Deep Q-Learning based resource allocation and load balancing in a mobile edge system serving different types of user requests

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dc.authorscopusid58143293000
dc.authorscopusid24725434900
dc.contributor.authorYildiz, Onem
dc.contributor.authorSokullu, Radosveta Ivanova
dc.date.accessioned2024-08-25T18:45:41Z
dc.date.available2024-08-25T18:45:41Z
dc.date.issued2023
dc.departmentEge Üniversitesien_US
dc.description.abstractWith the expansion of the communicative and perceptual capabilities of mobile devices in recent years, the number of complex and high computational applications has also increased rendering traditional methods of traffic management and resource allocation quite insufficient. Recently, mobile edge computing (MEC) has emerged as a new viable solution to these problems. It can provide additional computing features at the edge of the network and allow alleviation of the resource limit of mobile devices while increasing the performance for critical applications especially in terms of latency. In this work, we addressed the issue of reducing the service delay by choosing the optimal path in the MEC network, which consists of multiple MEC servers that has different capabilities, applying network load balancing where multiple requests need to be handled simultaneously and routing selection based on a deep- Q network (DQN) algorithm. A novel traffic control and resource allocation method is proposed based on deep Q-learning (DQL) which allows reducing the end-to-end delay in cellular networks and in the mobile edge network. Real life traffic scenarios with various types of user requests are considered and a novel DQL resource allocation scheme which adaptively assigns computing and network resources is proposed. The algorithm optimizes traffic distribution between servers reducing the total service time and balancing the use of available resources under varying environmental conditions.en_US
dc.description.sponsorshipEge University Research Fund [FDK-2020-21953]en_US
dc.description.sponsorshipThis work has been developed under the FDK-2020-21953 project, funded by Ege University Research Funden_US
dc.identifier.doi10.2478/jee-2023-0006
dc.identifier.endpage56en_US
dc.identifier.issn1335-3632
dc.identifier.issn1339-309X
dc.identifier.issue1en_US
dc.identifier.scopus2-s2.0-85150201017en_US
dc.identifier.scopusqualityQ3en_US
dc.identifier.startpage48en_US
dc.identifier.urihttps://doi.org/10.2478/jee-2023-0006
dc.identifier.urihttps://hdl.handle.net/11454/101653
dc.identifier.volume74en_US
dc.identifier.wosWOS:000945497500006en_US
dc.identifier.wosqualityQ4en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSlovak Univ Technologyen_US
dc.relation.ispartofJournal of Electrical Engineering-Elektrotechnicky Casopisen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.snmz20240825_Gen_US
dc.subjectcellular networken_US
dc.subjectdeep Q-learningen_US
dc.subjectmobile edge networken_US
dc.subjectresource allocationen_US
dc.titleDeep Q-Learning based resource allocation and load balancing in a mobile edge system serving different types of user requestsen_US
dc.typeArticleen_US

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