A carbon aware job scheduling framework for data center sustainability using deep learning training

Abstract

Deep learning workloads have experienced rapid growth which has resulted in higher energy consumption and increased carbon emissions for contemporary data centres. The existing solutions of carbon tracking and static scheduling systems provide insufficient capacity to implement carbon awareness in actual machine learning operational processes. In this paper, we present EcoSchedAI (Eco-Aware Scheduling using Artificial Intelligence), a carbon-aware scheduling framework that integrates both real-time and predicted carbon intensity into the AI training process. The system introduces the concept of carbon opportunity windows, enabling proactive scheduling of training jobs during low-carbon periods. It also incorporates job-level constraints, allowing flexible and practical deployment in real-world scenarios. The framework uses a convolutional neural network (CNN) to evaluate three different scheduling methods which include baseline and rule-based and reinforcement learning-based approaches together with multi-region carbon assessment. The results demonstrate that EcoSchedAI can reach a maximum carbon reduction of 9.72% while sustaining model performance through its small operational impact. This research demonstrates how predictive scheduling and constraint-aware scheduling methods can create environmentally friendly AI systems which operate at large scales.