Lizard：通过最后一英里边缘路由器上的内容感知数据包丢弃实现带宽自适应的实时视频分析
Lizard: Bandwidth-Adaptive Real-Time Video Analytics through Content-Aware Packet Discarding at Last-Mile Edge Routers
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中文总结 AI 辅助
Lizard系统通过在最后一英里边缘路由器上利用内容感知的数据包丢弃，在带宽下降时降低延迟53.2%并提升分析准确性27.1%。
中文摘要 AI 辅助
边缘视频分析的及时性和准确性可能受到最后一英里边缘路由器可用带宽（ABW）急剧下降的阻碍，导致排队延迟延长。这项工作提出了Lizard，一种利用视频内容感知的数据包丢弃来减轻最后一英里边缘路由器可能频繁发生的剧烈ABW下降负面影响的系统，通过明智地丢弃包含对目的地分析不太重要的帧块的数据包。为实现这一目标，我们首先设计了一种帧块感知的RTP头扩展，以有效解耦数据包依赖关系来编码帧块。其次，Lizard使用基于优先级的反馈机制，根据相对准确性影响动态评估数据包优先级。第三，我们在路由器上开发了一种基于自适应相位转换的数据包丢弃策略，以丢弃代表不重要块的数据包。我们对Lizard的评估显示，与现有方法相比改进显著：延迟降低53.2%，分析准确性提高27.1%。
英文摘要
The timeliness and accuracy of edge-based video analytics can be hindered by drastic reductions in available bandwidth (ABW) at last-mile edge routers, causing prolonged queuing delays. This work proposes Lizard, a system that leverages video-content-aware packet discarding to mitigate the negative effects of drastic ABW degradation that may frequently occur at a last-mile edge router by judiciously discarding packets that contain frame blocks less important to the analytics at the destination. To achieve this, we first devise a frame-block-aware RTP header extension to effectively decouple packet dependencies to encode frame blocks. Second, Lizard uses a priority-based feedback mechanism that dynamically evaluates packet priorities based on relative accuracy impacts. Third, we develop an adaptive phase-transition-based packet discarding strategy at the router to discard packets that represent unimportant blocks. Our evaluation of Lizard shows improvements over existing methods are substantial: 53.2% reduction in latency and 27.1% increase in analysis accuracy.
发表机构
- University of California, Los Angeles（加州大学洛杉矶分校）
- Renmin University of China（中国人民大学）
- University of California, Berkeley（加州大学伯克利分校）
- Nanjing University（南京大学）
- Princeton University（普林斯顿大学）
机构由 AI 辅助整理，请以论文原文为准。
