蜘蛛:用于实时视频分析的多跳毫米波网络

Zhuqi Li, Yuanchao Shu, G. Ananthanarayanan, Longfei Shangguan, K. Jamieson, P. Bahl
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引用次数: 6

摘要

大规模视频分析系统由许多密集部署的摄像头和支持边缘服务器组成,正在推动智能零售商店和安全监控等许多领域的创新。为了支持这样的系统,挑战在于以最大化端到端应用程序目标的方式收集视频片段,并随着摄像机密度的增加而扩展这种性能以满足应用程序的需求。本文提出了Spider,一种多跳毫米波(mmWave)无线中继网络设计来满足这些需求。为了减轻物理毫米波链路阻塞,Spider将低延迟Wi-Fi控制平面与毫米波中继数据平面集成在一起,允许灵活地绕过阻塞重新路由。Spider提出了一种新颖的视频比特率分配算法,该算法与可扩展的路由算法相结合,可以携手实现最大化视频分析准确性的应用级目标,而不仅仅是最大化数据吞吐量。我们的实验评估结合了测试平台部署和跟踪驱动模拟,并与没有Spider算法的Wi-Fi和毫米波网格方案进行了比较。结果表明,与性能最好的比较方案相比,Spider能够支持高达176%的相机密度(增益2.76倍),使其能够单独满足现实世界的相机密度目标(4-250个相机/1,000平方米)。(视应用而定)。进一步的实验证明了Spider在出现故障时的可伸缩性,平均故障恢复时间减少了5.4-100倍。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Spider: A Multi-Hop Millimeter-Wave Network for Live Video Analytics
Massive video analytics systems, comprised of many densely-deployed cameras and supporting edge servers, are driving innovation in many areas including smart retail stores and security monitoring. To support such systems the challenge lies in collecting video footage in a way that maximizes end-to-end application goals, and scales this performance as camera density increases to meet application needs. This paper presents Spider, a multi-hop, millimeter-wave (mmWave) wireless relay network design that meets these needs. To mitigate physical mmWave link blockage, Spider integrates a low-latency Wi-Fi control plane with a mmWave relay data plane, allowing agile re-routing around blockages. Spider proposes a novel video bit-rate allocation algorithm coupled with a scalable routing algorithm that works hand-in-hand toward the application-level objective of maximizing video analytics accuracy, rather than simply maximizing data throughput. Our experimental evaluation uses a combination of testbed deployment and trace-driven simulation and compares against both Wi-Fi and mmWave mesh schemes that operate without Spider's algorithms. Results show that Spider is able to support camera densities up to 176% higher (gains of 2.76x) than the best-performing comparison scheme, allowing it alone to meet real-world camera density targets (4–250 cameras/1,000 sq. ft., depending on application). Further experiments demonstrate Spider's scalability in the presence of failures, with a 5.4-100x reduction in average failure recovery time.
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