Abstract
Large-scale Internet applications can benefit from an ability to predict round-trip times to other hosts without having to contact them first. In various predictable model, network coordinates system is an efficient mechanism for internet distance prediction with limited measurements. In this paper, we identify a coordinate matrix which consists of measured value between benchmark node and other common node, so convert computation between nodes into question of factorizing coordinate matrix. We present an algorithm of matrix factorization which factorize coordinate a matrix into non-negative matrix U,V. Through the factorization of the matrix greatly reduces the dimension from the calculation, make for fast convergence of the prediction algorithm.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
We’re sorry, something doesn't seem to be working properly.
Please try refreshing the page. If that doesn't work, please contact support so we can address the problem.
References
Ng, T.S., Zhang, H.: Predicting Internet network distance with coordinates-based approaches. In: Proc. of the IEEE INFOCOM. IEEE Press, Piscataway (2002)
Dabek, F., Cox, R., Kaashoek, F., Morris, R.: Vivaldi: A decentralized network coordinate system. In: Proc. of the ACM SIGCOMM, pp. 15–26. ACM Press, New York (2004)
Francis, P., Jamin, S., Jin, C., Jin, Y., Raz, D., Shavitt, Y., Zhang, L.: IDMaps: A global internet host distance estimation service. IEEE/ACM Trans. on Networking 9(5), 525–540 (2001)
Eades, P., Lin, X.: Spring algorithm and symmetry. Theoretical Computer Science, 379–405 (2000)
Guyton, J.D., Schwartz, M.F.: Locating nearby copies of replicated Internet servers. In: Proc. of the ACM SIGCOMM, pp. 288–298. ACM Press, New York (1995)
Leed, D., Seungh, S.: Learning the parts of objects by non-negative matrix factorization. Nature 401(6755), 788–791 (1999)
Leed, D., Seungh, S.: Algorithms for non2negative matrix factorization. In: Proc. of Neural Information Processing Systems, pp. 556–562 (2000)
Inderjit, S., Dharmendra, S.: Concept decompositions for large sparse text using clustering. Machine Learning 42(1), 143–175 (2001)
Wild, S., Curry, J., Dougherty, A.: Improving non-negative matrix factorizations through structured initialization. Pattern Recognition 37(11), 2217–2232 (2004)
Wang, Y., Jia, Y., Hu, C., et al.: Fisher non2negative matrix factorization for learning local features. In: Proc. of Asian Conf. on Comp. Vision, Jeju Island, Korea (2004)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Chen, S., Li, Y., Pen, M., Zhang, R. (2011). A Multi-dimensional Coordinate Factorization Algorithm for Network Distance Prediction. In: Qi, L. (eds) Information and Automation. ISIA 2010. Communications in Computer and Information Science, vol 86. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-19853-3_5
Download citation
DOI: https://doi.org/10.1007/978-3-642-19853-3_5
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-19852-6
Online ISBN: 978-3-642-19853-3
eBook Packages: Computer ScienceComputer Science (R0)