DocumentCode :
2236911
Title :
Multistep ahead prediction for real-time VBR video traffic using deterministic echo state network
Author :
Xiaochuan Sun ; Hongyan Cui ; Renping Liu ; Jianya Chen ; Yunjie Liu
Author_Institution :
Key Lab. of Network Syst. Archit. & Convergence, Beijing Univ. of Posts & Telecommun., Beijing, China
fYear :
2012
fDate :
Oct. 30 2012-Nov. 1 2012
Firstpage :
928
Lastpage :
931
Abstract :
Variable bit rate (VBR) video traffic, exhibiting high self-similarity and burstiness, has been a major traffic component in high speed network. However, its complex bit rate distribution makes VBR video traffic prediction, especially multistep ahead prediction, very difficult. Recently, deterministic echo state network with adjacent-feedback loop reservoir structure (ALR) was proved to have high prediction accuracy, good memory capacity, and simple structure. In the paper, we apply ALR to real-time VBR video traffic prediction. The proposed scheme makes use of loop reservoir with identity activation function to conduct sample learning in high dimension states. Experimental results show that the simplified ALR scheme can effectively capture dynamic characteristics of VBR video traffic with less training time. Its multistep prediction accuracy is improved by 2% on average, compared with the neural network based on multi-resolution learning.
Keywords :
Internet; feedback; telecommunication traffic; video coding; video communication; VBR H.264 encoded video; activation function; adjacent-feedback loop reservoir structure; complex bit rate distribution; deterministic echo state network; dynamic characteristics; high speed network; multiresolution learning; multistep ahead prediction; neural network; real-time VBR video traffic prediction; traffic component; variable bit rate; Educational institutions; Neural networks; Real-time systems; Reservoirs; Streaming media; Training; Transform coding; Burstiness; Echo state network; Loop reservoir; Self-similarity; VBR video traffic;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Cloud Computing and Intelligent Systems (CCIS), 2012 IEEE 2nd International Conference on
Conference_Location :
Hangzhou
Print_ISBN :
978-1-4673-1855-6
Type :
conf
DOI :
10.1109/CCIS.2012.6664312
Filename :
6664312
Link To Document :
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