基于大数据告警信息的光网络风险感知路由方法
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摘要
光网络的业务传输安全问题一直是光网络运维管理工作的重点之一。基于光网络大数据告警信息,提出了一套光网络风险感知路由方法。首先,结合故障处理手册,将光网络告警进行定位,计算风险综合值;然后,通过模糊C均值聚类(FCM)方法对数据进行模糊聚类,定位模糊语言项;最后,采用熵权法结合风险综合值计算权重,调用最短路径算法寻路。仿真结果表明:该方法可以基于最近一周光网络实时告警状况,同时结合历史告警状况,感知网络风险,动态调整路由方案,规避风险点,找出源节点与目标节点间的风险最低路径。
The security of optical network service transmission has always been one of the focuses of optical network operation and maintenance management. Based on optical network big data alarm information, a risk perception routing method for optical networks is proposed. Firstly, combined with the fault handling manual, the optical network alarm is positioned to calculate the comprehensive risk value. Secondly, fuzzy C means clustering(FCM) is used to cluster the data and locate fuzzy linguistic items.Finally, the weight is calculated by entropy weight method, and the shortest path algorithm is used to find the way. The simulation results show that the system can sense the network risk according to the real-time alarm status of optical network in the last week,combine with historical warning, dynamically to adjust the routing scheme and avoid the risk points, finally get the lowest risk path between source and destination nodes.
The security of optical network service transmission has always been one of the focuses of optical network operation and maintenance management. Based on optical network big data alarm information, a risk perception routing method for optical networks is proposed. Firstly, combined with the fault handling manual, the optical network alarm is positioned to calculate the comprehensive risk value. Secondly, fuzzy C means clustering(FCM) is used to cluster the data and locate fuzzy linguistic items.Finally, the weight is calculated by entropy weight method, and the shortest path algorithm is used to find the way. The simulation results show that the system can sense the network risk according to the real-time alarm status of optical network in the last week,combine with historical warning, dynamically to adjust the routing scheme and avoid the risk points, finally get the lowest risk path between source and destination nodes.
引文
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[3]刘思峰,胡明礼,FORREST J,等.灰色系统模型研究进展[J].南京航空航天大学学报,2012,29(2):103-111.
[4]丁慧霞,赵百捷,杨储华,等.基于FMECA的电力通信网运行风险评估方法[J].光通信技术,2017,41(5):5-8.
[5]冯萍慧,连一峰,戴英侠,等.基于可靠性理论的分布式系统脆弱性模型[J].软件学报,2006(7):1633-1640.
[6]樊鹤红.光通信网络可靠性评估模型及应用:中国电子学学会.全国第十三次光纤通信暨第十四届集成光学学术会议论文集[C].北京:中国电子学会通信学分会,2007.
[7]何明,权冀川,郑翔,等.基于二元决策图的网络可靠性评估[J].控制与决策,2011,26(1):32-36.
[8]王斐,林杰,黎颖,等.基于大数据的电力行业现场作业风险评估方法研究[J].价值工程,2018,37(33):1-4.
[9]王扬.基于大数据的实验室安全风险评估[J].现代电子技术,2018,41(8):113-115,120.
[10]彭静,卢继平,汪洋,等.广域测量系统通信主干网的风险评估[J].中国电机工程学报,2010,4(30):84-90.
[11]陈丽萍.模糊C-均值聚类的研究[D].河北:燕山大学,2009.
[12]文传军,汪庆淼.广义多变量模糊C均值聚类算法[J].计算机工程与科学,2017,39(11):2153-2160.
[13]中国移动集团.中国移动集团客户网络服务等级协议(SLA)规范2013修订版[S].北京:中国移动有限公司网络部,2013:14.
[14]华为技术有限公司. OptiX2000+系列设备故障处理手册[Z].深圳:华为技术有限公司,2010.
[15]顾兆军,辛倩.熵权神经网络的信息系统安全评估[J].计算机工程与设计,2018,39(7):1856-1860.
[16]熊宁欣,王应明.基于改进模糊熵和证据推理的多属性决策方法[J/OL].计算机应用:1-7[2018-10-19]. http://kns.cnki.net/kcms/detail/51.1307.TP.20180521.1440.008.html.
[17]郭金维,蒲绪强,高祥,等.一种改进的多目标决策指标权重计算方法[J].西安电子科技大学学报,2014,41(6):118-125.
[18] MARTINS E Q V, PASCOAL M M B. A new implementation of Yen'sranking loopless paths algorithm[J]. Quarterly Journal of the Belgian,French and Italian Operations Research Societies, 2003, 1(2):121-133.