IP地理定位的时延-距离相关性研究
Delay-Distance Correlation Study for IP Geolocation
摘要：
虽然许多经典的IP定位算法都适用于富连接网络，但在时延-距离相关性弱的贫连接网络中，其性能受到严重影响。本文试图通过在弱连通网络中寻找富连通子网络来提高经典IP定位算法的性能。首先，提出了一种新的时延-距离相关模型（RTD-Corr模型）。建立了时延-距离相关性与网络路径曲折度、传播延迟比、直达地理距离等实际网络因素之间的关系。其次，基于RTD-Corr模型，结合实际网络的特点，讨论了如何在中国互联网这一典型的实际贫连接网络中发现富连接网络。然后通过对中国互联网三大运营商、三十个省的大规模网络测量，发现了中国互联网的富连接子网。最后，基于所建立的富连通子网，对两种经典的IP定位算法进行了改进，并在中国互联网上进行了实验，结果表明改进后的定位精度有了显著提高。
英文摘要：
Although many classical IP geolocation algorithms are suitable to rich-connected networks, their performances are seriously affected in poor-connected networks with weak delay-distance correlation. This paper tries to improve the performances of classical IP geolocation algorithms by finding rich-connected sub-networks inside poor-connected networks. First, a new delay-distance correlation model (RTD-Corr model) is proposed. It builds the relationship between delay-distance correlation and actual network factors such as the tortuosity of the network path, the ratio of propagation delay and the direct geographical distance. Second, based on the RTD-Corr model and actual network characteristics, this paper discusses about how to find rich-connected networks inside China Internet which is a typical actual poor-connected network. Then we find rich-connected sub-networks of China Internet through a large-scale network measurement which covers three major ISPs and thirty provinces. At last, based on the founded rich-connected sub-networks, we modify two classical IP geolocation algorithms and the experiments in China Internet show that their accuracy is significantly increased.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
