OpenTM: Traffic Matrix Estimator for OpenFlow Networks

2010 ◽  
pp. 201-210 ◽  
Author(s):  
Amin Tootoonchian ◽  
Monia Ghobadi ◽  
Yashar Ganjali
Keyword(s):  
Traffic Matrix

scholarly journals Deep learning based network traffic matrix prediction

2021 ◽  
Vol 2 ◽  
pp. 46-56
Author(s):  
Dalal Aloraifan ◽  
Imtiaz Ahmad ◽  
Ebrahim Alrashed
Keyword(s):  
Deep Learning ◽  
Network Traffic ◽  
Traffic Matrix

scholarly journals Multipath Wireless Network Coding: A Scheduling Constraint in Game Perspective

2021 ◽  
pp. 105-109
Author(s):  
Dr. D. Chitra ◽  
K. Ilakkiya
Keyword(s):  
Wireless Networks ◽  
Network Coding ◽  
Potential Game ◽  
Level Control ◽  
Link Capacity ◽  
Space Extension ◽  
Wireless Network Coding ◽  
Traffic Matrix ◽  
Control Scheme ◽  
User Actions

This paper considers wireless networks in which various paths are obtainable involving each source and destination. It is allowing each source to tear traffic among all of its existing paths, and it may conquer the lowest achievable number of transmissions per unit time to sustain a prearranged traffic matrix. Traffic bound in contradictory instructions in excess of two wireless hops can utilize the “reverse carpooling” advantage of network coding in order to decrease the number of transmissions used. These call such coded hops “hyper-links.” With the overturn carpooling procedure, longer paths might be cheaper than shorter ones. However, convenient is an irregular situation among sources. The network coding advantage is realized only if there is traffic in both directions of a shared path. This project regard as the problem of routing amid network coding by egotistic agents (the sources) as a potential game and develop a method of state-space extension in which extra agents (the hyper-links) decouple sources’ choices from each other by declaring a hyper-link capacity, allowing sources to split their traffic selfishly in a distributed fashion, and then altering the hyper-link capacity based on user actions. Furthermore, each hyper-link has a scheduling constraint in stipulations of the maximum number of transmissions authorized per unit time. Finally these project show that our two-level control scheme is established and verify our investigative insights by simulation.

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.

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