A novel approach for performance-based clustering and management of network traffic flows
dc.contributor.author | Al-Saadi, M | |
dc.contributor.author | Ghita, B | |
dc.contributor.author | Shiaeles, S | |
dc.contributor.author | Sarigiannidis, P | |
dc.date.accessioned | 2021-05-18T13:05:14Z | |
dc.date.available | 2021-05-18T13:05:14Z | |
dc.date.issued | 2019-06 | |
dc.identifier.isbn | 9781538677476 | |
dc.identifier.issn | 2376-6492 | |
dc.identifier.uri | http://hdl.handle.net/10026.1/17149 | |
dc.description.abstract |
Management of network performance comprises numerous functions such as measuring, modelling, planning and optimising networks to ensure that they transmit traffic with the speed, capacity and reliability expected by the applications, each with different requirements for bandwidth and delay. Overall, the objective of this paper is to propose a novel mechanism to optimise the network resource allocation through supporting the routing of individual flows, by clustering them based on performance and integrating the respective clusters with an SDN scheme. In this paper we have employed a particular set of traffic features then applied data reduction and unsupervised machine learning techniques, to derive an Internet traffic performance-based clustering model. Finally, the resulting data clusters are integrated within a unified SDN architectural solution, which improves network management by finding nearly optimal flow routing, to be evaluated against a number of traffic data sources. | |
dc.format.extent | 2025-2030 | |
dc.language.iso | en | |
dc.publisher | IEEE | |
dc.subject | Network performance | |
dc.subject | Clustering | |
dc.subject | Unsupervised algorithm | |
dc.subject | SDN | |
dc.title | A novel approach for performance-based clustering and management of network traffic flows | |
dc.type | conference | |
dc.type | Conference Proceeding | |
plymouth.author-url | https://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000492150100344&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=11bb513d99f797142bcfeffcc58ea008 | |
plymouth.date-start | 2019-06-24 | |
plymouth.date-finish | 2019-06-28 | |
plymouth.volume | 00 | |
plymouth.publisher-url | https://ieeexplore.ieee.org/xpl/conhome/8761262/proceeding | |
plymouth.conference-name | 2019 15th International Wireless Communications and Mobile Computing Conference (IWCMC) | |
plymouth.publication-status | Published | |
plymouth.journal | 2019 15th International Wireless Communications & Mobile Computing Conference (IWCMC) | |
dc.identifier.doi | 10.1109/iwcmc.2019.8766728 | |
plymouth.organisational-group | /Plymouth | |
plymouth.organisational-group | /Plymouth/Faculty of Science and Engineering | |
plymouth.organisational-group | /Plymouth/Faculty of Science and Engineering/School of Engineering, Computing and Mathematics | |
plymouth.organisational-group | /Plymouth/REF 2021 Researchers by UoA | |
plymouth.organisational-group | /Plymouth/REF 2021 Researchers by UoA/UoA11 Computer Science and Informatics | |
plymouth.organisational-group | /Plymouth/Users by role | |
plymouth.organisational-group | /Plymouth/Users by role/Academics | |
dc.rights.embargoperiod | Not known | |
rioxxterms.versionofrecord | 10.1109/iwcmc.2019.8766728 | |
rioxxterms.licenseref.uri | http://www.rioxx.net/licenses/all-rights-reserved | |
rioxxterms.type | Conference Paper/Proceeding/Abstract |