From an In-Depth Understanding of Multipath TCP Enhancement Schemes to an Adaptive Control Framework in Wireless Networks

Multipath TCP (MPTCP) has gained popularity to enhance data transmission. From the last decade, proposed MPTCP enhancement schemes for congestion control, path management, and packet scheduling, have been used to benefit transmission performance. However, despite their efforts, they are exigent with a comprehensive understanding of real-world performance to guide the implementation of MPTCP to a more complex wireless network. To that end, we conduct a measurement-driven study of MPTCP enhancement schemes, providing insights and in-depth demonstrations of their performance with a comprehensive real-world platform. Our finding indicates that the enhancement schemes struggle to consistently maintain high performance at all times. One can achieve optimal efficiency in its specific scenarios, but suffers extreme degradation at times. To eliminate this transmission uncertainty in wireless networks, we further propose an adaptive control framework OLSch to integrate different schemes, emphasizing their strengths to provide consistently high performance. To be specific, OLSch is implemented with different scheduling schemes and leverages an online-learning-driven approach to choose one that best fits the current network conditions. Evaluations show that OLSch obviously improves the stability of transmission in harsh network scenarios, eliminates performance degradation, and increases the 95% tail throughput by 1.45×-2.39×.