Research On Multi-path Traffic Scheduling Algorithm For 5G Highly Dynamic Network

In 5G networks,it has been observed that traditional Transmission Control Protocol(TCP)is insufficient to meet the demands of real-time data transmission.As an alternative,Quick UDP Internet Communication(QUIC)has emerged as a promising solution.Additionally,multi-path technology has been proposed to enhance the efficiency and reliability of application data transmission.However,conventional schedulers face challenges in adapting to the complex and dynamic 5G networks.Consequently,addressing real-time changes in multi-path conditions and devising effective packet scheduling strategies and multi-stream management algorithms to mitigate intra-stream and inter-stream Head-ofLine(HoL)blocking issues respectively has become a hot topic.With a focus on path selection and multi-stream scheduling,this study aims to investigate multi-path algorithms in dynamic 5G network scenarios.Specifically,solutions are proposed from two perspectives:alleviating intra-stream and inter-stream HoL blocking issues at the receiving end.Firstly,to address the issue of intra-stream Head-of-Line(HoL)blocking in multi-path packet scheduling,we propose a Deep Reinforcement Learning(DRL)based path selection strategy,called DeepPath,for the multi-path Quick UDP Internet Communication(MPQUIC)protocol.DeepPath employs a customized Deep Q-Network(DQN)algorithm to learn an optimal policy.To accurately model the dynamic and heterogeneous nature of 5G networks,DeepPath takes various factors into consideration,such as Round-Trip Time(RTT),standard deviation of RTT,size of congestion window,and average length of queue,as part of the state setting.The double network is jointly trained to accurately approximate the path value in the offline phase.In the online phase,the multi-path scheduler makes real-time greedy decisions to achieve maximum goodput.DeepPath has been extensively evaluated in both simulated and real-world 5G scenarios,demonstrating superior performance compared to other multi-path schedulers.Secondly,to mitigate the inter-stream Head-of-Line(HoL)blocking issue,we propose a stream priority based multi-stream management algorithm in this paper,termed PriorityStream.PriorityStream encompasses two strategies.In the first strategy,the number of packets sent in a scheduling period is determined based on the proportion of priorities,ensuring that packets of higher priority are transmitted first to prevent blocking of streams at each priority level.In the second strategy,to overcome the blocking issue associated with traditional reinjection algorithms,we firstly check whether there are packets in the unacknowledged queue that are greater than or equal to the high-priority stream.Then we introduce an immediate checking mechanism for packets of the same priority in the unacknowledged queue when the last packet of a higher priority stream is sent.This approach effectively prevents low priority streams from blocking high priority streams.Through experimental evaluations,we demonstrate that our scheduler,consisting of DeepPath for path selection and PriorityStream for multi-stream management,outperforms other scheduling algorithms.We have also established a complete 5G network simulation environment,consisting of a 5G core network,a terminal,and a base station.We conduct experiments in the 5G core network to evaluate the effectiveness of the proposed scheme and the network throughput.The results show that by replacing HTTP/2 with our improved MPQUIC protocol,the time required for signaling transmission between network functions is reduced,leading to significant improvement in signaling transmission efficiency.