期刊
IEEE NETWORK
卷 34, 期 2, 页码 254-261出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/MNET.011.1900305
关键词
Collaboration; 5G mobile communication; Browsers; Object recognition; Servers; Energy consumption; Processor scheduling
类别
资金
- National Key R&D Program of China [2018YFE0205503]
- National Natural Science Foundation of China (NSFC) [61671081]
- Funds for International Cooperation and Exchange of NSFC [61720106007]
- 111 Project [B18008]
- Beijing Natural Science Foundation [4172042]
- Fundamental Research Funds for the Central Universities [2018XKJC01]
- BUPT Excellent Ph.D.
- Students Foundation [CX2019213]
- China Scholarship Council (CSC) [201906470033]
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.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据