Please use this identifier to cite or link to this item: http://buratest.brunel.ac.uk/handle/2438/13382
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dc.contributor.authorSabbeh, A-
dc.contributor.authorAl-Dunainawi, Y-
dc.contributor.authorAl-Raweshidy, HS-
dc.contributor.authorAbbod, MF-
dc.date.accessioned2016-10-20T11:41:42Z-
dc.date.available2016-08-29-
dc.date.available2016-10-20T11:41:42Z-
dc.date.issued2016-
dc.identifier.citationProceedings of 2016 SAI Computing Conference (SAI 2016), pp. 80 - 84, (13-15 July 2016)en_US
dc.identifier.isbn9781467384605-
dc.identifier.isbn978-1-4673-8460-5-
dc.identifier.urihttp://ieeexplore.ieee.org/document/7555965/-
dc.identifier.urihttp://bura.brunel.ac.uk/handle/2438/13382-
dc.description.abstractAn Artificial Neural Network has been proposed as predicting the performance of the Software Defined Network according to effective traffic parameters. Those used in this study are round-trip time, throughput and the flow table rules for each switch, POX controller and OpenFlow switches, which characterize the behaviour of the Software Defined Network, have been modelled and simulated via Mininet and Matlab platforms. An ANN has the ability to provide an excellent input-output relationship for nonlinear and complex processes. The network has been implemented using different topologies, one and two layers in the hidden zone with different numbers of neurons. Generalization of the prediction model has been tested with new data that are unseen in the training stage. The simulated results show reasonably good performance of the network.en_US
dc.description.sponsorshipThe Iraqi Ministry of Higher Education and Scientific Researchen_US
dc.format.extent80 - 84-
dc.language.isoenen_US
dc.publisherIEEEen_US
dc.subjectNeural networken_US
dc.subjectSDNen_US
dc.subjectTraffic predictionen_US
dc.titlePerformance prediction of software defined network using an artificial neural networken_US
dc.typeConference Paperen_US
dc.identifier.doihttp://dx.doi.org/10.1109/SAI.2016.7555965-
dc.relation.isPartOfProceedings of 2016 SAI Computing Conference, SAI 2016-
pubs.publication-statusPublished-
Appears in Collections:Dept of Electronic and Computer Engineering Research Papers

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