Publication:

Application Placement in Fog Environments using Multi-Objective Reinforcement Learning with Maximum Reward Formulation

 
dc.contributor.authorEyckerman, Reinout
dc.contributor.authorReiter, Phil
dc.contributor.authorLatre, Steven
dc.contributor.authorMarquez-Barja, Johann
dc.contributor.authorHellinckx, Peter
dc.contributor.imecauthorEyckerman, Reinout
dc.contributor.imecauthorReiter, Phil
dc.contributor.imecauthorLatre, Steven
dc.contributor.imecauthorMarquez-Barja, Johann
dc.contributor.imecauthorHellinckx, Peter
dc.contributor.orcidimecEyckerman, Reinout::0000-0002-9352-7981
dc.contributor.orcidimecReiter, Phil::0000-0002-2548-7172
dc.contributor.orcidimecLatre, Steven::0000-0003-0351-1714
dc.contributor.orcidimecMarquez-Barja, Johann::0000-0001-5660-3597
dc.contributor.orcidimecHellinckx, Peter::0000-0001-8029-4720
dc.date.accessioned2023-01-04T10:44:52Z
dc.date.available2022-09-22T02:50:31Z
dc.date.available2022-10-04T14:20:46Z
dc.date.available2023-01-04T10:44:52Z
dc.date.embargo9999-12-31
dc.date.issued2022
dc.description.wosFundingTextThis research received funding from the Flemish Government (AI Research Program). This article describes work in the context of the DEDICAT 6G project under the European Union (EU) H2020 research and innovation programme (Grant Agreement No. 101016499). The contents of this publication are the sole responsibility of the authors and do not in any way reflect the views of the EU.
dc.identifier.doi10.1109/NOMS54207.2022.9789757
dc.identifier.eisbn978-1-6654-0601-7
dc.identifier.issn1542-1201
dc.identifier.urihttps://imec-publications.be/handle/20.500.12860/40480
dc.publisherIEEE
dc.source.conferenceIEEE/IFIP Network Operations and Management Symposium
dc.source.conferencedateAPR 25-29, 2022
dc.source.conferencelocationBudapest, Hungary
dc.source.journalIEEE/IFIP Network Operations and Management Symposium : [proceedings] : NOMS.
dc.source.numberofpages6
dc.subject.disciplineComputer science/information technology
dc.title

Application Placement in Fog Environments using Multi-Objective Reinforcement Learning with Maximum Reward Formulation

dc.typeProceedings paper
dspace.entity.typePublication
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