Multipath Computation Offloading for Mobile Augmented Reality

TY - GEN

T1 - Multipath Computation Offloading for Mobile Augmented Reality

AU - Braud, Tristan

AU - Zhou, Pengyuan

AU - Kangasharju, Jussi

AU - Hui, Pan

N1 - Publisher Copyright: © 2020 IEEE.

PY - 2020/3

Y1 - 2020/3

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=85088700656&partnerID=8YFLogxK

U2 - 10.1109/PerCom45495.2020.9127360

DO - 10.1109/PerCom45495.2020.9127360

M3 - Article in proceedings

AN - SCOPUS:85088700656

BT - 18th Annual IEEE International Conference on Pervasive Computing and Communications, PerCom 2020

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 18th Annual IEEE International Conference on Pervasive Computing and Communications, PerCom 2020

Y2 - 23 March 2020 through 27 March 2020

ER -