基于深度强化学习的数据动态传输策略

Data Dynamic Transmission Strategy Based on Deep Reinforcement Learning

通过分析数据中心数据传输目前存在的问题,利用深度强化学习技术设计了一种基于数据优先级的动态传输策略。该策略模型根据数据的三个特征计算每条数据流的优先级,以缩短数据流的平均等待时间为目标,实现数据的动态传输。与传统算法的对比实验表明,所提出的策略模型能够有效减少数据传输的延迟,提高了数据中心的资源使用效率。

By analyzing the current problems of data transmission in data center,a dynamic transmission policy based on data prioritization is designed using deep reinforcement learning.The model calculates the priority of each data stream based on three characteristics,and aims at reducing the average waiting time to achieve the dynamic transmission.The comparison experiments with traditional algorithms show that the strategy model can effectively reduce the delay of data transmission and improve the efficiency of data center resource usage.