FedVision: Federated Video Analytics With Edge Computing
FedVision: Federated Video Analytics With Edge Computing
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DOI：
10.1109/ojcs.2020.2996184
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发表时间：
2020-01-01
影响因子：
5.9
通讯作者：
Ansari, Nirwan
中科院分区：
文献类型：
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作者：
Deng, Yang;Han, Tao;Ansari, Nirwan
广泛部署的智能摄像机正在生成大量视频数据，并能够在设备上处理帧。在边缘计算的支持下，视频数据也可以卸载到边缘服务器进行处理。通过利用设备上的处理和计算卸载，我们提出了一个名为FedVision的联合视频分析系统，以有效地跨设备和服务器提供视频分析。设计FedVision的挑战在于如何最佳地使用计算和网络资源进行视频分析。由于系统性能没有封闭形式的表达式，采用黑盒优化来优化系统性能。然而，使用黑盒优化直接导致过多的系统查询，导致非常差的系统性能。为了解决这个问题，我们设计了一种新的优化方法，将黑箱优化与神经过程（NP）作为系统性能近似器。这种方法允许黑盒优化器查询NP而不是真实的系统。我们使用数值计算结果和测试平台的实验来验证FedVision和新优化方法的性能。
Widely deployed smart cameras are generating a large amount of video data and capable of processing frames on devices. Empowered by edge computing, the video data can also be offloaded to edge servers for processing. By leveraging the on-device processing and computation offloading, we propose a federated video analytics system named FedVision to efficiently provision video analytics across devices and servers. The challenge of designing FedVision is to optimally use the computing and networking resources for video analytics. Since there is no closed-form expression of the system performance, black-box optimization is employed to optimize the system performance. However, using black-box optimization directly incurs excessive system queries that lead to very poor system performance. To solve this problem, we design a new optimization method that integrates black-box optimization with Neural Processes (NPs) as a system performance approximator. This method allows black-box optimizer to query NPs instead of the real system. We validate the performance of FedVision and the new optimization method using both numerical results and experiments with a testbed.