In order to improve the data transmission efficiency in edge computing application scenarios and manage the concurrent data traffic effectively， a relay control model for concurrent data flow in edge computing was designed. Firstly， based on the features of Data Plane Development Kit （DPDK） such as bypassing kernel， multi-core processing， and sending and receiving packets from multiple network ports， the concurrent receiving and forwarding processing of data flow was realized. Secondly， by establishing a system model with Model Predictive Control （MPC） as the core， state prediction was used to optimize control inputs and provide timely feedback and adjustment， so as to achieve the control of data traffic. Finally， a Weighted Round-Robin （WRR） algorithm was proposed to allocate weights according to buffer size and recent usage time in order to achieve load balancing of data flow. Experimental results show that the proposed model is able to control the real-time data flow rate effectively in edge network environment， and has the control error between -1% and 2%. The proposed model improves the data flow sending bit rate of edge nodes in real application scenarios compared with traditional Linux kernel forwarding， and the transmission quality and packet delay are also improved accordingly. It can be seen that the proposed model can meet the demands for low latency and high bandwidth in edge clusters and internet of things data centers， and can optimize critical computing resources while reducing peak loads.