一种面向光纤网络路径优化的机器学习改进算法

An improved machine learning algorithm for optical fiber network path optimization

针对光纤网络通信中数据流传输路径质量影响网络资源利用率的问题,提出了一种改进的数据传输路径优化机器学习算法。首先,利用机器学习完成对初始数据的预处理,获取数据特征信息,完成数据流分类。基于对光纤跨度内数据流的分析,构建集群组完成数据路径的调整,实现网络资源的充分利用。其次,以包含特征参数的相似矩阵为约束条件,完成聚类分析的优化。根据数据特征参数建立相似矩阵,并在特征参数与通信路径的数据流类型之间建立函数映射关系。最后利用核函数对传输路径进行优化,实现网络传输路径的优化。实验针对包含多个光纤跨度的网络进行路径优化,并与传统的K-means聚类算法对比。测试中6种不同数据流的比例可以充分反映不同条件下的数据通信状态。实验结果表明:该算法的分类准确率为94.6%,平均执行时间为12.8 s,平均聚类变化度为31.3%。传统的K-means聚类算法分类准确率为84.6%,平均执行时间为20.8 s,平均聚类变化为46.2%。该算法的收敛时间也优于传统算法,其在网络数据传输中具有更高的准确性和实时性。

Aiming at the problem that the quality of the data stream transmission path in optical fiber network communication affected the utilization of network resources,an improved data transmission path optimization machine learning algorithm was proposed.Firstly,the machine learning was used to complete the preprocessing of the initial data,the data feature information was obtained,and the data stream classification was completed.Based on the analysis of the data flow within the optical fiber span,a cluster group was constructed to complete the adjustment of the data path and realize the full use of network resources.Secondly,the optimization of the cluster analysis was completed by taking the similarity matrix containing the characteristic parameters as the constraint condition.The similarity matrix was established according to the data characteristic parameters,and the function mapping relationship was established between the characteristic parameters and the data flow type of the communication path.Finally,the kernel function was used to optimize the transmission path to realize the optimization of the network transmission path.The experiment optimized the path for a network containing multiple fiber spans,and compared it with the traditional K-means clustering algorithm.The ratio of the 6 different data streams in the test can fully reflect the data communication status under different conditions.The experimental results show that the classification accuracy of the algorithm is 94.6%,the average execution time is12.8 s,and the average cluster change degree is 31.3%.The classification accuracy of the traditional K-means clustering algorithm is 84.6%,the average execution time is 20.8 s,and the average clustering change is 46.2%.The convergence time of this algorithm is also better than that of traditional algorithms,and it has higher accuracy and real-time performance in network data transmission.