低时延网络多路通信数据无冲突调度仿真  

Simulation of Collision Free Scheduling for Multi-Channel Communication Data in Low Delay Networks

作　　者：郝聪妙 孟晓丽[2] 王辉[3] HAO Cong-miao;MENG Xiao-li;WANG Hui(School of Computing,Zhengzhou Institute of Industrial Application Technology,Henan Zhengzhou 450000,China;Engineering College,Xi'an International University,Shaanxi Xi'an 710077,China;School of Computer Science and Engineering,Xi'an Technological University,Shaanxi,Xi'an 710021,China)

机构地区：[1]郑州工业应用技术学院计算机学院,河南郑州450000 [2]西安外事学院工学院,陕西西安710077 [3]西安工业大学计算机科学与工程学院,陕西西安710021

出　　处：《计算机仿真》2024年第9期526-531,共6页Computer Simulation

基　　金：教育部产学合作协同育人项目(221005812103741)。

摘　　要：随着信息传播数据量的飞速增长,以及低时延网络的广泛应用,低时延网络无冲突调度的需求也进一步增加。为解决当前通信调度算法时效性差、调度效果不佳等问题,提出了基于强化学习演员-评论家算法的无冲突调度模型。模型首先对低时延网络架构进行分析,并对状态空间中的多路通信网络进行数据监测;然后基于受限制的马尔科夫决策对其进行无冲突调度,并基于贝尔曼方程计算结果进行策略调度;最后使用强化学习中的Actor-Critic算法对调度算法进行优化,通过Actor提出调度方案,通过Critic评价调度情况,进而达到无冲突调度的效果。实验结果表明,所提算法将冲突率降低了8.72%,将吞吐率提升了7.58%,降低了数据在传输过程中丢失和乱码的概率,提高了通信数据的传输效率。With the rapid growth of information dissemination data and the widespread application of low-latency networks,the demand for conflict-free scheduling for low-latency networks has further increased.In order to address the issues of poor effectiveness and scheduling effectiveness of current communication scheduling algorithms,this paper proposes a conflict-free scheduling model based on the Actor-Critic algorithm in reinforcement learning.This model first analyzes the low-latency network architecture and monitors the data of multi-channel communication networks in the state space.Then,based on the constrained Markov decision,conflict-free scheduling is performed,and the scheduling strategy is calculated based on the Bellman equation.Finally,the Actor-Critical algorithm in reinforcement learning is used to optimize the scheduling algorithm.The Actor is used to propose a scheduling plan,and Critical is used to evaluate the scheduling situation,thereby achieving the effect of conflict-free scheduling.The experimental results show that the algorithm proposed in this article reduces the conflict rate by 8.72%,increases the throughput rate by 7.58%,reduces the probability of data loss and garbled code during transmission,and improves the transmission efficiency of communication data.

关 键 词：无冲突调度 低时延网络 强化学习 多路通信数据 

分 类 号：TP301.6[自动化与计算机技术—计算机系统结构]