Network measurement based modeling and optimization for IP geolocation

Abstract

IP geolocation plays a critical role in location-aware network services and network security applications. Commercially deployed IP geolocation databases may provide outdated or incorrect location of Internet hosts due to slow record updates and dynamic IP address assignment by the ISPs. Measurement-based IP geolocation is used to provide real time location estimation of Internet hosts based on network delays. This paper proposes a measurement-based IP geolocation framework that provides location estimation of an Internet host in real time. The proposed frame work models the relationship between measured network delays and geographic distances using segmented polynomial regression model and semidefinite programming for optimization. Weighted and non-weighted schemes are evaluated for location estimation. The proposed framework shows close to 17 and 26 miles median estimation error for nodes in North America and Europe, respectively. The proposed schemes achieve 70–80% improvement in median estimation error comparing to the first order regression approach for experimental data collected from Planet-Lab.