一句话：提出 Edge4Sys 端边协同框架，以无人机配送中的深度学习用户识别为例，显著降低网络流量与响应时间。
当前智能系统多依赖云计算，智能终端产生的大量数据需传输至云端部署的 AI 模型，常导致网络拥塞和高延迟。移动边缘计算（MEC）通过将计算密集型任务卸载至本地网络边缘服务器，成为有前景的解决方案，而充分发挥 MEC 优势需要终端设备与边缘服务器有效协同。本文作为初步探索，提出 Edge4Sys——面向 MEC 智能系统的设备-边缘协同框架。为验证有效性，作者以基于 MEC 的无人机（UAV）配送系统中的深度学习用户识别流程为案例开展研究。结果表明，该框架能显著减少网络流量并缩短响应时间。
At present, most of the smart systems are based on cloud computing, and massive data generated at the smart end device will need to be transferred to the cloud where AI models are deployed. Therefore, a big challenge for smart system engineers is that cloud based smart systems often face issues such as network congestion and high latency. In recent years, mobile edge computing (MEC) is becoming a promising solution which supports computation-intensive tasks such as deep learning through computation offloading to the servers located at the local network edge. To take full advantage of MEC, an effective collaboration between the end device and the edge server is essential. In this paper, as an initial investigation, we propose Edge4Sys, a Device-Edge Collaborative Framework for MEC based Smart System. Specifically, we employ the deep learning based user identification process in a MEC-based UAV (Unmanned Aerial Vehicle) delivery system as a case study to demonstrate the effectiveness of the proposed framework which can significantly reduce the network traffic and the response time.
DOI 原文 ·
@inproceedings{paperbot5876,
title = {Edge4Sys},
author = {Han Gao and Yi Xu and Xiao Liu and Jia Xu and Tianxiang Chen and Bowen Zhou and Rui Li and Xuejun Li},
booktitle = {ASE},
year = {2020},
doi = {10.1145/3324884.3418908}
}