dc.contributor.author | Wu, Hongjia | |
dc.contributor.author | Alay, Özgü | |
dc.contributor.author | Brunstrom, Anna | |
dc.contributor.author | Ferlin, Simone | |
dc.contributor.author | Caso, Giuseppe | |
dc.date.accessioned | 2021-02-02T11:41:33Z | |
dc.date.accessioned | 2021-03-11T06:01:38Z | |
dc.date.available | 2021-02-02T11:41:33Z | |
dc.date.available | 2021-03-11T06:01:38Z | |
dc.date.issued | 2020-06-08 | |
dc.identifier.citation | Wu, Alay OA, Brunstrom, Ferlin, Caso. Peekaboo: Learning-Based Multipath Scheduling for Dynamic Heterogeneous Environments. IEEE Journal on Selected Areas in Communications. 2020 | en |
dc.identifier.issn | 0733-8716 | |
dc.identifier.issn | 1558-0008 | |
dc.identifier.uri | https://hdl.handle.net/10642/9989 | |
dc.description.abstract | Multipath 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.sponsorship | This work is partially funded by European Union’s Horizon 2020 research and innovation programme under grant agreement No. 815178 (5GENESIS). | en |
dc.language.iso | en | en |
dc.publisher | IEEE | en |
dc.relation.ispartofseries | IEEE 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.2020.3000365 | en |
dc.subject | Multipath scheduling | en |
dc.subject | Dynamic heterogeneous paths | en |
dc.subject | Multi armed bandits | en |
dc.subject | Stochastic adjustments | en |
dc.title | Peekaboo: Learning-Based Multipath Scheduling for Dynamic Heterogeneous Environments | en |
dc.type | Journal article | en |
dc.type | Peer reviewed | en |
dc.date.updated | 2021-02-02T11:41:33Z | |
dc.description.version | acceptedVersion | en |
dc.identifier.doi | https://doi.org/10.1109/JSAC.2020.3000365 | |
dc.identifier.cristin | 1865221 | |
dc.source.journal | IEEE Journal on Selected Areas in Communications | |