Machine-Learning Based Prediction of Multiple Types of Network Traffic

Prior knowledge regarding approximated future traffic requirements allows adjusting suitable network parameters to improve the network’s performance. To this end, various analyses and traffic prediction methods assisted with machine learning techniques are developed. In this paper, we study on-line multiple time series prediction for traffic of various frame sizes. Firstly, we describe the gathered real network traffic data and study their seasonality and correlations between traffic types. Secondly, we propose three machine learning algorithms, namely, linear regression, k nearest neighbours, and random forest, to predict the network data which are compared under various models and input features. To evaluate the prediction quality, we use the root mean squared percentage error (RMSPE). We define three machine learning models, where traffic related to particular frame sizes is predicted based on the historical data of corresponding frame sizes solely, several frame sizes, and all frame sizes. According to the performed numerical experiments on four different datasets, linear regression yields the highest accuracy when compared to the other two algorithms. As the results indicate, the inclusion of historical data regarding all frame sizes to predict summary traffic of a certain frame size increases the algorithm’s accuracy at the cost of longer execution times. However, by appropriate input features selection based on seasonality, it is possible to decrease this time overhead at the almost unnoticeable accuracy decrease.