Carbon-Aware AI Scheduling: A Data-Driven Framework for Environmentally Sustainable Artificial Intelligence Workloads

Abstract

The fast-growing Artificial Intelligence (AI) has led to the enormous processing requirements previously unknown, which cause high energy usage and related carbon emissions. Although the previous studies have concentrated on enhancing the efficiency of the algorithms, less attention has been given on the timing and placement of AI workloads. This paper introduces a Carbon-Aware AI Scheduler a full-stack application that optimizes the carbon footprint of AI workloads by utilizing smart time-shifting and geo-shifting operations. The system combines actual carbon intensity data, a hybrid model of forecasting which uses historical baselines and artificial diurnal curves, and daily job urgency and flexibility optimization algorithms. Experimental testing in over 50 regions throughout the U.S. shows that carbon emissions can be reduced by as much as 87 percent in case of geo-shifted workloads and by 2530 percent in case of time-shifted workloads in one region. The scheduler has a latency of sub-200 ms, which is acceptable in real-time application. Findings suggest that carbon-conscious scheduling, alongside the clear user feedback, may have a considerable effect on the behavior of developers and have a positive impact on the environmental impact of AI systems.