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On-line learning and stochastic approximations
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A scalable content-addressable network
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King: estimating latency between arbitrary internet end hosts
Proceedings of the 2nd ACM SIGCOMM Workshop on Internet measurment
Virtual landmarks for the internet
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Vivaldi: a decentralized network coordinate system
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Proceedings of the 2004 conference on Applications, technologies, architectures, and protocols for computer communications
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Damped Newton Algorithms for Matrix Factorization with Missing Data
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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
A distributed hash table
Internet Measurement: Infrastructure, Traffic and Applications
Internet Measurement: Infrastructure, Traffic and Applications
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IMC '05 Proceedings of the 5th ACM SIGCOMM conference on Internet Measurement
OASIS: anycast for any service
NSDI'06 Proceedings of the 3rd conference on Networked Systems Design & Implementation - Volume 3
Projected Gradient Methods for Nonnegative Matrix Factorization
Neural Computation
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Proceedings of the 7th ACM SIGCOMM conference on Internet measurement
Distributed algorithms for stable and secure network coordinates
Proceedings of the 8th ACM SIGCOMM conference on Internet measurement
Scalable Collaborative Filtering Approaches for Large Recommender Systems
The Journal of Machine Learning Research
Exact Matrix Completion via Convex Optimization
Foundations of Computational Mathematics
Matrix completion from a few entries
ISIT'09 Proceedings of the 2009 IEEE international conference on Symposium on Information Theory - Volume 1
A measurement study of internet delay asymmetry
PAM'08 Proceedings of the 9th international conference on Passive and active network measurement
Network coordinates in the wild
NSDI'07 Proceedings of the 4th USENIX conference on Networked systems design & implementation
Decentralized prediction of end-to-end network performance classes
Proceedings of the Seventh COnference on emerging Networking EXperiments and Technologies
Internet routing policies and round-trip-times
PAM'05 Proceedings of the 6th international conference on Passive and Active Network Measurement
Network distance prediction based on decentralized matrix factorization
NETWORKING'10 Proceedings of the 9th IFIP TC 6 international conference on Networking
A Survey on Network Coordinates Systems, Design, and Security
IEEE Communications Surveys & Tutorials
IEEE Journal on Selected Areas in Communications
IDES: An Internet Distance Estimation Service for Large Networks
IEEE Journal on Selected Areas in Communications
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The knowledge of end-to-end network distances is essential to many Internet applications. As active probing of all pairwise distances is infeasible in large-scale networks, a natural idea is to measure a few pairs and to predict the other ones without actually measuring them. This paper formulates the prediction problem as matrix completion where the unknown entries in a pairwise distance matrix constructed from a network are to be predicted. By assuming that the distance matrix has low-rank characteristics, the problem is solvable by low-rank approximation based on matrix factorization. The new formulation circumvents the well-known drawbacks of existing approaches based on Euclidean embedding. A new algorithm, so-called Decentralized Matrix Factorization by Stochastic Gradient Descent (DMFSGD), is proposed. By letting network nodes exchange messages with each other, the algorithm is fully decentralized and only requires each node to collect and to process local measurements, with neither explicit matrix constructions nor special nodes such as landmarks and central servers. In addition, we compared comprehensively matrix factorization and Euclidean embedding to demonstrate the suitability of the former on network distance prediction. We further studied the incorporation of a robust loss function and of nonnegativity constraints. Extensive experiments on various publicly available datasets of network delays show not only the scalability and the accuracy of our approach, but also its usability in real Internet applications.