In this letter, a path dynamics assessment asynchronous advantage actor-critic scheduling algorithm (PDAA3C) is proposed to solve the MPTCP scheduling problem by using deep reinforcement learning Actor-Critic framework. The algorithm picks out the optimal transmitting path faster by multi-core asynchronous updating and also guarantee the network fairness. Compared with the existing algorithms, the proposed algorithm achieves 8.6% throughput gain over RLDS algorithm, and approaches the theoretic upper bound in the NS3 simulation.

A Multipath TCP (MPTCP) connection uses multiple subflows (i.e., TCP flows), each of which traverses over a wireless link, enabling throughput and resilience enhancements in mobile wireless networks. However, to achieve the benefits, the subflows are necessarily initialized (i.e., must complete TCP handshakes) and sequentially attached to the MPTCP connection. In the standard (MPTCPST), MPTCP initialization raises several problems. First, the TCP handshake of opening subflow is generally associated with a predetermined network. That leads to degraded MPTCP performance when the network does not have the lowest latency among available ones. Second, the first subflow's initialization needs to be successful before the next subflow can commence its attempt to achieve initialization. Therefore, the resilience of multiple paths fails when the first initialization fails. This paper proposes a novel method for MPTCP initialization, namely MPTCPSD (i.e., MPTCP with SYN duplication), which can solve the problems. MPTCPSD duplicates the first SYN and attempts to establish TCP handshakes for all subflows simultaneously, hence inherently improves the loss-resiliency. The subflow that achieves initialization first, is selected as the first subflow, consequently solving the first problem. We have implemented and extensively evaluated MPTCPSD in comparison to MPTCPST. In an emulated network, the evaluation results show that MPTCPSD has better performance that MPTCPST with the scenarios of medium and short flows. Moreover, MPTCPSD outperforms MPTCPST in the case that the opening subflow fails. Moreover, a real network evaluation proves that MPTCPSD efficiently selects the lowest delay network among three ones for the first subflow regardless of the preconfigured default network. Additionally, we propose and implement a security feature for MPTCPSD, that prevents the malicious subflow from being established by a third party.