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dc.contributor.authorWu, Hongjia
dc.contributor.authorAlay, Özgü
dc.contributor.authorBrunstrom, Anna
dc.contributor.authorFerlin, Simone
dc.contributor.authorCaso, Giuseppe
dc.date.accessioned2021-02-02T11:41:33Z
dc.date.accessioned2021-03-11T06:01:38Z
dc.date.available2021-02-02T11:41:33Z
dc.date.available2021-03-11T06:01:38Z
dc.date.issued2020-06-08
dc.identifier.citationWu, Alay OA, Brunstrom, Ferlin, Caso. Peekaboo: Learning-Based Multipath Scheduling for Dynamic Heterogeneous Environments. IEEE Journal on Selected Areas in Communications. 2020en
dc.identifier.issn0733-8716
dc.identifier.issn1558-0008
dc.identifier.urihttps://hdl.handle.net/10642/9989
dc.description.abstractMultipath transport protocols utilize multiple network paths (e.g., WiFi and cellular) to achieve improved performance and reliability, compared with their single-path counterparts. The scheduler of a multipath transport protocol determines how to distribute the data packets onto different paths. However, state-of-the-art multipath schedulers face the challenge when dealing with heterogeneous paths with dynamic path characteristics (i.e., packet loss, fluctuation of delay). In this paper, we propose Peekaboo, a novel learning-based multipath scheduler that is aware of the dynamic characteristics of the heterogeneous paths. Peekaboo is able to learn scheduling decisions to adopt over time based on the current path characteristics and dynamicity levels - from both deterministic and stochastic perspectives. We implement Peekaboo in Multipath QUIC (MPQUIC) and compare it with state-of-the-art multipath schedulers for a wide range of dynamic heterogeneous environments, upon both emulated and real networks. Our results show that Peekaboo outperforms the other schedulers by up to 31.2% in emulated networks and up to 36.3% in real network scenarios.en
dc.description.sponsorshipThis work is partially funded by European Union’s Horizon 2020 research and innovation programme under grant agreement No. 815178 (5GENESIS).en
dc.language.isoenen
dc.publisherIEEEen
dc.relation.ispartofseriesIEEE Journal on Selected Areas in Communications;Volume: 38, Issue: 10
dc.rights© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. DOI: http://doi.org/10.1109/JSAC.2020.3000365en
dc.subjectMultipath schedulingen
dc.subjectDynamic heterogeneous pathsen
dc.subjectMulti armed banditsen
dc.subjectStochastic adjustmentsen
dc.titlePeekaboo: Learning-Based Multipath Scheduling for Dynamic Heterogeneous Environmentsen
dc.typeJournal articleen
dc.typePeer revieweden
dc.date.updated2021-02-02T11:41:33Z
dc.description.versionacceptedVersionen
dc.identifier.doihttps://doi.org/10.1109/JSAC.2020.3000365
dc.identifier.cristin1865221
dc.source.journalIEEE Journal on Selected Areas in Communications


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