An Empirical Study of Throughput Prediction in Mobile Data Networks

Bandwidth-sensitive applications such as adaptive video streaming rely on accurate prediction of future network throughput to enable them to react to and compensate for the rapidly fluctuating bandwidth often found in mobile networks. Researchers have developed various prediction algorithms in the literature of which many have been employed in real-world applications. However, there is a lack of systematic study on the comparative performance of the existing prediction algorithms in the context of mobile networks. This work addresses this void by conducting a systematic performance comparison of 7 prediction algorithms, and analyzes their characteristics when applied to the prediction of TCP throughput in mobile networks. The performance results are obtained from extensive trace-driven simulations where the throughput trace data were captured in production 3G/HSPA mobile networks in 3 locations over a period of 9 months and hence offer a good representation of the prediction algorithms' real-world performance. Furthermore, we applied the theory of differential entropy in information theory to obtain an estimated lower bound on throughput prediction errors which, for the first time, enables one to evaluate the absolute performance of these prediction algorithms. The results revealed that more complex algorithms are not necessarily better, and there exists a specific range of operating parameters where predictions are generally more accurate.