Backpropagation Neural Network Algorithm for Optimizing Network Bandwidth Allocation Based on User Access Patterns †

Abstract

In the era of rapid digital transformation, efficient network bandwidth allocation is vital for ensuring high-quality service and seamless network operations, particularly in environments with dynamic traffic patterns. This study proposes a novel approach to optimize bandwidth allocation using a Backpropagation Neural Network (BPNN) algorithm. The research utilizes a dataset sourced from Kaggle that has been modified to focus on prioritization during resource allocation and employs a preprocessing pipeline for consistency across network parameters. The BPNN model is trained with normalized data and evaluated using metrics such as Mean Squared Error (MSE) and R2 score. The results, with an MSE of 0.1637 and an R2 score of 0.9920, demonstrate high accuracy and minimal error in predicting bandwidth allocation. Furthermore, training and validation loss trends confirm the model’s convergence and effectiveness in real-time applications. The study underscores the integration of machine learning with network optimization principles, offering a robust framework for dynamic bandwidth management and resource-efficient network operations.