Carbon-Aware Continuous Learning for Sustainable Real-Time Machine Learning Analytics

Real-time machine learning (ML) analytics models deployed on edge servers often experience degraded inference accuracy due to data drift. Continuous learning mitigates this by periodically retraining models using newly collected data. However, retraining incurs significant computational overhead, increasing energy consumption and carbon footprint several-fold. Existing approaches for reducing carbon footprint predominantly focus on general workload scheduling strategies, such as shifting jobs to periods or regions with lower carbon intensity. However, they neglect continuous-learning-specific parameters like data labeling model, retraining threshold, and retraining hyperparameters. Consequently, these approaches miss opportunities to further reduce the carbon footprint and enhance inference accuracy in continuous learning systems under dynamic data drift. In this paper, we propose a novel Carbon-footprint-aware Continuous Learning (CCL) scheme that minimizes carbon emissions during model retraining without sacrificing inference accuracy. Distinct from prior workload scheduling approaches, CCL adaptively adjusts labeling model, retraining threshold, and retraining hyperparameters based on predictive models that estimate data drift severity and carbon intensity dynamics. Our adaptive real-time optimization approach consistently achieves a near-optimal balance between accuracy and carbon footprint under dynamic conditions. Experimental results demonstrate that CCL reduces operational carbon footprint by up to 68.5% compared to state-of-the-art carbon-agnostic methods, with negligible accuracy degradation.