Evaluating and Modeling 5G MPTCP Performance

Multipath connectivity and aggregation of multiple communication links is actively being researched with the aim to achieve higher throughput and lower latency. In this work we perform an emulation-based evaluation of the relative goodput of MPTCP and TCP in a 5G usage context. A large range of path capacity and delay conditions is explored, for both the primary and secondary paths, with over 2000 different configurations evaluated. Evaluations are performed over eight combinations of MPTCP schedulers and congestion controls. The results show that MPTCP running over two links provide lower goodput than TCP over a single link for the majority of cases. Asymmetry in link conditions is in many cases a major complication for the MPTCP scheduler. To examine the predictability of poor performance, and to obtain further insight on the structure of this phenomena, we perform regression modeling of the relative goodput. In addition to the traditional approaches of Linear Regression and Random Forest, we also employ Symbolic Regression to obtain mathematical expressions capable of providing insight on the path conditions most contributing to poor MPTCP performance. Such regression expressions can be informative when evaluating different schedulers or link aggregation approaches.