IDMaps: a global internet host distance estimation service
IEEE/ACM Transactions on Networking (TON)
Towards global network positioning
IMW '01 Proceedings of the 1st ACM SIGCOMM Workshop on Internet Measurement
On the use and performance of content distribution networks
IMW '01 Proceedings of the 1st ACM SIGCOMM Workshop on Internet Measurement
Globally Distributed Content Delivery
IEEE Internet Computing
Network Measurement as a Cooperative Enterprise
IPTPS '01 Revised Papers from the First International Workshop on Peer-to-Peer Systems
Efficient topology-aware overlay network
ACM SIGCOMM Computer Communication Review
Topology Discovery by Active Probing
SAINT-W '02 Proceedings of the 2002 Symposium on Applications and the Internet (SAINT) Workshops
Server Selection Using Dynamic Path Characterization in Wide-Area Networks
INFOCOM '97 Proceedings of the INFOCOM '97. Sixteenth Annual Joint Conference of the IEEE Computer and Communications Societies. Driving the Information Revolution
Constructing internet coordinate system based on delay measurement
Proceedings of the 3rd ACM SIGCOMM conference on Internet measurement
Virtual landmarks for the internet
Proceedings of the 3rd ACM SIGCOMM conference on Internet measurement
PIC: Practical Internet Coordinates for Distance Estimation
ICDCS '04 Proceedings of the 24th International Conference on Distributed Computing Systems (ICDCS'04)
Constrained mirror placement on the Internet
IEEE Journal on Selected Areas in Communications
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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.