Forecasting Bandwidth of Network Interfaces Using Advanced Machine Learning Techniques

Accurate bandwidth prediction in network interfaces is essential for effective network management, facilitating optimized resource allocation, intelligent traffic engineering, and enhanced Quality of Service (QoS) in dynamic and complex environments. This research explores advanced methodologies for bandwidth utilization forecasting, emphasizing machine learning-based approaches. The study aims to develop predictive models that leverage historical network traffic data to forecast bandwidth demand with high precision, utilizing Python programming language for implementation. This investigation presents a hybrid bandwidth prediction framework that integrates Long Short-Term Memory (LSTM) network for feature extraction, and Support Vector Regression (SVR), Ridge regression with Random Fourier Features (RFF), Random Forest Regression (RFR), and XGBoost (Extreme Gradient Boosting) for forecasting bandwidth prediction. The proposed work contributes a comparative analysis of prediction models based on evaluation metrics. Utilizing high-resolution SNMPv2 (Simple Network Transport Protocol) data, the proposed approach ensures real-world applicability by effectively capturing temporal dependencies. The research assesses model selection strategies based on dataset complexity, feature interactions, and scalability, offering insights into optimal deployment across network environments. Finally, the result establishes XGBoost as the superior model in terms of accuracy, efficiency, and resilience to noise and paving the way for adaptive, real-time forecasting in dynamic network environments.