-off analysis identifies a Pareto-efficient operating region in which up to 55% energy reduction per inference is achievable with less than one percentage point of accuracy degradation, beyond which further compression yields diminishing accuracy returns. Ablation results confirm that scheduling and model optimization contribute complementary and largely additive carbon reductions, with the combined framework outperforming either component in isolation. These findings demonstrate that meaningful reductions in AI-related carbon emissions are achievable through coordinated system-level and model-level interventions without requiring fundamental changes to underlying hardware infrastructure.