On-line learning and stochastic approximations
On-line learning in neural networks
A scalable content-addressable network
Proceedings of the 2001 conference on Applications, technologies, architectures, and protocols for computer communications
End-to-end available bandwidth: measurement methodology, dynamics, and relation with TCP throughput
IEEE/ACM Transactions on Networking (TON)
Virtual landmarks for the internet
Proceedings of the 3rd ACM SIGCOMM conference on Internet measurement
Vivaldi: a decentralized network coordinate system
Proceedings of the 2004 conference on Applications, technologies, architectures, and protocols for computer communications
Damped Newton Algorithms for Matrix Factorization with Missing Data
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Meridian: a lightweight network location service without virtual coordinates
Proceedings of the 2005 conference on Applications, technologies, architectures, and protocols for computer communications
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
S3: a scalable sensing service for monitoring large networked systems
Proceedings of the 2006 SIGCOMM workshop on Internet network management
Internet Measurement: Infrastructure, Traffic and Applications
Internet Measurement: Infrastructure, Traffic and Applications
On the treeness of internet latency and bandwidth
Proceedings of the eleventh international joint conference on Measurement and modeling of computer systems
Network coordinates in the wild
NSDI'07 Proceedings of the 4th USENIX conference on Networked systems design & implementation
Comparison of public end-to-end bandwidth estimation tools on high-speed links
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
IDES: An Internet Distance Estimation Service for Large Networks
IEEE Journal on Selected Areas in Communications
Bandwidth estimation: metrics, measurement techniques, and tools
IEEE Network: The Magazine of Global Internetworking
Future Generation Computer Systems
DMFSGD: a decentralized matrix factorization algorithm for network distance prediction
IEEE/ACM Transactions on Networking (TON)
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In large-scale networks, full-mesh active probing of end-to-end performance metrics is infeasible. Measuring a small set of pairs and predicting the others is more scalable. Under this framework, we formulate the prediction problem as matrix completion, whereby unknown entries of an incomplete matrix of pairwise measurements are to be predicted. This problem can be solved by matrix factorization because performance matrices have a low rank, thanks to the correlations among measurements. Moreover, its resolution can be fully decentralized without actually building matrices nor relying on special landmarks or central servers. In this paper we demonstrate that this approach is also applicable when the performance values are not measured exactly, but are only known to belong to one among some predefined performance classes, such as "good" and "bad". Such classification-based formulation not only fulfills the requirements of many Internet applications but also reduces the measurement cost and enables a unified treatment of various performance metrics. We propose a decentralized approach based on Stochastic Gradient Descent to solve this class-based matrix completion problem. Experiments on various datasets, relative to two kinds of metrics, show the accuracy of the approach, its robustness against erroneous measurements and its usability on peer selection.