基于数据挖掘的光传送网路由波长优化研究  

Research on wavelength optimization of optical transport network routing based on data mining

作　　者：刘全智 武爽 LIU Quanzhi;WU Shuang(Department of Mathematics and Statistics,Cangzhou Normal University,Cangzhou Hebei 061001,China)

机构地区：[1]沧州师范学院数学与统计学院,河北沧州061001

出　　处：《激光杂志》2025年第2期193-197,共5页Laser Journal

基　　金：河北省高等学校自然科学研究项目(No.ZC2023032)。

摘　　要：为解决当前光传送网面临的日益增长的数据传输需求与有限的网络资源之间的矛盾,设计基于数据挖掘的光传送网路由波长优化方法。构建分层图模型,将物理链路中的波长资源映射为图形结构中的连接线,在同一框架内解决路由和波长分配问题,利用遗传算法设计网络负载动态适应算法,实现光传送网网络负载动态适应优化,从而实现路由、波长的共同优化。实验测试结果表明,该方法在整个测试过程中都保持较低的阻塞指数,随着业务数的增加,阻塞指数上升速度慢,资源利用指数在业务数初期增长快,随后逐渐平稳,最终达到接近0.7的资源利用指数。In order to solve the contradiction between the increasing data transmission demand and limited network resources faced by the current optical transmission network,a wavelength optimization method for optical transmission network routing based on data mining is designed.Build a hierarchical graph model to map wavelength resources in physical links to connection lines in a graphical structure,and solve routing and wavelength allocation problems within the same framework.Use genetic algorithms to design network load dynamic adaptation algorithms to achieve dynamic load adaptation optimization in optical transport networks,thereby achieving joint optimization of routing and wave-length.The experimental test results show that the method maintains a low blocking index throughout the entire testing process.As the number of services increases,the blocking index rises slowly,while the resource utilization index in-creases rapidly in the early stages of service,gradually stabilizes,and finally reaches a resource utilization index close to 0.7.

关 键 词：数据挖掘 用户识别 光传送网 分层图模型 路由波长优化 

分 类 号：TN929.1[电子电信—通信与信息系统]