Multistep ahead prediction for real-time VBR video traffic using deterministic echo state network

Sun, X; Cui, H; Liu, R; Chen, J; Liu, Y

Multistep ahead prediction for real-time VBR video traffic using deterministic echo state network

Sun, X Cui, H Liu, R

Chen, J Liu, Y

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Publication Type:: Conference Proceeding
Citation:: Proceedings - 2012 IEEE 2nd International Conference on Cloud Computing and Intelligence Systems, IEEE CCIS 2012, 2013, 2 pp. 928 - 931
Issue Date:: 2013-11-13

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Field	Value	Language
dc.contributor.author	Sun, X	en_US
dc.contributor.author	Cui, H	en_US
dc.contributor.author	Liu, R https://orcid.org/0000-0001-7001-6305	en_US
dc.contributor.author	Chen, J	en_US
dc.contributor.author	Liu, Y	en_US
dc.date.issued	2013-11-13	en_US
dc.identifier.citation	Proceedings - 2012 IEEE 2nd International Conference on Cloud Computing and Intelligence Systems, IEEE CCIS 2012, 2013, 2 pp. 928 - 931	en_US
dc.identifier.isbn	9781467318556	en_US
dc.identifier.uri	http://hdl.handle.net/10453/120961
dc.description.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. © 2012 IEEE.	en_US
dc.relation.ispartof	Proceedings - 2012 IEEE 2nd International Conference on Cloud Computing and Intelligence Systems, IEEE CCIS 2012	en_US
dc.relation.isbasedon	10.1109/CCIS.2012.6664312	en_US
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	Multistep ahead prediction for real-time VBR video traffic using deterministic echo state network	en_US
dc.type	Conference Proceeding
utslib.citation.volume	2	en_US
utslib.for	0805 Distributed Computing	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - GBDTC - Global Big Data Technologies
utslib.copyright.status	closed_access	*
pubs.publication-status	Published	en_US
pubs.volume	2	en_US

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. © 2012 IEEE.

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http://hdl.handle.net/10453/120961