Towards carbon emission modeling and optimization for time-sensitive IIoT

The large-scale deployment of edge computing and cloud computing infrastructures has brought both opportunities and challenges to the realization of the green and low-carbon industrial Internet of things (IIoT). Aiming at time-sensitive IIoT services, a carbon emission optimization method based on cloud-edge collaboration was proposed. Firstly, an in-depth analysis was conducted upon the carbon emissions of time-sensitive services in IIoT under a cloud-edge collaborative framework, and a comprehensive carbon emission model including cloud computing centers, edge nodes, and backbone network data transmission was established. Based on this, considering low-latency constraints, a task offloading optimization algorithm based on the alternative direction method of multipliers (ADMM) was designed to minimize the overall carbon emissions of the considered IIoT system. To verify the effectiveness of the proposed method, extensive numerical experiments were conducted using real carbon intensity data from different regions of the United States. The results show that the proposed method can significantly reduce the carbon emissions of the considered IIoT system while guaranteeing low latency for services, and realizing the complementary advantages of cloud-edge collaboration.