Sorting and scheduling of TSN flows based on deep reinforcement learning
Addressing the time-consuming process of finding the optimal data flow sorting in existingresearch and the lack of an effective engineering method to achieve this,this study proposes the PSNDRL framework for flow sorting and scheduling based on deep reinforcement learning. This framework comprises three key modules:the Preprocessing module for establishing a relationship map between Time-Triggered (TT) flows,the Agent module for identifying and quantifying complex cor-relations between TT flows and selecting the flow with the highest probability value,and the Environ-ment module for TT flow scheduling and reward calculation. By leveraging graph convolutional net-works and reinforcement learning,the PSNDRL framework intelligently explores the impact of flow characteristics and intricate relationships between flows from numerous TT flows on the solving time of flow scheduling algorithms based on Satisfiability Modulo Theories (SMT). Through the training of this framework,an efficient TT flow strategy sorting network is developed for TT flow selection dur-ing scheduling with SMT algorithms. To validate the effectiveness of PSNDRL,this study compares it with random sorting and baseline sorting methods. Results demonstrate that the total scheduling time of PSNDRL is reduced by 25.95％ and 24.62％,respectively,compared to random sorting and baseline sorting methods. This framework offers a novel direction for research to improve the effi-ciency of Time Sensitive Networking(TSN) flow scheduling.
time sensitive networkingdeep reinforcement learningflow sortingflow scheduling
万方数据
维普
