NETWORKING 2012: NETWORKING 2012 pp 331-343 | Cite as
Towards a Robust Framework of Network Coordinate Systems
Abstract
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.
Keywords
Network Coordinate Systems Robust Error Measure Metric Space Embedding Matrix FactorizationReferences
- 1.Harvard data set, http://www.eecs.harvard.edu/~syrah/nc/king/lats.n8.gz
- 2.Meridian data set, http://www.cs.cornell.edu/People/egs/meridian/data.php
- 3.P2psim data set, http://pdos.csail.mit.edu/p2psim/kingdata/
- 4.Agarwal, S., Lorch, J.: Matchmaking for online games and other latency-sensitive p2p systems. ACM SIGCOMM Computer Communication Review 39(4), 315–326 (2009)CrossRefGoogle Scholar
- 5.Barto, A.G., Anandan, P.: Pattern-recognizing stochastic learning automata. IEEE Transactions on Systems, Man, & Cybernetics (1985)Google Scholar
- 6.Brin, S., Page, L.: The anatomy of a large-scale hypertextual web search engine. Computer networks and ISDN systems 30(1-7), 107–117 (1998)CrossRefGoogle Scholar
- 7.Buchanan, A.M., Fitzgibbon, A.W.: Damped newton algorithms for matrix factorization with missing data. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol. 2, pp. 316–322. IEEE (2005)Google Scholar
- 8.Chen, Y., Wang, X., Song, X., Lua, E., Shi, C., Zhao, X., Deng, B., Li, X.: Phoenix: Towards an Accurate, Practical and Decentralized Network Coordinate System. In: Fratta, L., Schulzrinne, H., Takahashi, Y., Spaniol, O. (eds.) NETWORKING 2009. LNCS, vol. 5550, pp. 313–325. Springer, Heidelberg (2009)CrossRefGoogle Scholar
- 9.Dabek, F., Cox, R., Kaashoek, F., Morris, R.: Vivaldi: A decentralized network coordinate system. In: ACM SIGCOMM Computer Communication Review, vol. 34, pp. 15–26. ACM (2004)Google Scholar
- 10.Guitton, A., Symes, W.W.: Robust inversion of seismic data using the huber norm. Geophysics 68(4), 1310 (2003)CrossRefGoogle Scholar
- 11.Gummadi, K., Saroiu, S., Gribble, S.: King: Estimating latency between arbitrary internet end hosts. In: Proceedings of the 2nd ACM SIGCOMM Workshop on Internet Measurment, pp. 5–18. ACM (2002)Google Scholar
- 12.Huber, P.J.: Robust regression: asymptotics, conjectures and monte carlo. The Annals of Statistics 1(5), 799–821 (1973)MathSciNetMATHCrossRefGoogle Scholar
- 13.Ke, Q., Kanade, T.: Robust l1 norm factorization in the presence of outliers and missing data by alternative convex programming. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol. 1, pp. 739–746. IEEE (2005)Google Scholar
- 14.Ledlie, J., Pietzuch, P., Mitzenmacher, M., Seltzer, M.: Wired geometric routing. In: Proc. of IPTPS, Citeseer (2007)Google Scholar
- 15.Liao, Y., Geurts, P., Leduc, G.: Network Distance Prediction Based on Decentralized Matrix Factorization. In: Crovella, M., Feeney, L.M., Rubenstein, D., Raghavan, S.V. (eds.) NETWORKING 2010. LNCS, vol. 6091, pp. 15–26. Springer, Heidelberg (2010)CrossRefGoogle Scholar
- 16.Lumezanu, C., Spring, N.: Playing vivaldi in hyperbolic space (2006)Google Scholar
- 17.Mao, Y., Saul, L.K., Smith, J.M.: Ides: An internet distance estimation service for large networks. IEEE Journal on Selected Areas in Communications 24(12), 2273–2284 (2006)CrossRefGoogle Scholar
- 18.Valiant, L.G.: A theory of the learnable. Communications of the ACM 27(11), 1134–1142 (1984)MATHCrossRefGoogle Scholar