云边端架构下边缘智能计算关键问题综述：计算优化与计算卸载

董裕民; 张静; 谢昌佐; 李子扬

doi:10.11999/JEIT230390

云边端架构下边缘智能计算关键问题综述：计算优化与计算卸载

doi: 10.11999/JEIT230390

董裕民^{1, 2, 3},
张静^{1, 2},
谢昌佐^{1, 2, 3},
李子扬^{1, 2, ,}

1.
中国科学院空天信息创新研究院北京 100094
2.
中国科学院定量遥感信息技术重点实验室北京 100094
3.
中国科学院大学电子电气与通信工程学院北京 100049

基金项目: 国家重点研发计划 (2021YFC3002204)

详细信息

作者简介:
董裕民：男，博士生，研究方向为边缘计算、知识工程等

张静：女，硕士，高级工程师，研究方向为遥感信息质量控制、分布式智能决策等

谢昌佐：男，硕士生，研究方向为分布式计算和边缘计算等

李子扬：男，博士，研究员，研究方向为边缘计算、并行计算等

通讯作者:
李子扬　zyli@aircas.ac.cn

中图分类号: TN919.1; TP393.09
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- 被引次数: 0
出版历程
- 收稿日期: 2023-05-09
- 修回日期: 2023-07-07
- 网络出版日期: 2023-07-14
- 刊出日期: 2024-03-27

A Survey of Key Issues in Edge Intelligent Computing Under Cloud-Edge-Terminal Architecture: Computing Optimization and Computing Offloading

DONG Yumin^{1, 2, 3},
ZHANG Jing^{1, 2},
XIE Changzuo^{1, 2, 3},
LI Ziyang^{1, 2
, ,}

1.
Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
2.
Key Laboratory of Quantitative Remote Sensing Information Technology, Chinese Academy of Sciences, Beijing 100094, China
3.
School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, China

Funds: National Key R&D Program (2021YFC3002204)

摘要

摘要: 近年来，随着入网设备数量与数据体量的急剧增加，以云计算为代表的中心式计算模式的缺点越来越显露出来。边缘计算，即让计算尽量靠近数据源，以减少数据传输时间和网络延迟，作为云计算的补充，已经成为学术界和工业界关注的焦点。该文面向边缘计算中应用较广的实例架构—云边端架构，以及边缘计算的典型应用—边缘智能计算，讨论云边端架构下边缘智能计算的两大关键问题：计算优化和计算卸载。首先分析和梳理了云边端架构下边缘智能计算优化的应用与研究现状。然后讨论了云边端架构下计算卸载的研究思路和现状。最后，总结提出了目前云边端架构下边缘智能计算业务所面临的挑战和未来研究趋势。
- 云边端架构 /
- 边缘智能计算 /
- 计算优化 /
- 计算卸载
Abstract: With the rapid increase in the number of network access devices and the volume of network access data currently, the shortcomings of the centralized computing architecture represented by cloud computing are increasingly exposed. Edge computing, that is making computing as close to the data source as possible to reduce data transmission time and network delay, has become the focus of academia and industry as a supplement to cloud computing. An instance architecture widely used in edge computing: Cloud-Edge-Terminal architecture, and a typical application of edge computing: edge intelligent computing is focused on in this paper. Two key issues of edge intelligent computing under Cloud-Edge-Terminal architecture: computing optimization and computing offloading is analyzed. First, the research focus of edge intelligent computing is analyzed, and the application and research status of intelligent computing optimization under Cloud-Edge-Terminal architecture is combed. Then the research ideas and current situation of computing offloading under Cloud-Edge-Terminal architecture is discussed. Finally, the challenges and research trends of edge intelligent computing under Cloud-Edge-Terminal architecture is summarized.
- Cloud-Edge-Terminal /
- Edge intelligent computing /
- Computing optimization /
- Computing offloading

HTML全文

图 1 边缘计算与智能计算结合的趋势

下载: 全尺寸图片幻灯片

图 2 云边端架构特点

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图 3 中心式计算卸载流程

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图 4 边缘式计算卸载流程

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图 5 基于“两种选择的力量”策略的卸载流程

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表 1 边缘智能计算框架对比

	TensorFlow Lite	PyTorch Mobile	MindSpore Lite	Paddle Lite
开发者	谷歌	Linux基金会	华为	百度
支持功能	推理	训练+推理	训练+推理	推理
支持的操作系统	Android iOS Linux	iOS Android	Android iOS OpenHarmony Linux Windows	Android iOS Linux Windows macOS

下载: 导出CSV

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