Research of Network Traffic Matrix Based on Improved Fanout Model

2013 ◽  
Vol 321-324 ◽  
pp. 2745-2748
Author(s):  
Lin Bo He ◽  
Li Liu ◽  
Zhi Wei Sheng
Keyword(s):  
Network Design ◽  
Network Management ◽  
Network Optimization ◽  
Network Traffic ◽  
Matrix Estimation ◽  
Traffic Matrix ◽  
Network Research ◽  
Traffic Matrix Estimation ◽  
Short Time ◽  
Traffic Detection

Traffic matrix plays a very important role in network management field, such as network design, network optimization, traffic detection, etc. As a result, it is always a hot topic in network research. Based on traditional fanout model, an improved fanout model is proposed to conduct traffic matrix estimation. The model takes into consideration of estimation deviation brought by non-persistent sudden burst of networks flow in a short time, which improves the accuracy of traffic matrix estimation. The simulation shows with algorithm the estimation value has greatly improved in a one-day period.

scholarly journals Probability-model based network traffic matrix estimation

2014 ◽  
Vol 11 (1) ◽  
pp. 309-320
Author(s):  
Hui Tian ◽  
Yingpeng Sang ◽  
Hong Shen ◽  
Chunyue Zhou
Keyword(s):  
Large Scale ◽  
Probability Model ◽  
Estimation Methods ◽  
Matrix Estimation ◽  
Large Scale Network ◽  
Traffic Matrix ◽  
Minimization Methods ◽  
Traffic Matrix Estimation ◽  
Scale Network ◽  
Mean Square Errors

Traffic matrix is of great help in many network applications. However, it is very difficult to estimate the traffic matrix for a large-scale network. This is because the estimation problem from limited link measurements is highly underconstrained. We propose a simple probability model for a large-scale practical network. The probability model is then generalized to a general model by including random traffic data. Traffic matrix estimation is then conducted under these two models by two minimization methods. It is shown that the Normalized Root Mean Square Errors of these estimates under our model assumption are very small. For a large-scale network, the traffic matrix estimation methods also perform well. The comparison of two minimization methods shown in the simulation results complies with the analysis.

Sign in / Sign up

Export Citation Format

Share Document