基于机器学习的OTN业务时延估算方法研究  

Research on OTN Service Delay Estimation Method based on Machine Learning

作　　者：杨刚刚 邵珠贵 姜先荣 YANG Ganggang;SHAO Zhugui;JIANG Xianrong(Research Institute of China Telecom Co.,Ltd.,Beijing 102209,China)

机构地区：[1]中国电信股份有限公司研究院,北京102209

出　　处：《光通信研究》2024年第6期12-17,共6页Study on Optical Communications

摘　　要：【目的】为满足时延敏感型应用场景对时延数据的时效性、准确性和完整性要求,需要实现光传送网络(OTN)端到端业务时延估算。【方法】文章分析了OTN业务的传输特点,根据子网连接采集业务路由信息,并将业务路由中的网元(NE)、链路和交叉等基础数据离散化,得到时延估算的特征变量,提出了基于工程现网数据的时延估算模型,并采用多种机器学习算法进行了仿真比较。【结果】基于支持向量机回归(SVR)和决策树回归的时延预测结果的平均绝对百分比误差(MAPE)分别为3.3628%和1.2849%。【结论】文章基于机器学习、结合OTN业务传输特点提出的OTN业务时延估算方法准确性高,具有广泛的应用场景。【Objective】To meet the requirements of timeliness,accuracy,and completeness of delay data in delay-sensitive application scenarios,it is necessary to implement end-to-end service delay estimation in Optical Transport Networks(OTN).【Methods】This paper first analyzes the transmission characteristics of OTN services,and collects service routing information according to the sub-net connections.Next,it discretizes the basic data such as Network Elements(NE),links,and cross-connection in service route.Then the characteristic variables for delay estimation are obtained.Finally,the paper proposes a delay estimation model based on engineering live network,and compares the simulation results of various machine learning algorithms.【Results】The Mean Absolute Percentage Errors(MAPE)of the delay estimation results based on Support Vector Regression(SVR)and decision tree regression were 3.3628%and 1.2849%,respectively.【Conclusion】The OTN service delay estimation method based on machine learning and the characteristic of OTN transmission in this paper has high accuracy and wide application scenarios.

关 键 词：光传送网络 时延估算 机器学习 

分 类 号：TP393[自动化与计算机技术—计算机应用技术]