Powered by deep learning, video analytic applications process millions of camera feeds in real-time to extract meaningful information from their surroundings. And this number grows by the minute. To avoid saturating the backhaul network and provide lower latencies, a distributed and heterogeneous edge cloud is postulated as a key enabler for widespread video analytics. This article provides a complete characterization of end-to-end video analytics across a set of hardware platforms and different neural network architectures. Each platform is selected to fill a different gap in a distributed, shared, and heterogeneous infrastructure. Moreover, we analyze how performance scales on each of these platforms with respect to the amount of resources dedicated to video analytics. Finally, we extract the key conclusions of the characterization to build an experimental model to estimate performance and cost of end-to-end video analytics in different edge scenarios. Our experiments show that managing video analytics workloads efficiently requires awareness of both, the platforms in which these are executed, and the full end-to-end pipeline. To the best of our knowledge, this is the first work that provides a complete characterization of end-to-end video analytics in heterogeneous edge platforms.

中文翻译：

---

异构边缘基础设施中视频分析工作负载的性能表征

在深度学习的支持下，视频分析应用程序实时处理数百万个摄像头输入，以从周围环境中提取有意义的信息。而且这个数字每分钟都在增长。为了避免回程网络饱和并提供更低的延迟，分布式和异构边缘云被假定为广泛视频分析的关键推动因素。本文提供了跨一组硬件平台和不同神经网络架构的端到端视频分析的完整特征。选择每个平台来填补分布式、共享和异构基础架构中的不同空白。此外，我们分析了每个平台的性能如何根据专用于视频分析的资源量进行扩展。最后，我们提取了表征的关键结论，以构建一个实验模型，以估计不同边缘场景中端到端视频分析的性能和成本。我们的实验表明，有效管理视频分析工作负载需要了解执行这些工作的平台以及完整的端到端管道。据我们所知，这是第一项在异构边缘平台中提供端到端视频分析的完整特征的工作。