Abstract: A variable length shaping queue adjustment algorithm based on an improved krill herd algorithm and traffic prediction is proposed to address the issues of low buffer resource utilization and high average delay of schedulable streams using fixed length shaping queues for traffic shaping in asynchronous traffic shaper(ATS).Considering the queue allocation rules of flows,bounded delay requirements,and limited buffer resources,transmission constraints for schedulable flows are defined in time-sensitive networks.The improved krill herd algorithm is used to find the maximum adjustable upper limit of the shaping queue,using a combination of chaos mapping,opposition-based learning,elite policy,and adaptive location update strategy to enhance the algorithm’s solving ability.The traffic is predicted based on convolutional neural network and long short-term memory model(CNN-LSTM),and the queue length is calculated according to the predicted value to adjust the step.Simulation results show that compared with the method of using fixed-length shaping queues,the proposed algorithm can effectively increase the number of sche-dulable flows,reduce the average delay of scheduled traffic(ST),and improve the utilization rate of network buffer resources.

Key words: Time-sensitive network, Asynchronous traffic shaper, Improved krill herd algorithm, Traffic prediction, Variable length queue