Open Access
Issue
MATEC Web Conf.
Volume 232, 2018
2018 2nd International Conference on Electronic Information Technology and Computer Engineering (EITCE 2018)
Article Number 01037
Number of page(s) 6
Section Network Security System, Neural Network and Data Information
DOI https://doi.org/10.1051/matecconf/201823201037
Published online 19 November 2018
  1. Zhang, R., Tang, C., Hu, Y. C., Fahmy, S., & Lin, X. (2006, April). Impact of the inaccuracy of distance prediction algorithms on internet applications-an analytical and comparative study. In INFOCOM 2006. 25th IEEE International Conference on Computer Communications. Proceedings (pp. 1-12) IEEE. [Google Scholar]
  2. Lua, E. K., Zhou, X., Crowcroft, J., & Van Mieghem, P. (2008). Scalable multicasting with network-aware geometric overlay. Computer Communications, 31(3), 464-488. [CrossRef] [Google Scholar]
  3. Zhao, B. Y., Huang, L., Stribling, J., Rhea, S. C., Joseph, A. D., & Kubiatowicz, J. D. (2004). Tapestry: A resilient global-scale overlay for service deployment. IEEE Journal on selected areas in communications, 22(1), 41-53. [CrossRef] [Google Scholar]
  4. Lian, Q., Zhang, Z., Yang, M., Zhao, B. Y., Dai, Y., & Li, X. (2007, June). An empirical study of collusion behavior in the Maze P2P file-sharing system. In Distributed Computing Systems, 2007. ICDCS’07. 27th International Conference on (pp. 56-56). IEEE. [Google Scholar]
  5. Komosny D, Simek M, Kathiravelu G. (2013) . Can Vivaldi Help in IP Geolocation?. Przegląd Elektrotechniczny, 2013, 89(5): 100-106. [Google Scholar]
  6. Moravek, P., Komosny, D., Burget, R., Sveda, J., Handl, T., & Jarosova, L. (2011). Study and performance of localization methods in IP based networks: Vivaldi algorithm. Journal of Network and Computer Applications, 34(1), 351-367. [CrossRef] [Google Scholar]
  7. Donnet, B., Gueye, B., & Kaafar, M. A. (2010). A survey on network coordinates systems, design, and security. IEEE Communications Surveys & Tutorials, 12(4), 488-503. [CrossRef] [Google Scholar]
  8. Ng, T. E., & Zhang, H. (2002). Predicting Internet network distance with coordinates-based approaches. In INFOCOM 2002. Twenty-First Annual Joint Conference of the IEEE Computer and Communications Societies. Proceedings. IEEE (Vol. 1, pp. 170-179). IEEE. [Google Scholar]
  9. Tang, L., & Crovella, M. (2003, October). Virtual landmarks for the internet. In Proceedings of the 3rd ACM SIGCOMM conference on Internet measurement (pp. 143-152). ACM. [Google Scholar]
  10. Lim, H., Hou, J. C., & Choi, C. H. (2005). Constructing Internet coordinate system based on delay measurement. IEEE/ACM Transactions on Networking (TON), 13(3), 513-525. [CrossRef] [Google Scholar]
  11. Dabek, F., Cox, R., Kaashoek, F., & Morris, R. (2004, August). Vivaldi: A decentralized network coordinate system. In ACM SIGCOMM Computer Communication Review (Vol. 34, No. 4, pp. 15-26). ACM. [CrossRef] [Google Scholar]
  12. Costa, M., Castro, M., Rowstron, R., & Key, P. (2004). PIC: Practical Internet coordinates for distance estimation. In Distributed Computing Systems, 2004. Proceedings. 24th International Conference on (pp. 178-187). IEEE. [CrossRef] [Google Scholar]
  13. Ng, T. E., & Zhang, H. (2004, June). A Network Positioning System for the Internet. In USENIX Annual Technical Conference, General Track (pp. 141-154). [Google Scholar]
  14. Liu, Y., Du, H., & Ye, Q. (2015). WDCS: A Weight-Based Distributed Coordinate System. In Combinatorial optimization and application (pp. 251-260). Springer, Cham. [CrossRef] [Google Scholar]
  15. Zheng, H., Lua, E. K., Pias, M., & Griffin, T. G. (2005, March). Internet routing policies and round-trip-times. In International Workshop on Passive and Active Network Measurement (pp. 236-250). Springer, Berlin, Heidelberg. [Google Scholar]
  16. Cheng, J., Liu, Y., Ye, Q., Du, H., & Vasilakos, A.V (2017). DISCS: A distributed coordinate system based on robust nonnegative matrix completion. IEEE/ACM Transactions on Networking (TON), 25(2), 934-947. [J]. IEEE/ACM Transactions on Networking (TON), 2017, 25(2): 934-947. [CrossRef] [Google Scholar]
  17. Mao, Y., Saul, L.K, & Smith, J.M (2006). Ides: An internet distance estimation service for large networks. IEEE Journal on Selected Areas in Communications, 24(12), 2273-2284. [CrossRef] [Google Scholar]
  18. Nelder, J. A., & Mead, R. (1965). A simplex method for function minimization. The computer journal, 7(4), 308-313 [Google Scholar]
  19. Chen, Y., Xiong, Y., Shi, X., Deng, B., & Li, X. (2007, November). Pharos: A decentralized and hierarchical network coordinate system for internet distance prediction. In Global Telecommunications Conference, 2007. GLOBECOM’07. IEEE (pp. 421-426). IEEE. [Google Scholar]
  20. Chen, Y., Wang, X., Shi, C., Lua, E.K, Fu, X., Deng, B., & Li, X. (2011). Phoenix: A weight-based network coordinate system using matrix factorization. IEEE Transactions on Network and Service Management, 8(4), 334-347. [CrossRef] [Google Scholar]
  21. Chen, Y., Wang, X., Song, X., Lua, E.K, Shi, C., Zhao, X & Li, X. (2009, May). Phoenix: Towards an accurate, practical and decentralized network coordinate system. In International Conference on Research in Networking (pp. 313-325). Springer, Berlin, Heidelberg. [Google Scholar]
  22. Zhao F, Chen J, Ye D & Luo X. (2017, June). A Network Coordinate System Constructing Algorithm Based on Optimal Neighbor Nodes. In Data Science in Cyberspace (DSC), 2017 IEEE Second International Conference on (pp. 140-144). IEEE. [Google Scholar]
  23. Kaafar, M. A., Mathy, L., Turletti, T., & Dabbous, W. (2006, December). Virtual networks under attack: Disrupting internet coordinate systems. In Proceedings of the 2006 ACM CoNEXT conference (p. 12). ACM. [Google Scholar]
  24. Lee, D. D., & Seung, H.S (1999). Learning the parts of objects by non-negative matrix factorization. Nature, 401(6755), 788. [NASA ADS] [CrossRef] [PubMed] [Google Scholar]
  25. Boutsidis, C., & Gallopoulos, E. (2008). SVD based initialization: A head start for nonnegative matrix factorization. Pattern Recognition, 41(4), 1350-1362. [CrossRef] [Google Scholar]

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