NARGES: Prediction Model for Informed Routing in a Communications Network

Homayounfard, H; Kennedy, PJ; Braun, RM

NARGES: Prediction Model for Informed Routing in a Communications Network

Homayounfard, H Kennedy, PJ Braun, RM

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Publisher:: Springer
Publication Type:: Journal Article
Citation:: Lecture Notes in Computer Science, 2013, 7818 (1), pp. 1 - 12
Issue Date:: 2013-01

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Field	Value	Language
dc.contributor.author	Homayounfard, H	en_US
dc.contributor.author	Kennedy, PJ	en_US
dc.contributor.author	Braun, RM	en_US
dc.contributor.editor	Pei	en_US
dc.contributor.editor	J	en_US
dc.contributor.editor	Tseng	en_US
dc.contributor.editor	S, V	en_US
dc.contributor.editor	Cao	en_US
dc.contributor.editor	L	en_US
dc.contributor.editor	Motoda	en_US
dc.contributor.editor	H	en_US
dc.contributor.editor	Xu	en_US
dc.contributor.editor	Eds, G	en_US
dc.date.issued	2013-01	en_US
dc.identifier.citation	Lecture Notes in Computer Science, 2013, 7818 (1), pp. 1 - 12	en_US
dc.identifier.issn	0302-9743	en_US
dc.identifier.uri	http://hdl.handle.net/10453/27161
dc.description.abstract	There is a dependency between packet-loss and the delay and jitter time-series derived from a telecommunication link. Multimedia applications such as Voice over IP (VoIP) are sensitive to loss and packet recovery is not a merely efficient solution with the increasing number of Internet users. Predicting packet-loss from network dynamics of past transmissions is crucial to inform the next generation of routers in making smart decisions. This paper proposes a hybrid data mining model for routing management in a communications network, called NARGES. The proposed model is designed and implemented for predicting packet-loss based on the forecasted delays and jitters. The model consists of two parts: a historical symbolic time-series approximation module, called HDAX, and a Multilayer Perceptron (MLP). It is validated with heterogeneous quality of service (QoS) datasets, namely delay, jitter and packet-loss time-series. The results show improved precision and quality of prediction compared to autoregressive moving average, ARMA.	en_US
dc.publisher	Springer	en_US
dc.relation.ispartof	Lecture Notes in Computer Science	en_US
dc.relation.isbasedon	10.1007/978-3-642-37453-1_27	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	NARGES: Prediction Model for Informed Routing in a Communications Network	en_US
dc.type	Journal Article
utslib.citation.volume	1	en_US
utslib.citation.volume	7818	en_US
utslib.location.activity	Gold Coast, Australia	en_US
utslib.for	0805 Distributed Computing	en_US
utslib.for	08 Information And Computing Sciences	en_US
dc.location.activity	Gold Coast, Australia	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/Faculty of Engineering and Information Technology/School of Software
pubs.organisational-group	/University of Technology Sydney/Strength - AAI - Advanced Analytics Research Centre
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
pubs.organisational-group	/University of Technology Sydney/Strength - CHT - Health Technologies
pubs.organisational-group	/University of Technology Sydney/Strength - CRIN - Realtime Information Networks
utslib.copyright.status	closed_access
pubs.consider-herdc	true	en_US
pubs.issue	1	en_US
pubs.volume	7818	en_US

Abstract:

There is a dependency between packet-loss and the delay and jitter time-series derived from a telecommunication link. Multimedia applications such as Voice over IP (VoIP) are sensitive to loss and packet recovery is not a merely efficient solution with the increasing number of Internet users. Predicting packet-loss from network dynamics of past transmissions is crucial to inform the next generation of routers in making smart decisions. This paper proposes a hybrid data mining model for routing management in a communications network, called NARGES. The proposed model is designed and implemented for predicting packet-loss based on the forecasted delays and jitters. The model consists of two parts: a historical symbolic time-series approximation module, called HDAX, and a Multilayer Perceptron (MLP). It is validated with heterogeneous quality of service (QoS) datasets, namely delay, jitter and packet-loss time-series. The results show improved precision and quality of prediction compared to autoregressive moving average, ARMA.

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http://hdl.handle.net/10453/27161