Traffic Matrix Prediction Based on Deep Learning for Dynamic Traffic Engineering

2019 ◽  
Author(s):  
Zhifeng Liu ◽  
Zhiliang Wang ◽  
Xia Yin ◽  
Xingang Shi ◽  
Yingya Guo ◽  
...  
Keyword(s):  
Deep Learning ◽  
Traffic Engineering ◽  
Dynamic Traffic ◽  
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

The Joint Optimization of Online Traffic Matrix Measurement and Traffic Engineering For Software-Defined Networks

2020 ◽  
Vol 28 (1) ◽  
pp. 234-247
Author(s):  
Xiong Wang ◽  
Qi Deng ◽  
Jing Ren ◽  
Mehdi Malboubi ◽  
Sheng Wang ◽  
...  
Keyword(s):  
Traffic Engineering ◽  
Joint Optimization ◽  
Software Defined Networks ◽  
Traffic Matrix ◽  
Online Traffic

DTE-SDN: A Dynamic Traffic Engineering Engine for Delay-Sensitive Transfer

2018 ◽  
Vol 5 (6) ◽  
pp. 5240-5253 ◽  
Author(s):  
Chuan Lin ◽  
Yuanguo Bi ◽  
Hai Zhao ◽  
Zheng Liu ◽  
Siyuan Jia ◽  
...  
Keyword(s):  
Traffic Engineering ◽  
Dynamic Traffic ◽  
Delay Sensitive

scholarly journals OpenFlow-controlled QoS-aware dynamic traffic engineering for differentiated service provisioning in hybrid IPv4/IPv6 networks

2016 ◽  
Vol 46 (5) ◽  
pp. 665-676
Author(s):  
Yan SHAO ◽  
Shengru LI ◽  
Zuqing ZHU ◽  
Shoujiang MA ◽  
Suoheng LI ◽  
...  
Keyword(s):  
Traffic Engineering ◽  
Service Provisioning ◽  
Differentiated Service ◽  
Dynamic Traffic ◽  
Ipv6 Networks

scholarly journals Estimation of Traffic Matrix from Links Load using Genetic Algorithm

2021 ◽  
Vol 22 (1) ◽  
pp. 29-38
Author(s):  
Joseph L Pachuau ◽  
Arnab Roy ◽  
Gopal Krishna ◽  
Anish Kumar Saha
Keyword(s):  
Genetic Algorithm ◽  
Network Management ◽  
Gravity Model ◽  
Traffic Engineering ◽  
Optimization Method ◽  
Traffic Flows ◽  
Traffic Measurement ◽  
Traffic Models ◽  
Traffic Matrix ◽  
Infinite Set

Traffic Matrix (TM) is a representation of all traffic flows in a network. It is helpful for traffic engineering and network management. It contains the traffic measurement for all parts of a network and thus for larger network it is difficult to measure precisely. Link load are easily obtainable but they fail to provide a complete TM representation. Also link load and TM relationship forms an under-determined system with infinite set of solutions. One of the well known traffic models Gravity model provides a rough estimation of the TM. We have proposed a Genetic algorithm (GA) based optimization method to further the solutions of the Gravity model. The Gravity model is applied as an initial solution and then GA model is applied taking the link load-TM relationship as a objective function. Results shows improvement over Gravity model.

Dynamic traffic engineering for mixed traffic on international networks

2008 ◽  
Vol 52 (11) ◽  
pp. 2237-2258 ◽  
Author(s):  
Sukrit Dasgupta ◽  
Jaudelice C. de Oliveira ◽  
J.-P. Vasseur
Keyword(s):  
Traffic Engineering ◽  
Mixed Traffic ◽  
Dynamic Traffic ◽  
International Networks
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