Network tomography on general topologies
SIGMETRICS '02 Proceedings of the 2002 ACM SIGMETRICS international conference on Measurement and modeling of computer systems
Analysis of network congestion inference techniques
ACM SIGMETRICS Performance Evaluation Review - Special issue on the First ACM SIGMETRICS Workshop on Large Scale Network Inference (LSNI 2005)
Estimating network proximity and latency
ACM SIGCOMM Computer Communication Review
One-way delay estimation using network-wide measurements
IEEE/ACM Transactions on Networking (TON) - Special issue on networking and information theory
Estimating the evolution of categorized web page populations
ICWE '06 Workshop proceedings of the sixth international conference on Web engineering
Online linear optimization and adaptive routing
Journal of Computer and System Sciences
Estimating the size and evolution of categorised topics in web directories
Web Intelligence and Agent Systems
Properties and Evolution of Internet Traffic Networks from Anonymized Flow Data
ACM Transactions on Internet Technology (TOIT)
Network link tomography and compressive sensing
ACM SIGMETRICS Performance Evaluation Review - Performance evaluation review
On identifying additive link metrics using linearly independent cycles and paths
IEEE/ACM Transactions on Networking (TON)
Cell-graph coloring for cancerous tissue modelling and classification
Multimedia Tools and Applications
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Distance estimation is important to many Internet applications. It can aid a World Wide Web client when selecting among several potential candidate servers or among candidate peer-to-peer servers. It can also aid in building efficient overlay or peer-to-peer networks that react dynamically to changes in the underlying Internet. One of the approaches to distance (i.e., time delay) estimation in the Internet is based on placing tracer stations in key locations and conducting measurements between them. The tracers construct an approximated map of the Internet after processing the information obtained from these measurements. This work presents a novel algorithm, based on algebraic tools, that computes additional distances, which are not explicitly measured. As such, the algorithm extracts more information from the same amount of measurement data. Our algorithm has several practical impacts. First, it can reduce the number of tracers and measurements without sacrificing information. Second, our algorithm is able to compute distance estimates between locations where tracers cannot be placed. To evaluate the algorithm's performance, we tested it both on randomly generated topologies and on real Internet measurements. Our results show that the algorithm computes up to 50%-200% additional distances beyond the basic tracer-to-tracer measurements.