DC Field | Value | Language |
---|---|---|
dc.contributor.author | Lee, DongHoon | ko |
dc.contributor.author | Chong, Song | ko |
dc.date.accessioned | 2023-06-23T07:00:16Z | - |
dc.date.available | 2023-06-23T07:00:16Z | - |
dc.date.created | 2023-06-08 | - |
dc.date.issued | 2018-06 | - |
dc.identifier.citation | 13th International Conference on Future Internet Technologies, CFI 2018 | - |
dc.identifier.uri | http://hdl.handle.net/10203/308714 | - |
dc.description.abstract | This poster addresses the problem of Network Utility Maximization (NUM) where multiple resources (computing/networking) participate in user services. NUM has usually been solved by Backpressure algorithms which has to build up queue size gradualy. This disadvantage stands out in the situation of multi-resource environment or multi-hop networking. To address the problem, we propose a reinforcement learning based algorithm that utilizes future prediction to overcome the previous limitation of non-learning based algorithms. | - |
dc.language | English | - |
dc.publisher | Association for Computing Machinery | - |
dc.title | Learning based utility maximization for multi-resource management | - |
dc.type | Conference | - |
dc.identifier.scopusid | 2-s2.0-85053681625 | - |
dc.type.rims | CONF | - |
dc.citation.publicationname | 13th International Conference on Future Internet Technologies, CFI 2018 | - |
dc.identifier.conferencecountry | KO | - |
dc.identifier.conferencelocation | Seoul | - |
dc.identifier.doi | 10.1145/3226052.3226060 | - |
dc.contributor.localauthor | Chong, Song | - |
dc.contributor.nonIdAuthor | Lee, DongHoon | - |
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