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Zeitschriftenartikel:

P. Ren, X. Qiao, Y. Huang, L. Liu, S. Dustdar, J. Chen:
"Edge-Assisted Distributed DNN Collaborative Computing Approach for Mobile Web Augmented Reality in 5G Networks";
IEEE Network, Volume 34 (2020), Issue 2; S. 254 - 261.



Kurzfassung englisch:
Web-based DNNs provide accurate object recognition to the mobile Web AR, which is newly emerging as a lightweight mobile AR solution. Webbased DNNs are attracting a great deal of attention. However, balancing the UX against the computing cost for DNN-based object recognition on the Web is difficult for both self-contained and cloud-based offloading approaches, as it is a latency-sensitive service but also has high requirements in terms of computing and networking abilities. Fortunately, the emerging 5G networks promise not only bandwidth and latency improvement but also the pervasive deployment of edge servers which are closer to the users. In this article, we propose the first edge-based collaborative object recognition solution for mobile Web AR in the 5G era. First, we explore the finegrained and adaptive DNN partitioning for the collaboration between the cloud, the edge, and the mobile Web browser. Second, we propose a differentiated DNN computation scheduling approach specially designed for the edge platform. On one hand, performing part of DNN computations on mobile Web without decreasing the UX (i.e., keep response latency below a specific threshold) will effectively reduce the computing cost of the cloud system; on the other hand, performing the remaining DNN computations on the cloud (including remote and edge cloud) will also improve the inference latency and thus UX when compared to the self-contained solution. Obviously, our collaborative solution will balance the interests of both users and service providers. Experiments have been conducted in an actually deployed 5G trial network, and the results show the superiority of our proposed collaborative solution.


"Offizielle" elektronische Version der Publikation (entsprechend ihrem Digital Object Identifier - DOI)
http://dx.doi.org/10.1109/MNET.011.1900305


Erstellt aus der Publikationsdatenbank der Technischen Universitšt Wien.