Traffic Analysis and Optimal Control Technology in Computer Network

With the rapid development of information technology, the explosive growth of network traffic brings great challenges to the stability and performance of the network. The purpose of this paper is to deeply analyze the new characteristics of network traffic and explore more general and efficient optimal control technology to cope with the ever-changing network environment and demand. The study first uses clustering analysis in data mining techniques to deeply explore the inherent laws and changing trends of network traffic. Then, the time series prediction model Long Short Term Memory Neural Network (LSTM) is used to predict the future trend of network traffic, providing important basis for the rational allocation of network resources, prevention of congestion control, and optimization of network performance. In addition, an optimal control strategy based on flow prediction is proposed, and a corresponding optimal control system is designed and implemented. The system collects network traffic data in real time, uses advanced LSTM prediction algorithm to predict future traffic, and formulates and optimizes control strategies based on the prediction results, so as to improve network performance. The experimental results show that the optimized control system can significantly improve the network throughput and reduce the delay under different network loads, which verifies the effectiveness of the proposed strategy.