Fast pattern-based throughput prediction for TCP bulk transfers

The ability to quickly predict the throughput of a TCP transfer between a client and a server, or between peers, has wide application in scientific computing and commercial computing. This paper presents a new approach to fast prediction of overall throughput of a large TCP file transfer. The method constructs the time series of windows of segments arriving at the receiver, and predicts future throughput by exploiting knowledge of how TCP manages transfer window size. When the file transfer time series resembles a known TCP pattern, this information is utilized for prediction, otherwise simple heuristics are used. We have compared TCP pattern based prediction against traditional methods like a simple moving average, exponential weighted moving average, and aggregate measured throughput on a large suite of real life TCP traces. Our results show that TCP pattern based prediction generally performs as well or better than the best of other methods in any given scenario.