Grid Carbon-Intensity-Aware Power–Computing Coordinated Task Scheduling for Data Centers

With the rapid growth of AI, data-center energy consumption continues to increase. To balance computing service requirements and renewable-energy fluctuations, a grid carbon-intensity-aware power–computing coordinated scheduling method was proposed. First, a scheduling framework for geographically distributed heterogeneous data centers was developed to characterize how carbon emission attributes on the power supply side influence task allocation on the computing side. Then, considering workflow dependencies, heterogeneous resources, and spatiotemporal variations in grid carbon intensity, a GINEConv-PPO scheduling mechanism was proposed. A temporal planning algorithm extracted critical-chain positions and resource-contention risks to construct temporal-priority features, while GINEConv was integrated with PPO for low-carbon task allocation under QoS constraints. Simulations across workflow types and sizes showed that GINEConv-PPO met job-batch deadlines. Relative to the lowest emissions achieved by the compared algorithms in each scenario, its emission deviation remained near 0%, versus approximately 6%–35% for the baselines.

# Telecommunication # Power-Computing co-scheduling # task scheduling # carbon emissions # deep reinforcement learning

WANG Ziyi, GUO Shaoyong, CHEN Jiewei, et al. (2026). "Grid Carbon-Intensity-Aware Power–Computing Coordinated Task Scheduling for Data Centers". Tongxin xuebao.