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dc.contributor.authorKouloumpris, Andreasen
dc.contributor.authorTheocharides, Theocharisen
dc.contributor.authorMichael, Maria K.en
dc.coverage.spatialCrete Greeceen
dc.creatorKouloumpris, Andreasen
dc.creatorTheocharides, Theocharisen
dc.creatorMichael, Maria K.en
dc.date.accessioned2021-01-26T09:45:46Z
dc.date.available2021-01-26T09:45:46Z
dc.date.issued2019
dc.identifier.isbn978-1-4503-6640-3
dc.identifier.urihttp://gnosis.library.ucy.ac.cy/handle/7/63389
dc.description.abstractIncreased demands for real-time decision support and data analytics facilitate the need of performing significant computing away from the cloud and onto the IoT devices. In this paper, we propose Metis, a mathematical programming based framework, able to deliver an optimal task allocation when targeting a specific performance metric. Metis is currently suitable for systems which consist of an edge node, an intermediate node and the cloud infrastructure, but can be expanded to multi-Edge/Hub systems. Evaluation results using a real-life use-case scenario demonstrate that Metis provides the optimal task allocation by minimizing the overall latency of the system while taking into consideration the application's specific requirements and resource constraints of each computational unit.en
dc.language.isoenen
dc.publisherACMen
dc.sourceProceedings of the International Conference on Omni-Layer Intelligent Systemsen
dc.sourceCOINS '19: INTERNATIONAL CONFERENCE ON OMNI-LAYER INTELLIGENT SYSTEMSen
dc.source.urihttps://dl.acm.org/doi/10.1145/3312614.3312643
dc.titleMetis: Optimal Task Allocation Framework for the Edge/Hub/Cloud Paradigmen
dc.typeinfo:eu-repo/semantics/conferenceObject
dc.identifier.doi10.1145/3312614.3312643
dc.description.startingpage128
dc.description.endingpage133
dc.author.facultyΠολυτεχνική Σχολή / Faculty of Engineering
dc.author.departmentΤμήμα Ηλεκτρολόγων Μηχανικών και Μηχανικών Υπολογιστών / Department of Electrical and Computer Engineering
dc.type.uhtypeConference Objecten
dc.contributor.orcidTheocharides, Theocharis [0000-0001-7222-9152]
dc.gnosis.orcid0000-0001-7222-9152


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