A machine learning approach to TCP throughput prediction

Abstract

TCP throughput prediction is an important capability in wide area overlay and multi-homed networks where multiple paths may exist between data sources and receivers. In this paper we describe a new, lightweight method for TCP throughput prediction that can generate accurate forecasts for a broad range of file sizes and path conditions. Our method is based on Support Vector Regression modeling that uses a combination of prior file transfers and measurements of simple path properties. We calibrate and evaluate the capabilities of our throughput predictor in an extensive set of lab-based experiments where ground truth can be established for path properties using highly accurate passive measurements. We report the performance for our method in the ideal case of using our passive path property measurements over a range of test configurations. Our results show that for bulk transfers in heavy traffic, 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. In the same lab environment, we assess our method using less accurate active probe measurements of path properties, and show that predictions can be made within 10% of the actual value nearly 50% of the time over a range of file sizes and traffic conditions. This result represents approximately a 60% improvement over history-based methods with a much lower impact on end-to-end paths. Finally, we implement our predictor in a tool called PathPerf and test it in experiments conducted on wide area paths. The results demonstrate that PathPerf predicts TCP through put accurately over a variety of paths.