GreenCloud: Carbon-Aware Resource Scheduling in Cloud Data Centers using Deep Reinforcement Learning

Cloud data centers are large power consumers and thus substantial contributors to carbon emissions. Conventional resource scheduling techniques mostly focus on performance and energy efficiency, mostly ignoring the environmental footprint. This work introduces GreenCloud, a new carbon-conscious resource scheduling system using Deep Reinforcement Learning (DRL) to reduce carbon emissions in cloud computing systems. By integrating real-time carbon intensity information into decision-making, GreenCloud adaptively distributes workloads among geographically dispersed data centers both according to resource and environmental conditions. Empowered with Proximal Policy Optimization (PPO) as the backbone of the DRL algorithm, our model learns efficient scheduling policies that optimize energy usage, carbon emissions, and SLA compliance. Experimental results show GreenCloud to have the ability to curtail carbon footprints by as high as 56% lower than Round Robin scheduling yet retain high utilization of resources and low SLA violation rates.