Multipath Computation Offloading for Mobile Augmented Reality

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dc.contributor.author Braud, Tristan
dc.contributor.author Zhou, Pengyuan
dc.contributor.author Kangasharju, Jussi
dc.contributor.author Hui, Pan
dc.date.accessioned 2020-08-24T13:20:01Z
dc.date.available 2020-08-24T13:20:01Z
dc.date.issued 2020-03
dc.identifier.citation Braud , T , Zhou , P , Kangasharju , J & Hui , P 2020 , Multipath Computation Offloading for Mobile Augmented Reality . in Proceedings of the IEEE International Conference on Pervasive Computing and Communications (PerCom 2020), Austin USA . International Conference on Pervasive Computing and Communications , IEEE , PerCom 2020: Annual IEEE Conference on Pervasive Computing and Communications , Austin , United States , 23/03/2020 . https://doi.org/10.1109/PerCom45495.2020.9127360
dc.identifier.citation conference
dc.identifier.other PURE: 131007028
dc.identifier.other PURE UUID: 44f1ba92-7856-473f-a639-384ec07382ff
dc.identifier.other ORCID: /0000-0001-6119-1638/work/79517405
dc.identifier.other ORCID: /0000-0002-7909-4059/work/79521294
dc.identifier.other WOS: 000612702600009
dc.identifier.uri http://hdl.handle.net/10138/318518
dc.description.abstract Mobile Augmented Reality (MAR) applications employ computationally demanding vision algorithms on resource-limited devices. In parallel, communication networks are becoming more ubiquitous. Offloading to distant servers can thus overcome the device limitations at the cost of network delays. Multipath networking has been proposed to overcome network limitations but it is not easily adaptable to edge computing due to the server proximity and networking differences. In this article, we extend the current mobile edge offloading models and present a model for multi-server device-to-device, edge, and cloud offloading. We then introduce a new task allocation algorithm exploiting this model for MAR offloading. Finally, we evaluate the allocation algorithm against naive multipath scheduling and single path models through both a real-life experiment and extensive simulations. In case of sub-optimal network conditions, our model allows reducing the latency compared to single-path offloading, and significantly decreases packet loss compared to random task allocation. We also display the impact of the variation of WiFi parameters on task completion. We finally demonstrate the robustness of our system in case of network instability. With only 70% WiFi availability, our system keeps the excess latency below 9 ms. We finally evaluate the capabilities of the upcoming 5G and 802.11ax. en
dc.format.extent 10
dc.language.iso eng
dc.publisher IEEE
dc.relation.ispartof Proceedings of the IEEE International Conference on Pervasive Computing and Communications (PerCom 2020), Austin USA
dc.relation.ispartofseries International Conference on Pervasive Computing and Communications
dc.relation.isversionof 978-1-7281-4658-4
dc.relation.isversionof 978-1-7281-4657-7
dc.rights unspecified
dc.rights.uri info:eu-repo/semantics/openAccess
dc.subject 113 Computer and information sciences
dc.title Multipath Computation Offloading for Mobile Augmented Reality en
dc.type Conference contribution
dc.contributor.organization Department of Computer Science
dc.contributor.organization Collaborative Networking research group / Jussi Kangasharju
dc.description.reviewstatus Peer reviewed
dc.relation.doi https://doi.org/10.1109/PerCom45495.2020.9127360
dc.relation.issn 2474-2503
dc.rights.accesslevel openAccess
dc.type.version acceptedVersion

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