Online autogenerated congestion control for high-speed transfer over high BDP networks

With the exponential growth of network bandwidth and application requirements, the existing transmission control protocols have led to the issues of efficiency and applicability. In order to solve the network utility issues and accelerate the data delivery legitimately, the swift and self-learned transmission methods have attracted attention gradually due to its adaptability and predictability. In this research, we propose a high-speed transport protocol, called Hita, based on network performance learning framework to cope with high-speed transfer challenge over high bandwidth delay product networks. The key idea of Hita is to select preferable network performance metrics to reflect network property variability and build corresponding window control model. The proposed protocol, which adopts the online-learning methods, establishes the relation model between the transmission performance metrics and the corresponding congestion window. Afterwards, by determining the direction of window adjustment, the optimal sending window size can be predicted and approached quickly. Numerical results show that Hita can use the limited bandwidth more adequately. For simulation experiments, Hita achieves higher throughput while maintaining a lower packet delay comparing with other protocols. Moreover, Hita shows good performance in terms of intra-protocol fairness, friendliness and protocol stability. For real-world network experiments, Hita achieves more throughput than the other high-speed protocols as well.