Multipath Computation Offloading for Mobile Augmented Reality

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Pysyväisosoite

http://hdl.handle.net/10138/318518

Lähdeviite

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

Julkaisun nimi: Multipath Computation Offloading for Mobile Augmented Reality
Tekijä: Braud, Tristan; Zhou, Pengyuan; Kangasharju, Jussi; Hui, Pan
Tekijän organisaatio: Department of Computer Science
Collaborative Networking research group / Jussi Kangasharju
Julkaisija: IEEE
Päiväys: 2020-03
Kieli: eng
Sivumäärä: 10
Kuuluu julkaisusarjaan: Proceedings of the IEEE International Conference on Pervasive Computing and Communications (PerCom 2020), Austin USA
Kuuluu julkaisusarjaan: International Conference on Pervasive Computing and Communications
ISBN: 978-1-7281-4658-4
978-1-7281-4657-7
ISSN: 2474-2503
DOI-tunniste: https://doi.org/10.1109/PerCom45495.2020.9127360
URI: http://hdl.handle.net/10138/318518
Tiivistelmä: 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.
Avainsanat: 113 Computer and information sciences
Vertaisarvioitu: Kyllä
Tekijänoikeustiedot: unspecified
Pääsyrajoitteet: openAccess
Rinnakkaistallennettu versio: acceptedVersion


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