  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%.
