DocumentCode :
532779
Title :
MP2P traffic intelligent management model based on BP neural network
Author :
Qu, Haitao ; Song, Meina ; Wang, Rihua ; Zhu, Binjie ; Qu, Wu ; Song, Junde
Author_Institution :
ICT&SSME Center, Beijing Univ. of Posts & Telecommun., Beijing, China
Volume :
12
fYear :
2010
fDate :
22-24 Oct. 2010
Abstract :
A Mobile Peer-to-Peer (MP2P) traffic intelligent management model (BP Neural Network and Intelligent Management, BP-IM) based on neural network is proposed. First, the whole MP2P network is modeled by the semi-distributed structure. Then the BP neural network is introduced to measure and control the MP2P flow effectively. In addition, the corresponding P2P traffic priority table is established and the assignment of the buffer flow is dynamically adjusted. The BP-IM model has the features of flexible configuration, efficient detection, and easy to be extended, reducing the complexity of the algorithm. Computer simulations based on OMNeT++ show that: compared to the traditional model, higher-level traffic processing delay is small by the proposed BP-IM model, which saves the limited MP2P bandwidth resources and avoids network congestion. MP2P traffic processing delay decreases with increase of the traffic priority, which reflects the intelligent traffic management concept for BP-IM model.
Keywords :
backpropagation; mobile computing; neural nets; peer-to-peer computing; telecommunication computing; telecommunication congestion control; telecommunication traffic; BP neural network; BP-IM model; MP2P flow; MP2P traffic intelligent management model; OMNeT++; buffer flow; mobile peer-to-peer network; network congestion avoidance; Artificial neural networks; Computational modeling; Delay; Mobile communication; Peer to peer computing; Routing; Servers; BP neural network; intelligent management; peer-to-peer; traffic model;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computer Application and System Modeling (ICCASM), 2010 International Conference on
Conference_Location :
Taiyuan
Print_ISBN :
978-1-4244-7235-2
Electronic_ISBN :
978-1-4244-7237-6
Type :
conf
DOI :
10.1109/ICCASM.2010.5622310
Filename :
5622310
Link To Document :
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