A Contextual Bi-armed Bandit Approach for MPTCP Path Management in Heterogeneous LTE and WiFi Edge Networks

Abstract

Multi-homed mobile devices are capable of aggregating traffic transmissions over heterogeneous networks. MultiPath TCP (MPTCP) is an evolution of TCP that allows the simultaneous use of multiple interfaces for a single connection. Despite the success of MPTCP, its deployment can be enhanced by controlling which network interface to be used as an initial path during the connectivity setup. In this paper, we proposed an online MPTCP path manager based on the contextual bandit algorithm to help choose the optimal primary path connection that maximizes throughput and minimizes delay and packet loss. The contextual bandit path manager deals with the rapid changes of multiple transmission paths in heterogeneous networks. The output of this algorithm introduces an adaptive policy to the path manager whenever the MPTCP connection is attempted based on the last hop wireless signals characteristics. Our experiments run over a real dataset of WiFi/LTE networks using NS3 implementation of MPTCP, enhanced to better support MPTCP path management control. We analyzed MPTCP’s throughput and latency metrics in various network conditions and found that the performance of the contextual bandit MPTCP path manager improved compared to the baselines used in our evaluation experiments. Utilizing edge computing technology, this model can be implemented in a mobile edge computing server to dodge MPTCP path management issues by communicating to the mobile equipment the best path for the given radio conditions. Our evaluation demonstrates that leveraging adaptive contextawareness improves the utilization of multiple network interfaces.