DocumentCode :
3444262
Title :
A Novel Approach to Large-Scale IP Traffic Matrix Estimation Based on RBF Neural Network
Author :
Jiang, Dingde ; Hu, Guangmin
Author_Institution :
Key Lab. of Broadband Opt. Fiber Transm. & Commun. Networks, Univ. of Electron. Sci. & Technol. of China, Chengdu
fYear :
2008
fDate :
12-14 Oct. 2008
Firstpage :
1
Lastpage :
4
Abstract :
Traffic matrix is a key parameter of traffic engineering, which describes the characteristics of IP networks from the global aspects. Though traffic matrix estimation is extensively studied, traffic matrix is generally unavailable in the large-scale IP network and is difficult to be estimated accurately. This paper proposes a novel method of large-scale IP traffic matrix estimation, termed the radial basis function (RBF) neural network and iterative proportional fitting procedure (RBFIPFP) method. Firstly, we model the large-scale IP traffic matrix estimation using the RBF neural network that has been studied widely. By training the RBF neural network, we can build the model of large-scale IP traffic matrix estimation. Secondly, combined with this model and iterative proportional fitting procedure, the good estimations of the large-scale IP traffic matrix are attained. Finally, we use the real data from the Abilene network to validate RBFIPFP. The results show that RBFIPFP can perform the accurate estimation of large-scale IP traffic matrix, and track well its dynamics.
Keywords :
IP networks; iterative methods; radial basis function networks; Abilene network; RBF neural network; iterative proportional fitting; large-scale IP network; large-scale IP traffic matrix estimation; radial basis function neural network; IP networks; Iterative methods; Large-scale systems; Mathematical model; Neural networks; Optical fibers; Routing; Telecommunication traffic; Tomography; Traffic control;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Wireless Communications, Networking and Mobile Computing, 2008. WiCOM '08. 4th International Conference on
Conference_Location :
Dalian
Print_ISBN :
978-1-4244-2107-7
Electronic_ISBN :
978-1-4244-2108-4
Type :
conf
DOI :
10.1109/WiCom.2008.1068
Filename :
4678976
Link To Document :
بازگشت