基于SDN与机器学习算法的通信网络传输流量调度  

Traffic Scheduling of Communication Network Transmission Based on SDN and Machine Learning Algorithm

作　　者：曹宇 CAO Yu(Jiangsu Branch of China Mobile Communications Group Co.,Ltd.,Nanjing 210000,China)

机构地区：[1]中国移动通信集团江苏有限公司,江苏南京210000

出　　处：《通信电源技术》2025年第6期13-15,共3页Telecom Power Technology

摘　　要：针对通信网络传输流量调度的难题,创新性地提出结合软件定义网络(Software Defined Network,SDN)与机器学习算法的调度方案,借助SDN控制器的强大功能,全面采集了网络数据层的关键信息。利用先进的机器学习算法,深入分析这些数据,准确预测了未来的网络流量走势。基于这些精准的预测结果,制定了细致入微的流量调度策略,从而实现了网络流量的动态优化和高效管理。实验数据充分证明,与传统方法相比,所提出的方法在降低网络丢包率、提升资源利用率及传输性能等方面均表现出显著优势。这一创新成果不仅有效增强了网络流量的稳定性和规律性,还为通信网络传输流量调度领域开辟了新的研究路径。Aiming at the problem of communication network transmission traffic scheduling,a scheduling scheme combining Software Defined Network(SDN)and machine learning algorithm is innovatively proposed.With the powerful function of SDN controller,the key information of network data layer is collected comprehensively.The advanced machine learning algorithm is used to analyze these data deeply and accurately predict the future network traffic trend.Based on these accurate prediction results,a detailed traffic scheduling strategy is formulated,thus realizing the dynamic optimization and efficient management of network traffic.Experimental data fully prove that compared with traditional methods,the proposed method has obvious advantages in reducing network packet loss rate,improving resource utilization and transmission performance.This innovation not only effectively enhances the stability and regularity of network traffic,but also opens up a new research path for the field of communication network transmission traffic scheduling.

关 键 词：软件定义网络(SDN) 机器学习算法 通信网络 传输流量调度 

分 类 号：TP3[自动化与计算机技术—计算机科学与技术]