A Data-Driven Framework for TCP to Achieve Flexible QoS Control in Mobile Data Networks.

Learning-based approaches have shown their great potential to adapt themselves to various environments (e.g., PCC and Sprout). Unfortunately, they do not consistently achieve superior QoS across different network conditions and configurations in mobile networks. Furthermore, although they can offer multiple application objectives by adjusting a preference weight vector, it is challenging for users to accurately express an application objective with a weight vector. In this work, we argue that, if configured correctly, the delay-based TCP scheme can outperform learned ones, and allow users to directly specify their objectives. To this end, we propose Post-QoS Analysis (PQSA), a data-driven framework that trains the key QoS-impacting parameters of the scheme to capture the statistical correlations between QoS objectives, network conditions, and configurations, thereby determining the optimal parameter-set that meets the user-defined QoS objective under different network conditions and configurations. To support this, we enhance conventional delay-based TCP design to develop a Generalized TCP-like Rate controller (GR) by exporting three key parameters. Extensive evaluations show that PQSA-optimized GR outperforms existing schemes in different scenarios consistently, and enables service providers to control the QoS flexibly.