Research On Transmission Behavior Of Internet Under Macroscopic Topology

Internet is a classical instance of complex system, the analysis on its characteristic behavior has become a hot issue at present. Recently, network measurements have been widely carried out, which promotes the researches of Internet behavior to be changed from the early hypothetical phase to subjective data analysis phase. However, the increasing scale and complexity of Internet and its topological structure reveal the shortcomings of the present measurement technology. Facing the huge and complex Internet, the traditional research methods which emphasize partial characteristic and optimize control hinder people's understanding of the Internet behaviors from a macroscopic perspective, and hinder the further development of Internet as well. To view Internet as a correlated single unit and carry out the network measurement macroscopically further demonstrate the behavioral characteristics and evolvement trend of Internet under the macro-topological structure. This in turn optimizes the Internet service and resources allotment, and provides valuable solution for the security and design of large-scale network.In order to meet the needs of network development, this dissertation uses complex network theory to analyze the network transmission behavior on the base of the giant sample data authorized by CAIDA. Since the transmission behavior of Internet has its irreplaceable typical character in the aspect of indicating the overall behavioral characteristic of network, this dissertation analyzes the overall behavioral characteristic of it within one measurement cycle with space the main clue, and forecasts its overall evolvement trend of a long period with time the main clue. The purpose is to reveal Internet transmission behavior under macroscopic topology. This research is carried out from three aspects:the collection of sample data, the statistic of network characteristic property and the forecast of network evolvement.This dissertation first uses the data from the CAIDA Skitter monitors to obtain the valid samples fitted for statistic analysis. Then two physical properties-traveling time and traveling diameter which can indicate network transmission behavior are defined on the IP level on basis of the transmission characteristic of Internet and the physical characteristic property in the complex network. Then the sample data is analyzed from both the overall and partial perspectives. It is found that the traveling times of similar traveling diameters differ a lot. The result shows that the Pearson coefficient between traveling time and traveling diameter is 0.346, which indicates that there is a low correlation between the two parameters. In order to explain the reason why the influence of traveling diameter to traveling time is not significant, this dissertation proposes a revising algorithm focusing on the dynamic complexity of network and gets link delay from the giant sample data. The statistical analysis of link delay sample data shows that the biggest link delay of more than 90% paths takes more than 1/4 of the traveling time, and the dominating delay of IP level is defined on this theory.Second, the dissertation further studies the behavior characteristic of dominant delay, because of its great influence on network transmission behavior. The detailed investigation of dominant delay reveals that the ratios of the dominant delay to the traveling time are similar among paths whose traveling time varies greatly. It indicates that there is no necessary relation between the dominant delay's influence and traveling time. But because the dominant delays themselves differ greatly on numerical values, it causes great difference among the similar diameter's traveling time and this is directly manifested in the multi-modal distribution of Internet traveling time. Then, a further analysis of dominant delay on AS autonomous domain is taken and it explains the reason why dominant delays seldom occur between the AS autonomous domains. By mapping the nodes from IP level to AS autonomous domain, the dissertation analyzes the transmission behavior of AS autonomous domains on topological structure and discovers that dominant delay tends to appear inside the AS autonomous domain. In addition, the dissertation discusses the main reason which causes the dominant delay by mapping the IP node to its geographical location. The results show that the length of the linking path mainly affects the scale of the dominant delay.Finally, this dissertation describes the evolving trend of transmission behavior of the whole network from great time scale. The definition of traveling time based on evolution was provided and the stability of evolving samples was proved. Basing on this, the saturated correlative dimension of chaotic attractor of Internet traveling time with phase space reconstruction and G-P algorithm are calculated, which approves that the evolvement of Internet traveling time has the characteristic of chaos. A revised logistic model with sine and cosine functions was proposed to describe the evolvement state of network transmission behavior. Moreover, particle swarm optimization (PSO) algorithm is adopted for the parameters estimation of the revised model, which is evaluated from the perspective of convergence, fitting accuracy and forecasting accuracy. The result indicates that the structure of the optimized model is reasonable and is able to reflect the movement of network transmission behavior accurately.