Neural Network-based Available Bandwidth Estimation from TCP Sender-side Measurements

The information that short-lived TCP flows provide on bandwidth estimation may benefit adaptive video streaming applications or may contribute towards the success of new TCP versions. To estimate the available bandwidth active probing techniques may be used, but they cause an intrusive effect on the network affecting TCP performance. Therefore, passive measurements are favored, though capturing traffic traces comes with its own challenges. In this paper, we use the feedback provided by TCP acknowledgments and perform the estimation from sender-side measurements only. However, difficulties arise when in the presence of discrete random cross traffic, multiple tight links, packet losses, and inaccurate timestamping in general, the acknowledgment packet gaps get distorted. To deal with noise-afflicted packet gaps, we consider a machine-learning approach, specifically the neural network, for estimating the available bandwidth. We also apply the neural network under a variety of notoriously difficult conditions that have not been included in the training, such as multiple tight links, heavy cross traffic burstiness and packet losses. We compare the performance of our proposed method with a state-of-the-art model-based technique, where our neural network approach shows improved performance.