1A Machine Learning Approach to TCP Throughput Prediction

Abstract—TCP throughput prediction is an important capability for networks where multiple paths exist between data senders and receivers. In this paper we describe a new, lightweight method for TCP throughput prediction. Our predictor uses Support Vector Regression; prediction is based on both prior file transfer history and measurements of simple path properties. We evaluate our predictor in a laboratory setting where ground truth can be measured with perfect accuracy. We report the performance of our predictor for oracular and practical measurements of path properties over a wide range of traffic conditions and transfer sizes. For bulk transfers in heavy traffic using oracular measurements, TCP throughput is predicted within 10 % of the actual value 87 % of the time, representing nearly a 3-fold improvement in accuracy over prior history-based methods. For practical measurements of path properties, predictions can be made within 10 % of the actual value nearly 50 % of the time, approximately a 60 % improvement over history-based methods, and with much lower measurement traffic overhead. We implement our predictor in a tool called PathPerf, test it in the wide area, and show that PathPerf predicts TCP throughput accurately over diverse wide area paths