基于感知流量算法的复杂网络拥塞问题研究

研究了在具有感知流量的路由策略下，复杂网络的拓扑结构对网络中传输流量的影响.为了描述数据包传输过程的有效性，通过引入一个状态参数，利用由稳态到拥塞的指标流量相变值来刻画网络的吞吐量.基于每个节点的数据包处理能力与该节点的度或介数成比例提出两种模型并进行仿真.仿真结果表明，平均度相同的情况下，模型Ⅰ中，WS小世界网络比ER随机网络和BA无标度网络更容易产生拥塞；模型Ⅱ中，所有网络容量都得到较大的提高，尤其是WS小世界网络.但当网络的基本连接参数改变时，哪种模型更利于网络的流量传输，还要依据网络本身的结构特性 关键词： 复杂网络 无标度网络 感知流量 拥塞

Study on the congestion in complex network based on traffic awareness algorithm

We investigate the influence of complex network topological structure on the traffic delivery by the traffic awareness routing strategy. In order to characterize the efficiency of the packet delivery process, we introduce an order parameter to measure the network capacity by the critical value of phase transition from free flow to congestion. Here, we present two kinds of models, based on its degree or its betweenness that the delivery capacity of each node is proportional to. Simulation results show that, in the case of identical average degree, WS small-world network is significantly more susceptible to traffic congestion than ER random networks and BA scale-free networks in the first model, while the capacities of all kinds of networks are enhanced greatly in the second model, especially for WS small-world network. Finally, it is worth noting that which of the two models will be best suitable for handling traffic delivery depends on the structural characteristics of networks.