Estimation of network distances using off-line measurements

Several emerging large-scale Internet applications such as Content Distribution Networks, and Peer-to-Peer networks could potentially benefit from knowing the underlying Internet topology and the distances (i.e., round-trip-time) between different hosts. Most existing techniques for distance estimation either use a dedicated infrastructure or use on-line measurements in which probe packets are injected into the network during estimation. Our goal in this paper is to study off-line techniques for distance estimation that do not require a dedicated infrastructure. To this end, we propose a metric termed “depth” and we observe that together with a quadratic function on the geographic distance, it can predict the network distance with high accuracy using multi-variable regression. When used for closest server selection, our approach performs much better than random server selection, and similar to the on-line metrics. Our approach incurs low overhead and can be deployed easily with some DNS extensions.