Resource Scheduling in Edge Computing: Architecture, Taxonomy, Open Issues and Future Research Directions

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dc.authoridKim, Byung-Seo/0000-0001-9824-1950
dc.authoridHabbal, Adib/0000-0002-3939-2609
dc.authoridDAKKAK, OMAR/0000-0001-9767-5685
dc.contributor.authorRaeisi-Varzaneh, Mostafa
dc.contributor.authorDakkak, Omar
dc.contributor.authorHabbal, Adib
dc.contributor.authorKim, Byung-Seo
dc.date.accessioned2024-09-29T16:03:27Z
dc.date.available2024-09-29T16:03:27Z
dc.date.issued2023
dc.departmentKarabük Üniversitesien_US
dc.description.abstractAn inflection point in the computing industry is occurring with the implementation of the Internet of Things and 5G communications, which has pushed centralized cloud computing toward edge computing resulting in a paradigm shift in computing. The purpose of edge computing is to provide computing, network control, and storage to the network edge to accommodate computationally intense and latency-critical applications at resource-limited endpoints. Edge computing allows edge devices to offload their overflowing computing tasks to edge servers. This procedure may completely exploit the edge server's computational and storage capabilities and efficiently execute computing operations. However, transferring all the overflowing computing tasks to an edge server leads to long processing delays and surprisingly high energy consumption for numerous computing tasks. Aside from this, unused edge devices and powerful cloud centers may lead to resource waste. Thus, hiring a collaborative scheduling approach based on task properties, optimization targets, and system status with edge servers, cloud centers, and edge devices is critical for the successful operation of edge computing. This paper briefly summarizes the edge computing architecture for information and task processing. Meanwhile, the collaborative scheduling scenarios are examined. Resource scheduling techniques are then discussed and compared based on four collaboration modes. As part of our survey, we present a thorough overview of the various task offloading schemes proposed by researchers for edge computing. Additionally, according to the literature surveyed, we briefly looked at the fairness and load balancing indicators in scheduling. Finally, edge computing resource scheduling issues, challenges, and future directions have discussed.en_US
dc.description.sponsorshipNational Research Foundation of Korea (NRF) Grant through the Korea Government; Ministry of Sciencea nd ICT (MSIT) [2022R1A2C1003549]en_US
dc.description.sponsorshipThis work was supported by the National Research Foundation of Korea (NRF) Grant through the Korea Government, Ministry of Sciencea nd ICT (MSIT), under Grant 2022R1A2C1003549en_US
dc.identifier.doi10.1109/ACCESS.2023.3256522
dc.identifier.endpage25350en_US
dc.identifier.issn2169-3536
dc.identifier.scopus2-s2.0-85151047205en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage25329en_US
dc.identifier.urihttps://doi.org/10.1109/ACCESS.2023.3256522
dc.identifier.urihttps://hdl.handle.net/20.500.14619/6102
dc.identifier.volume11en_US
dc.identifier.wosWOS:000953739000001en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherIeee-Inst Electrical Electronics Engineers Incen_US
dc.relation.ispartofIeee Accessen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectTask analysisen_US
dc.subjectProcessor schedulingen_US
dc.subjectEdge computingen_US
dc.subjectCloud computingen_US
dc.subjectJob shop schedulingen_US
dc.subjectResource managementen_US
dc.subjectComputational modelingen_US
dc.subjectresource schedulingen_US
dc.subjecttask offloadingen_US
dc.subjectfairnessen_US
dc.subjectload balancingen_US
dc.titleResource Scheduling in Edge Computing: Architecture, Taxonomy, Open Issues and Future Research Directionsen_US
dc.typeArticleen_US

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