边缘视频处理的细粒度划分与重组部署算法
Fine-Grained Partitioning and Reorganization Deployment Strategy of Edge Video Processing
- 电子学报 2021年49卷第11期 页码：2152-2159
- 作者机构：
1.重庆大学微电子与通信工程学院，重庆 400030
2.国网重庆市电力公司电力科学研究院，重庆 401123
- 作者简介：
[ "覃 剑 男，博士，1977年5月生，陕西宝鸡人.重庆大学微电子与通信工程学院副教授，研究方向为视频分析及传输.E-mail:qinjian@cqu.edu.cn" ]
[ "石昌伟 男，硕士，1993年5月生，山东菏泽人.重庆大学微电子与通信工程学院，研究方向为云计算与图像处理E-mail：593778745@qq.com" ]
- 基金信息：国家自然科学基金(61971077);天奥基金(2020154704)
DOI：10.12263/DZXB.20200767
中图分类号： TN919.8收稿：2020-07-23，
修回：2021-03-02，
纸质出版：2021-11-25
- 稿件说明：
移动端阅览
覃剑,石昌伟,张媛等.边缘视频处理的细粒度划分与重组部署算法[J].电子学报,2021,49(11):2152-2159.
QIN Jian,SHI Chang-wei,ZHANG Yuan,et al.Fine-Grained Partitioning and Reorganization Deployment Strategy of Edge Video Processing[J].ACTA ELECTRONICA SINICA,2021,49(11):2152-2159.
覃剑,石昌伟,张媛等.边缘视频处理的细粒度划分与重组部署算法[J].电子学报,2021,49(11):2152-2159. DOI： 10.12263/DZXB.20200767.
QIN Jian,SHI Chang-wei,ZHANG Yuan,et al.Fine-Grained Partitioning and Reorganization Deployment Strategy of Edge Video Processing[J].ACTA ELECTRONICA SINICA,2021,49(11):2152-2159. DOI： 10.12263/DZXB.20200767.
摘要
随着视频数据的迅速增长，大规模视频处理业务需求急剧增加.如何及时处理视频数据获取有效信息，进而向用户快速提供视频分析业务是亟待解决的重要问题.针对此问题，提出一种面向大规模视频处理的边缘功能模块化及重组部署方法（EFMR）.该方法将视频处理业务下沉到网络边缘，利用网络功能虚拟化，将边缘服务器中的视频业务请求根据其内在相关性进行功能细粒度划分，按需匹配并最大化复用资源，实现重组部署，从而以较小代价实现边缘视频业务处理功能的平滑扩展.实验结果表明，EFMR方法不仅降低了边缘服务器的接入与响应时延、业务的推理时间，而且还节省了大量的计算资源，提高了视频处理业务部署速度.
Abstract
With the rapid growth of video data
the demand for large-scale video processing tasks increases dramatically. How to process video data in time to obtain effective information and provide users with video analysis services quickly is an important issue to be solved. Aiming at this problem
a new deployment method of Edge Functions Modularized and Reorganized (EFMR) for large-scale video processing is proposed. This method sinks video processing services to the edge of the network. Using network function virtualization
video service requests sent to the edge server are divided fine-grainedly based on their inherent process correlation
and resources are matched and redeployed on demand based on the division results. In this way
we can smoothly expand the edge video service processing capabilities at a small cost. Experimental results show that EFMR method not only greatly reduces the edge server’s access and response delay
reduces the inference time
but also saves a lot of computing resources of edge servers and speeds up the deployment of video processing services.
关键词
Keywords
references
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