GADaM: Generic Adaptive Deep-learning-based Multipath Scheduler Selector for Dynamic Heterogeneous Environment

Multipath QUIC (MQ-QUIC) and Multipath TCP (MP-TCP), known as multipath protocols, introduced several certain advantages for the next internet generation, such as enabling bandwidth aggregation of links, preventing single-path failure, increasing Quality of Service (QoS), etc. Meanwhile, the pivotal point of the transport protocols is the scheduler. Various multipath schedulers have been proposed, and each of them usually outperforms the others in each specific scenario. To provide a generic approach with the best performance and stability, a novel one is introduced in this paper and aimed to fill this research gap. Indeed, the proposed GADaM prototype is a Generic Adaptive Deep-learning-based Multipath Scheduler Selector. The idea’s prototype is implemented for the MP-QUIC protocol. The extensive results show that our scheduler selector achieved over 95% accuracy on training and 91% accuracy on the testing set in the simulated environment.