Machine learning-based estimation of the number of competing flows at a bottleneck link

A main factor that hindered the performance of most congestion control algorithms in the current Internet is the lack of observable information about the network congestion situation. The most cost-effective and least complex way of tackling the problem of congestion detection is to implement a local solution based on the information gathered on the sender. The number of competing flows on the network bottleneck is one of the indicator of the network congestion situation. In this paper, we explore the possibility of estimating the number of competing flows in a network bottleneck using machine learning trained on locally observable information. We used the time series classification-based method and regression-based method to perform the estimation. The proposed method is evaluated in a virtualized network. The trained classification model results in an accuracy of 1.0 when the number of competing flows it encounters is trained in advance. And the regression method can have a MAPE of around 30 [%] in most situations for the number of competing flows that have not been trained into the model.