CEDTS-RL: Towards Efficient and Green Cross-Geographical Data Centers based on Reinforcement Learning

Nowadays, data centers contribute to a large portion of global carbon emissions due to the growing demand for cloud services. Traditional green data center technologies have utilized all kinds of methods to reduce energy consumption, e.g., optimizing HVAC (Heating, Ventilation, and Air Conditioning), shutting down idle servers or switches and using renewable energy, but they typically target a single data center and have pushed the optimization space to its limits. However, carbon emissions vary wildly across data centers due to differences in local energy sources, and delay-tolerant tasks can be flexibly allocated to data centers with lower emissions. This opens new opportunities for carbon-aware task scheduling across geographical locations. However, carbon-aware scheduling faces key challenges, including the fluctuation of carbon intensity due to weather and power dynamics, and the dynamic nature of task arrivals with diverse QoS (Quality of Service) requirements. To address this, we propose CEDTS-RL (Carbon-Emission Driven Task Scheduling based on Reinforcement Learning), a real-time cross-geographical scheduling framework based on reinforcement learning, which unlocks new potential for reducing carbon emissions by relaxing the location restriction during scheduling. The evaluation is conducted using real-world datasets from Google, Alibaba, and Microsoft, along with solar and meteorological data from NASA. Results demonstrate that CEDTS-RL significantly outperforms both traditional and recent scheduling algorithms.