Machine Learning for Traffic Prediction and Optimization in Web Networks

Efficient traffic prediction and optimization are essential for ensuring the performance and reliability of large-scale web networks. This paper explores the application of machine learning (ML) techniques to accurately forecast network traffic and optimize resource allocation. Traditional approaches often struggle with the dynamic, high-volume, and non-linear behavior of modern web traffic. To address these challenges, we propose a hybrid deep learning framework that combines Convolutional Neural Networks (CNNs) for spatial feature extraction across network segments and Long Short-Term Memory (LSTM) networks for capturing temporal dependencies. The model is trained using historical network data, including packet rates, latency measurements, and server load, to predict future traffic patterns with high accuracy.