Carbon-Computing Coupling Optimization and Green Scheduling System for Intelligent Computing Centers

Under China’s “Dual-Carbon” strategic goal, reducing carbon emissions in computing centers has become a critical challenge. The increasing scale of data centers, particularly in the context of initiatives such as “East Data, West Computing,” necessitates new approaches that jointly optimize computing efficiency and carbon footprint. This paper aims to address this challenge by proposing a novel carbon-computing coupling optimization framework and a green scheduling system designed to minimize the carbon emissions associated with computational tasks while maintaining system robustness. We first establish a carbon efficiency dynamic equilibrium equation and introduce the concept of virtual carbon flow to model the carbon footprint of computing tasks. Based on this modeling, we develop a deep reinforcement learning (DRL) based scheduler that dynamically migrates tasks to low-carbon nodes. In addition, we integrate a digital twin platform that preemptively simulates failure scenarios to enhance system robustness and resilience. Experimental results in simulated “East Data, West Computing” scenarios demonstrate the effectiveness of the proposed approach. The system reduces carbon emissions per unit of computing power by 18%, improves the energy efficiency ratio in western nodes by 35%, and decreases the Mean Time to Recovery (MTTR) from 2 hours to 15 minutes. These findings validate the potential of carbon-computing coupling optimization in achieving both sustainability and reliability goals for large-scale computing centers.