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dc.contributor.authorLu, Yao
dc.contributor.authorLiu, Lu
dc.contributor.authorPanneerselvam, John
dc.contributor.authorYuan, Bo
dc.contributor.authorGu, Jiayan
dc.contributor.authorAntonopoulos, Nick
dc.date.accessioned2020-09-17T09:40:04Z
dc.date.available2020-09-17T09:40:04Z
dc.date.issued2019-11-19
dc.identifier.citationLu, Y., Liu, L., Panneerselvam, J., Yuan, B., Gu, J. and Antonopoulos, N., (2019). 'A GRU-Based Prediction Framework for Intelligent Resource Management at Cloud Data Centres in the Age of 5G'. IEEE Transactions on Cognitive Communications and Networking, 6(2), pp. 486 - 498.en_US
dc.identifier.doi10.1109/tccn.2019.2954388
dc.identifier.urihttp://hdl.handle.net/10545/625171
dc.description.abstractThe increasing deployments of 5G mobile communication system is expected to bring more processing power and storage supplements to Internet of Things (IoT) and mobile devices. It is foreseeable the billions of devices will be connected and it is extremely likely that these devices receive compute supplements from Clouds and upload data to the back-end datacentres for execution. Increasing number of workloads at the Cloud datacentres demand better and efficient strategies of resource management in such a way to boost the socio-economic benefits of the service providers. To this end, this paper proposes an intelligent prediction framework named IGRU-SD (Improved Gated Recurrent Unit with Stragglers Detection) based on state-of-art data analytics and Artificial Intelligence (AI) techniques, aimed at predicting the anticipated level of resource requests over a period of time into the future. Our proposed prediction framework exploits an improved GRU neural network integrated with a resource straggler detection module to classify tasks based on their resource intensity, and further predicts the expected level of resource requests. Performance evaluations conducted on real-world Cloud trace logs demonstrate that the proposed IGRU-SD prediction framework outperforms the existing predicting models based on ARIMA, RNN and LSTM in terms of the achieved prediction accuracy.en_US
dc.description.sponsorshipNatural Science Foundation of Jiangsu Provinceen_US
dc.language.isoenen_US
dc.publisherIEEEen_US
dc.relation.urlhttps://ieeexplore.ieee.org/document/8906165en_US
dc.rightsAttribution-NonCommercial-ShareAlike 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/4.0/*
dc.subjectComputer Networks and Communicationsen_US
dc.subjectHardware and Architectureen_US
dc.subjectArtificial Intelligenceen_US
dc.titleA GRU-based prediction framework for intelligent resource management at cloud data centres in the age of 5Gen_US
dc.typeArticleen_US
dc.identifier.eissn2372-2045
dc.contributor.departmentUniversity of Leicesteren_US
dc.contributor.departmentUniversity of Derbyen_US
dc.contributor.departmentEdinburgh Napier Universityen_US
dc.identifier.journalIEEE Transactions on Cognitive Communications and Networkingen_US
dc.source.journaltitleIEEE Transactions on Cognitive Communications and Networking
dc.source.volume6
dc.source.issue2
dc.source.beginpage486
dc.source.endpage498
dcterms.dateAccepted2019-11-09
dc.author.detailSTF1867en_US


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Attribution-NonCommercial-ShareAlike 4.0 International
Except where otherwise noted, this item's license is described as Attribution-NonCommercial-ShareAlike 4.0 International