南京大学学报(自然科学版) ›› 2019, Vol. 55 ›› Issue (5): 725–732.doi: 10.13232/j.cnki.jnju.2019.05.003

• • 上一篇    下一篇

考虑邻接点贡献的通信网关键节点评估方法

伊小素,冀羽(),曾华菘,熊瑞   

  1. 北京航空航天大学仪器科学与光电工程学院,北京,100191
  • 收稿日期:2019-05-16 出版日期:2019-09-30 发布日期:2019-11-01
  • 通讯作者: 冀羽 E-mail:newbeil@126.com

Evaluation method of key nodes in communication networkconsidering contribution of adjacent nodes

Xiaosu Yi,Yu Ji(),Huasong Zeng,Rui Xiong   

  1. School of Instrumentation Science and Opto?electronics Engineering,Beihang University,Beijing,100191,China
  • Received:2019-05-16 Online:2019-09-30 Published:2019-11-01
  • Contact: Yu Ji E-mail:newbeil@126.com

摘要:

通信网络中的某些节点对整个网络具有重要的作用.关键节点的失效可能导致整个通信网络的性能急速下降甚至瘫痪.为确定通信网络中的关键节点,提出一种考虑了邻接点贡献的通信网关键节点评估方法,该方法同时考虑了节点的多方面属性以及其邻域内节点的影响.利用该方法对ARPANET(Advanced Research Projects Agency Network)进行了节点重要性的评估,并通过网络可靠性的相关理论和OPNET仿真建模工具对其进行了验证,证明了该方法的准确性.同时还发现,对通信网络的关键节点的冗余部署可以保障其网络性能,增加可靠性.

关键词: 通信网络, 关键节点, 节点重要度, 贡献矩阵, 邻 域

Abstract:

Key nodes of communication networks are important. The failure of key nodes may lead to a drastic drop of the network performance or even network crash. In order to find the key node in the communication network,a node importance evaluation method based on node contribution degree and contribution matrix was proposed. This method took into account the multi?attributes of nodes and also the influence of nodes within their adjacent area. The test result shows that this method provides reasonable result compared with other methods when used in the Advanced Research Projects Agency Network (ARPANET). This method was also verified with the network reliability theory and a simulation was run in the OPNET Modeler to verify this method. Finally the simulation results also discover that backup of the key nodes is of importance to the communication network’s performance and reliability.

Key words: communication network, key node, node importance, contribution matrix, adjacent area

中图分类号: 

  • TN915.02

图1

ARPANET拓扑结构"

表1

不同算法的节点重要度计算结果"

节点本文方法文献[5]文献[8]文献[4]文献[6]
V10.17470.15280.62620.03550.3808
V20.24700.29870.97210.17700.8538
V30.26280.29840.99300.45261.0000
V40.24800.15620.83870.29480.5000
V50.21470.10900.83870.27760.3192
V60.21290.12610.98360.32940.4038
V70.19770.09350.87970.18990.1269
V80.16700.06340.87970.14580.0115
V90.16530.06240.87970.13990.0000
V100.18610.06800.87970.19210.0808
V110.23460.10620.87970.26840.2654
V120.25990.18150.97800.43930.6385
V130.23810.18390.80510.24660.5500
V140.21330.23690.98640.26250.8423
V150.19990.25220.87870.10180.6654
V160.19390.19780.66390.07080.5115
V170.22730.22140.69770.15300.6154
V180.24560.19700.77010.23830.6077
V190.24570.18450.96710.36300.6615
V200.20980.11150.82790.17340.3269
V210.19680.10230.82790.16270.2154

表2

失效带来的可靠性下降"

节 点V3V12V4
可靠性下降25.0%22.1%16.5%

表3

冗余带来的可靠性提升"

节 点V3V12V4
可靠性上升3.2%2.8%1.3%

图2

有两个邻接点的节点层模型结构(OPNET模型示意)"

图3

节点内部MAC模块的进程模型结构(OPNET模型示意)"

图4

节点的失效"

表4

单节点失效下的吞吐下降(前三名)"

节 点V3V12V4
吞吐量下降25.1%20.6%16.1%

表5

随机失效下的吞吐下降(前三名)"

节 点V3V12V4
吞吐量下降29.0%21.8%17.5%
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