Communications of the ACM
The anatomy of a large-scale hypertextual Web search engine
WWW7 Proceedings of the seventh international conference on World Wide Web 7
King: estimating latency between arbitrary internet end hosts
Proceedings of the 2nd ACM SIGCOMM Workshop on Internet measurment
Vivaldi: a decentralized network coordinate system
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
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Phoenix: Towards an Accurate, Practical and Decentralized Network Coordinate System
NETWORKING '09 Proceedings of the 8th International IFIP-TC 6 Networking Conference
Matchmaking for online games and other latency-sensitive P2P systems
Proceedings of the ACM SIGCOMM 2009 conference on Data communication
Network distance prediction based on decentralized matrix factorization
NETWORKING'10 Proceedings of the 9th IFIP TC 6 international conference on Networking
IDES: An Internet Distance Estimation Service for Large Networks
IEEE Journal on Selected Areas in Communications
| Hi-index | 0.00 |
Network Coordinate System (NCS) is an efficient and scalable mechanism to predict latency between any two network hosts based on historical measurements. Most NCS models, such as metric space embedding based, like Vivaldi, and matrix factorization based, like DMF and Phoenix, use squared error measure in training which suffers from the erroneous records, i.e. the records with large noise. To overcome this drawback, we introduce an elegant error measure, the Huber norm to network latency prediction. The Huber norm shows its robustness to the large data noise while remaining efficiency of optimization. Based on that, we upgrade the traditional NCS models into more robust versions, namely Robust Vivaldi model and Robust Matrix Factorization model. We conduct extensive experiments to compare the proposed models with traditional ones and the results show that our approaches significantly increase the accuracy of network latency prediction.