| 引用本文: | 张慎文,许崇海,胡天乐,陶霜霜,李鲁群.高QoE的低时延智能网络数据传输调度算法[J].哈尔滨工业大学学报,2023,55(5):132.DOI:10.11918/202112138 |
| ZHANG Shenwen,XU Chonghai,HU Tianle,TAO Shuangshuang,LI Luqun.Low-latency intelligent network data transmission scheduling algorithm with high QoE[J].Journal of Harbin Institute of Technology,2023,55(5):132.DOI:10.11918/202112138 |
|
| 摘要: |
| 面向低时延、稳定传输、高用户体验质量(quality of experience,QoE)的网络实时传输需求场景,提出一种低时延智能网络数据传输调度算法。该算法由数据块排队控制策略和拥塞控制策略两部分组成。数据排队控制策略提出了综合数据块的创建时间和有效时限(effective time)的性价比模型,有效地解决了传输时间约束下的信息传输不均衡问题；拥塞控制策略提出了基于使用耿贝尔分布(Gumbel distribution)采样重参数化与混合经验优先级模型改进后的深度确定性策略梯度(deep deterministic policy gradient,DDPG)方法,解决了深度确定性策略梯度不适用于离散网络动作空间拥塞控制的问题,并通过学习自适应调整发送参数显著提升了网络拥塞控制质量。实验结果表明,实时传输场景下使用本文提出的排队算法能够有效提升QoE,采用改进后的DDPG进行拥塞控制能大幅降低传输时延。同样场景下,将提出的智能网络数据传输调度算法与排队策略及拥塞控制策略相结合,与传统的网络数据传输调度算法相比,能够更好地兼顾低时延和稳定传输,提供更高的数据传输质量。 |
| 关键词: 拥塞控制 低时延 排队算法 深度确定性策略梯度 体验质量（QoE） |
| DOI：10.11918/202112138 |
| 分类号:TP393 |
| 文献标识码:A |
| 基金项目:2021年高水平地方高校建设一流研究生教育子项目(209-AC9103-21-368012452) |
|
| Low-latency intelligent network data transmission scheduling algorithm with high QoE |
|
ZHANG Shenwen,XU Chonghai,HU Tianle,TAO Shuangshuang,LI Luqun
|
|
(The College of Information, Mechanical and Electrical Engineering, Shanghai Normal University, Shanghai 201418, China)
|
| Abstract: |
| This paper proposes a low-latency intelligent network data transmission scheduling algorithm for real-time network transmission demand scenarios of low latency, stable transmission, and high quality of experience (QoE). The algorithm consists of two parts: data block queuing control strategy and congestion control strategy. The data block queuing control strategy presents a cost-effective model that integrates the creation time and effective time of data blocks, effectively solving the problem of uneven information transmission under transmission time constraint. The congestion control strategy proposes a deep deterministic policy gradient (DDPG) method based on the Gumbel distribution sampling reparameterization with mixed experience prioritization model, which solves the problem that DDPG is not applicable to the congestion control of discrete network action space and significantly improves the quality of network congestion control by adaptively adjusting the sending parameters through learning. Results show that the proposed queuing algorithm could effectively improve QoE in real-time transmission scenarios, and the improved DDPG for congestion control could significantly reduce transmission delay. In the same scenario, compared with traditional network data transmission scheduling algorithms, by integrating the proposed queuing and congestion control strategies, the improved intelligent network data transmission scheduling algorithm could maintain a good balance between low latency and stable transmission and provide higher data transmission quality. |
| Key words: congestion control low latency queuing algorithm deep deterministic policy gradient quality of experience(QoE) |
