Please use this identifier to cite or link to this item: http://hdl.handle.net/11189/10493
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dc.contributor.authorPeriola, Ayodele Abiolaen_US
dc.contributor.authorAlonge, Akintunde Ayodejien_US
dc.contributor.authorOgudo, Kingsley A.en_US
dc.date.accessioned2026-01-20T10:58:17Z-
dc.date.available2026-01-20T10:58:17Z-
dc.date.issued2024-
dc.identifier.citationPeriola, A.A., Alonge, A.A. & Ogudo, K.A. 2024. Green intelligent content rendering paradigm. (In: International Conference on Artificial Intelligence, Big Data, Computing and Data Communication Systems (icABCD), Port Louis, Mauritius, 01-02 August 2024. p. 1-6). [https://doi.org/10.1109/icABCD62167.2024.10645257]en_US
dc.identifier.issn979-8-3503-8790-2 (Online)-
dc.identifier.urihttp://hdl.handle.net/11189/10493-
dc.description.abstractMulti-media content rendering is an important computing intensive task required for multi-media content generation. The content rendering is executed aboard ground based data centres. The operation of terrestrial data centres is inefficient when a significant amount of power is expended on cooling alongside a high-water footprint (WF). This results in a high-power usage effectiveness (PUE). The high PUE, and WF can be reduced via the use of alternative freely cooled data centre such as the underwater data centre. The research proposes render workload migration strategy enabling an underwater data centre to execute the render workload. Furthermore, the underwater data centre hosts mini - nuclear reactors that enhance its power accessibility instead of only relying on highly variable renewable energy resources in the underwater environment. Performance evaluation shows that the use of the PUE, and accessible power is improved by proposed approach by an average of 26.5%, and 63.8%, respectively.en_US
dc.language.isoenen_US
dc.publisherIEEEen_US
dc.subjectNon-terrestrial platformsen_US
dc.subjectMultimedia contenten_US
dc.subjectRenderingen_US
dc.subjectEnergy accessen_US
dc.titleGreen intelligent content rendering paradigmen_US
dc.relation.conference7th International Conference on Artificial Intelligence, Big Data, Computing and Data Communication Systems (icABCD)en_US
dc.identifier.doihttps://doi.org/10.1109/icABCD62167.2024.10645257-
Appears in Collections:Eng - Conference Papers
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