Short-Term Flow-Based Bandwidth Forecasting using Machine Learning
This work provides an incremental improvement in network traffic forecasting for network managers, enabling more informed decision-making in network management systems.
This paper addresses the problem of predicting network traffic flow bandwidth ahead of time to improve network management decisions. The proposed framework, utilizing machine learning models, achieved a 19.73% reduction in mean absolute error and an 18.00% reduction in root mean square error compared to using current bandwidth, with Random Forest being the best performing model.
This paper proposes a novel framework to predict traffic flows' bandwidth ahead of time. Modern network management systems share a common issue: the network situation evolves between the moment the decision is made and the moment when actions (countermeasures) are applied. This framework converts packets from real-life traffic into flows containing relevant features. Machine learning models, including Decision Tree, Random Forest, XGBoost, and Deep Neural Network, are trained on these data to predict the bandwidth at the next time instance for every flow. Predictions can be fed to the management system instead of current flows bandwidth in order to take decisions on a more accurate network state. Experiments were performed on 981,774 flows and 15 different time windows (from 0.03s to 4s). They show that the Random Forest is the best performing and most reliable model, with a predictive performance consistently better than relying on the current bandwidth (+19.73% in mean absolute error and +18.00% in root mean square error). Experimental results indicate that this framework can help network management systems to take more informed decisions using a predicted network state.