基于hops的Internet复杂网络分割度分析

首先讨论了相同分割度对不同网络传播行为的影响,并以小世界网络为例讨论了不同分割度对相同网络传播行为的影响;定义了基于hops的Internet分割度,根据CAIDA提供的全球范围30个主要monitor5年以来采集的数据,对Internethops进行统计分析,并计算出Internet分割度,与国际该领域研究成果进行相比,更进一步揭示了Internet小世界效应的规律;针对hops与RTT进行了10种模型的回归分析,得出hops与RTT更符合幂函数关系及其关键参数,从统计上证明了Internet复杂网络传播行为的幂率关系,并以此建立了Internet分割度时间敏感性模型(ISTSDM);建立了针对Internet分割度的时间序列随机过程模型(MTSSPISD),并以此讨论了Internet分割度的时间演化规律;最后利用模型ISTSDM和模型MTSSPISD对2008年北京奥运期间Internet分割度和IP层数据平均传播时间进行了预测。

Internet complex network separation degree analysis based on hops

Due to the fact that in dynamic wide-area optical backbone network the accuracies of the existing prediction methods were insufficient,a novel prediction method on quality of transmission (QoT) of optical channel was proposed based on ensemble learning theory.Firstly,under the framework of stacked ensemble learning,a base-learner including five multilayer perceptron (MLP) model was built,which could achieve homomorphic ensemble learning of sample data through parallel combination.Subsequently,the new training set fused from the predicted results of the preceding base-learner was used to training the meta-learner composed of a single MLP.The simulation results show that compared with the used deep neural network,the proposed method can obtain a more excellent nonlinear approximation in the scenarios of the single-channel and multi-channels,and the prediction accuracies have the improvements of 1.93% and 3.82% respectively.