Adaptive Configuration Selection and Bandwidth Allocation for Edge-Based Video Analytics

计算机科学 分析 服务器 边缘计算 计算机网络 带宽分配 能源消耗 实时计算 Lyapunov优化 边缘设备 云计算
作者
Sheng Zhang,Can Wang,Yibo Jin,Jie Wu,Zhuzhong Qian,Mingjun Xiao,Sanglu Lu
出处
期刊:IEEE ACM Transactions on Networking [Institute of Electrical and Electronics Engineers]
卷期号:30 (1): 285-298
标识
DOI:10.1109/tnet.2021.3106937
摘要

Major cities worldwide have millions of cameras deployed for surveillance, business intelligence, traffic control, crime prevention, etc. Real-time analytics on video data demands intensive computation resources and high energy consumption. Traditional cloud-based video analytics relies on large centralized clusters to ingest video streams. With edge computing, we can offload compute-intensive analysis tasks to nearby servers, thus mitigating long latency incurred by data transmission via wide area networks. When offloading video frames from the front-end device to an edge server, the application configuration (i.e., frame sampling rate and frame resolution) will impact several metrics, such as energy consumption, analytics accuracy and user-perceived latency. In this paper, we study the configuration selection and bandwidth allocation for multiple video streams, which are connected to the same edge node sharing an upload link. We propose an efficient online algorithm, called JCAB, which jointly optimizes configuration adaption and bandwidth allocation to address a number of key challenges in edge-based video analytics systems, including edge capacity limitation, unknown network variation, intrusive dynamics of video contents. Our algorithm is developed based on Lyapunov optimization and Markov approximation, works online without requiring future information, and achieves a provable performance bound. We also extend the proposed algorithms to the multi-edge scenario in which each user or video stream has an additional choice about which edge server to connect. Extensive evaluation results show that the proposed solutions can effectively balance the analytics accuracy and energy consumption while keeping low system latency in a variety of settings.
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