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首页> 外文期刊>IEEE Network >Edge-Assisted Distributed DNN Collaborative Computing Approach for Mobile Web Augmented Reality in 5G Networks
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Edge-Assisted Distributed DNN Collaborative Computing Approach for Mobile Web Augmented Reality in 5G Networks

机译：在5G网络中的移动网络增强现实的边缘辅助分布式DNN协作计算方法

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摘要

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.
机译：基于Web的DNN为移动Web AR提供准确的对象识别，这是一种作为轻量级移动AR解决方案的新出现。 Websbased DNN吸引了大量的关注。然而，对网络上的基于DNN的对象识别的计算成本平衡了UX对于自包含的和基于云的卸载方法难以实现，因为它是一个延迟敏感的服务，但在计算和计算方面也具有很高的要求网络能力。幸运的是，新兴的5G网络不仅承诺带宽和延迟改善，而且还承诺，也是更靠近用户的边缘服务器的普遍部署。在本文中，我们提出了在5G时代的移动Web AR中基于边缘的协作对象识别解决方案。首先，我们探索云，边缘和移动Web浏览器之间的协作的FineGremator和Adaptive DNN分区。其次，我们提出了一种专门为边缘平台设计的差异化的DNN计算调度方法。一方面，在移动网上执行部分DNN计算而不减少UX（即，低于特定阈值的响应延迟）将有效地降低云系统的计算成本;另一方面，与自包含的解决方案相比，执行云上的剩余DNN计算（包括远程和边缘云）也将提高推理延迟，因此UX。显然，我们的协作解决方案将平衡用户和服务提供商的利益。实验在实际部署的5G试用网络中进行了实验，结果表明了我们提出的协作解决方案的优越性。

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