面向边缘实时视频分析的效用感知负载削减
Utility-Aware Load Shedding for Real-time Video Analytics at the Edge
AI总结：
本文提出一种基于像素级颜色特征计算效用分数并动态调整阈值的轻量级负载削减器，通过丢弃不感兴趣视频帧，在满足端到端延迟约束的同时减少边缘视频分析的计算和网络资源消耗。
摘要：
实时视频分析通常要求对视频帧执行查询处理，以识别感兴趣的对象或活动，同时满足端到端帧处理延迟约束。由于需要流式传输并处理所有视频帧，此类应用对后端计算和网络基础设施施加了持续且沉重的负载。视频数据具有固有的冗余性，并且对于给定查询并不总是包含感兴趣的对象。我们利用视频流的这一特性，提出了一种轻量级负载削减器（Load Shedder），它可以部署在边缘服务器上，或部署在与摄像头同址的低成本边缘设备上，并丢弃不感兴趣的视频帧。所提出的负载削减器使用基于像素级颜色的特征为每个输入视频帧计算效用分数，该分数表示该帧对当前查询的效用。负载削减器使用最小效用阈值来选择要发送用于查询处理的感兴趣帧。丢弃不必要的帧使后端的视频分析查询能够以更少的计算和网络资源满足端到端延迟约束。为了保证运行时端到端延迟有界，我们引入了一个控制回路，该回路监控给定查询的后端负载并动态调整效用阈值。性能评估表明，所提出的负载削减器在满足端到端帧处理延迟约束的同时，选择了包含每个感兴趣对象的大部分帧。此外，当在计算资源适度的边缘设备上运行时，负载削减器不会带来显著的延迟开销。
英文摘要：
Real-time video analytics typically require video frames to be processed by a query to identify objects or activities of interest while adhering to an end-to-end frame processing latency constraint. Such applications impose a continuous and heavy load on backend compute and network infrastructure because of the need to stream and process all video frames. Video data has inherent redundancy and does not always contain an object of interest for a given query. We leverage this property of video streams to propose a lightweight Load Shedder that can be deployed on edge servers or on inexpensive edge devices co-located with cameras and drop uninteresting video frames. The proposed Load Shedder uses pixel-level color-based features to calculate a utility score for each ingress video frame, which represents the frame's utility toward the query at hand. The Load Shedder uses a minimum utility threshold to select interesting frames to send for query processing. Dropping unnecessary frames enables the video analytics query in the backend to meet the end-to-end latency constraint with fewer compute and network resources. To guarantee a bounded end-to-end latency at runtime, we introduce a control loop that monitors the backend load for the given query and dynamically adjusts the utility threshold. Performance evaluations show that the proposed Load Shedder selects a large portion of frames containing each object of interest while meeting the end-to-end frame processing latency constraint. Furthermore, the Load Shedder does not impose a significant latency overhead when running on edge devices with modest compute resources.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
