Large Scale Network Traffic Optimization Algorithm Based on Differential Equations

With the rapid growth of Internet users and data traffic, frequent congestion and high latency issues in large-scale networks challenge network performance and user experience. This article proposes a network traffic optimization algorithm based on differential equation theory. It first establishes a differential equation model to describe traffic dynamics between network nodes, then derives an optimal control strategy through stability analysis. An iterative algorithm is designed for real-time traffic adjustment and optimization. Experimental results demonstrate that the algorithm improves throughput and stability. Compared to TCP Reno and TCP Vegas, the differential equation-based algorithm achieves an average throughput of 72.3 Mbps and recovers congestion control to 9.5 Mbps within 3 seconds. It also adapts better to delays and maintains stable throughput even with a delay of up to 200ms. The research confirms the effectiveness of differential equation-based algorithms for optimizing network traffic, offering new methods to enhance large-scale network performance.