Delay-Distance Correlation Study for IP Geolocation

TL;DR

This paper introduces a new delay-distance correlation model and a method to identify rich-connected sub-networks within poor-connected networks, significantly improving IP geolocation accuracy in China.

Contribution

It proposes the RTD-Corr model for delay-distance correlation and a technique to find rich-connected sub-networks, enhancing classical geolocation algorithms in poor-connected networks.

Findings

Significant accuracy improvement in IP geolocation in China Internet.

Effective identification of rich-connected sub-networks within poor-connected networks.

Validation through large-scale measurements across major ISPs and provinces.

Abstract

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.