Construction of an Intelligent Edge Computing Platform Based on Cloud-Fog Hybrid Management System
Jing Yu Huang, Cheng Chen, Ningzhao Luo, Geng Li · 2025
With the rapid development of IoT and cloud fog systems, the distributed data from IOT terminals requires an effective method to manage and integrate relevant computing power, achieving efficient configuration and task scheduling. This paper proposes an innovative approach: constructing a dual-layer architecture that includes a DAG layer and a cloud fog layer. The DAG component simulates the task relationships and execution order, akin to the human brain, while the cloud fog component is responsible for the actual execution of tasks, similar to human limbs. Based on the use of a directed acyclic graph (DAG) architecture, various DOG configurations are designed within the cloud fog hybrid management system module, and the system selects the optimal scheduling strategy from complex tasks to achieve efficient resource management across all entities. Since DAG strategies can simulate and calculate various different policies, and flexibly configure various resources, while calculating the efficiency values after each policy and ranking them, ultimately allowing for the selection of the best strategy from multiple parallel DAG models, thereby better guiding the execution of the cloud fog hybrid system. Through experiments, this research method can significantly enhance the efficiency of edge computing in IoT networks, providing a new technological approach for the management and operation maintenance of IoT.