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| With the rapid development of mobile Internet and Internet of things, a series of time sensitive services such as video conferencing, cloud games, AR / VR have emerged. In order to meet the above time sensitive services, IETF proposed Deterministic Network(DetNet) architecture, which provides an ideal deterministic delay through clock synchronization, zero congestion loss and other mechanisms. At the same time, Tsinghua University proposed Deadline-aware transport protocol(DTP), hoping to specify the deadline in the application layer, and then meet the requirements in the transport layer. These strategies and ideas for time delay sensitive services are worth learning from. However, the current packet transmission mechanisms are all rule-based and relatively static strategies, which can’t meet the demand of time sensitive service in dynamic network. Therefore, based on the idea of DTP and reinforcement learning, this paper proposes an algorithm to dynamically adjust the transmission priority. More specifically, we design the reward and penalty function according to the requirements of DTP protocol, and design the algorithm of congestion control and packet scheduling in the transport layer. We consider not only the priority but also the service deadline. Comprehensive experiments show that compared to traditional packet scheduling strategies, our algorithm performs better in the transmission of time-sensitive services |
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| Keywords:Time-sensitive service,DQN,congestion control,packet scheduling |
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