DNN-based video analytics have empowered many new applications (e.g., automated retail). Meanwhile, the proliferation of fog devices provides developers with more design options to improve performance and save cost. To the best of our knowledge, this paper presents the first serverless system that takes full advantage of the client-fog-cloud synergy to better serve the DNN-based video analytics. Specifically, the system aims to achieve two goals: 1) Provide the optimal analytics results under the constraints of lower bandwidth usage and shorter round-trip time (RTT) by judiciously managing the computational and bandwidth resources deployed in the client, fog, and cloud environment. 2) Free developers from tedious administration and operation tasks, including DNN deployment, cloud and fog's resource management. To this end, we implement a holistic cloud-fog system referred to as VPaaS (Video-Platform-as-a-Service). VPaaS adopts serverless computing to enable developers to build a video analytics pipeline by simply programming a set of functions (e.g., model inference), which are then orchestrated to process videos through carefully designed modules. To save bandwidth and reduce RTT, VPaaS provides a new video streaming protocol that only sends low-quality video to the cloud. The state-of-the-art (SOTA) DNNs deployed at the cloud can identify regions of video frames that need further processing at the fog ends. At the fog ends, misidentified labels in these regions can be corrected using a light-weight DNN model. To address the data drift issues, we incorporate limited human feedback into the system to verify the results and adopt incremental learning to improve our system continuously. The evaluation demonstrates that VPaaS is superior to several SOTA systems: it maintains high accuracy while reducing bandwidth usage by up to 21%, RTT by up to 62.5%, and cloud monetary cost by up to 50%.
翻译:以 DNN 为基础的 VNN 视频解析器赋予了许多新的应用程序( 如自动化零售) 。 与此同时, 雾设备的扩散为开发者提供了更多的设计选项, 以提高性能和节省成本。 根据我们的最佳知识, 本文展示了第一个充分利用客户- 泡沫- 球式协同增效的无服务器系统, 以便更好地为基于 DNN 的视频解析器服务服务服务服务服务。 具体来说, 系统的目的是实现两个目标 :(1) 在低带宽使用和较短循环时间( RTT)的限制下, 提供最佳解析结果。 通过明智地管理客户、 雾和云环境中的计算和带宽资源。 2 自由开发者从乏味的管理和操作任务, 包括 DNNNN、 云和雾的资源管理。 为此, 我们实施一个名为 VPAA( Video- Platforma- as- servicice) 的全面的云式系统。 VPaS 使用不端计算器来让开发者能够进一步构建一个视频解析分析管道, 只需编程( e. g.