PIC: Practical Internet Coordinates for Distance Estimation
ICDCS '04 Proceedings of the 24th International Conference on Distributed Computing Systems (ICDCS'04)
Vivaldi: a decentralized network coordinate system
Proceedings of the 2004 conference on Applications, technologies, architectures, and protocols for computer communications
Reinforcement Learning for Autonomic Network Repair
ICAC '04 Proceedings of the First International Conference on Autonomic Computing
Meridian: a lightweight network location service without virtual coordinates
Proceedings of the 2005 conference on Applications, technologies, architectures, and protocols for computer communications
On suitability of Euclidean embedding of internet hosts
SIGMETRICS '06/Performance '06 Proceedings of the joint international conference on Measurement and modeling of computer systems
Machine-learnt versus analytical models of TCP throughput
Computer Networks: The International Journal of Computer and Telecommunications Networking
A network positioning system for the internet
ATEC '04 Proceedings of the annual conference on USENIX Annual Technical Conference
Towards network triangle inequality violation aware distributed systems
Proceedings of the 7th ACM SIGCOMM conference on Internet measurement
NETWORKING'08 Proceedings of the 7th international IFIP-TC6 networking conference on AdHoc and sensor networks, wireless networks, next generation internet
Internet routing policies and round-trip-times
PAM'05 Proceedings of the 6th international conference on Passive and Active Network Measurement
Finding routing shortcuts using an internet coordinate system
IWSOS'11 Proceedings of the 5th international conference on Self-organizing systems
The impact of triangular inequality violations on medoid-based clustering
ISMIS'11 Proceedings of the 19th international conference on Foundations of intelligent systems
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Internet Coordinates Systems (ICS) are used to predict Internet distances with limited measurements. However the precision of an ICS is degraded by the presence of Triangle Inequality Violations (TIVs). Simple methods have been proposed to detect TIVs, based e.g. on the empirical observation that a TIV is more likely when the distance is underestimated by the coordinates. In this paper, we apply supervised machine learning techniques to try and derive more powerful criteria to detect TIVs. We first show that (ensembles of) Decision Trees (DTs) learnt on our datasets are very good models for this problem. Moreover, our approach brings out a discriminative variable (called OREE ), which combines the classical estimation error with the variance of the estimated distance. This variable alone is as good as an ensemble of DTs, and provides a much simpler criterion. If every node of the ICS sorts its neighbours according to OREE , we show that cutting these lists after a given number of neighbours, or when OREE crosses a given threshold value, achieves very good performance to detect TIVs.