基于节点加权的网络流量测量点选择算法
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摘要
为了解决现有算法无法根据不同节点对网络流量传输具有不同重要性选择流量测量点的问题,提出了一种基于节点加权的网络流量测量点选择算法。该算法首先通过节点关键度对节点进行权重分配,之后使用节点加权的关联矩阵近似算法计算初始解,最后通过对基本蚁群算法中的信息素初始化以及期望启发信息值计算进行改进形成基于节点加权的蚁群算法,并以此计算问题最终解。实验结果表明,基于节点加权的网络流量测量点选择算法能够在保证链路覆盖率的前提下,优先选择关键度更高的节点。
In order to solve the problem that the existing algorithms cannot select the traffic measurement nodes according to the importance of different nodes to the network traffic transmission, this paper proposes a network traffic measurement nodes selection algorithm based on weighted nodes. It firstly assigns weight to nodes by node criticality,then uses correlation matrix approximation algorithm based on weighted nodes to calculate the initial solution, and finally, the algorithm is improved by pheromone initialization and expected heuristic information value calculation in the basic ant colony algorithm, forming the ant colony algorithm based on weighted nodes to calculate final solution of the problem. The experimental results show that the network traffic measurement nodes selection algorithm based on weighted nodes can prioritize the nodes with higher criticality on the premise of ensuring the link coverage.
In order to solve the problem that the existing algorithms cannot select the traffic measurement nodes according to the importance of different nodes to the network traffic transmission, this paper proposes a network traffic measurement nodes selection algorithm based on weighted nodes. It firstly assigns weight to nodes by node criticality,then uses correlation matrix approximation algorithm based on weighted nodes to calculate the initial solution, and finally, the algorithm is improved by pheromone initialization and expected heuristic information value calculation in the basic ant colony algorithm, forming the ant colony algorithm based on weighted nodes to calculate final solution of the problem. The experimental results show that the network traffic measurement nodes selection algorithm based on weighted nodes can prioritize the nodes with higher criticality on the premise of ensuring the link coverage.
引文
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[12]王芳,李美安,段卫军.基于动态自适应蚁群算法的云计算任务调度[J].计算机应用,2013,33(11):3160-3162,3196.
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[2]SHU Zhaogang,WAN Jiafu,LIN Jiaxiang,et al.Traffic engineering in software-defined networking:measurement and management[J].IEEE access,2016,4:3246-3256.
[3]BREITBART Y,CHAN C Y,GAROFALAKIS M,et al.Efficiently monitoring bandwidth and latency in IPnetworks[C]//Proceedings IEEE INFOCOM 2001.Conference on Computer Communications.Twentieth Annual Joint Conference of the IEEE Computer and Communications Society.Anchorage,AK,USA,2001:933-942.
[4]刘湘辉,殷建平,唐乐乐,等.网络流量的有效测量方法分析[J].软件学报,2003,14(2):300-304.
[5]蒋红艳,林亚平,黄生叶.网络流量有效监测点的设置模型及求解算法研究[J].电子与信息学报,2006,28(4):753-756.
[6]葛洪伟,彭震宇,岳海兵.基于混合优化算法的网络流量有效测量点选择[J].计算机应用研究,2009,26(4):1480-1483,1486.
[7]张荣,金跃辉,杨谈,等.分布式网络测量中测量节点的智能选择算法[J].计算机科学,2015,42(9):70-77,93.
[8]程光,龚俭,丁伟.大规模互联网活动IP流分布研究[J].计算机科学,2003,30(4):43-46.
[9]胡满玉.基于链接关系的有向加权复杂网络关键节点识别技术研究[D].南京:南京理工大学,2012.
[10]卢作维,刘博元,张沛,等.基于AS拓扑结构与网络服务分布的AS节点关键度分析[J].东南大学学报(自然科学版),2017,47(S1):59-64.
[11]SHYU S J,YIN P Y,LIN B M T.An ant colony optimization algorithm for the minimum weight vertex cover problem[J].Annals of operations research,2004,131(1/2/3/4):283-304.
[12]王芳,李美安,段卫军.基于动态自适应蚁群算法的云计算任务调度[J].计算机应用,2013,33(11):3160-3162,3196.
[13]GAO Lixin.On inferring autonomous system relationships in the Internet[J].IEEE/ACM transactions on networking,2001,9(6):733-745.
[14]LUCKIE M,HUFFAKER B,DHAMDHERE A,et al.ASrelationships,customer cones,and validation[C]//Proceedings of 2013 Conference on Internet Measurement Conference.Barcelona,Spain,2013.