부산대학교 도서관

Millions of video cameras are deployed globally across major cities for learning-based video analytic (VA) applications, such as object detection. Video streams from the cameras are either sent over the wide-area network to be processed by the cloud or are (at least partially) processed in a local edge workstation, incurring significant latency and elevated financial costs. In this paper, to minimize reliance on the cloud and overcome the unavailability of high-compute workstations on edge, we investigate the use of heterogeneous and distributed embedded devices as edge nodes shared by multiple cameras to fully serve the video processing needs of a VA application (without requiring cloud support). We present OVIDA, an edge-only orchestrator to deploy VA application(s) on a distributed edge environment to maximize accuracy. Given the resource-constrained nature of edge nodes, OVIDA disaggregates the VA application pipeline into multiple modules. OVIDA's core functionality and contributions are: (i) optimizing the placement and replication of the VA application modules across the edge nodes to maximize the throughput, and in turn, accuracy; and (ii) an adaptive model selection algorithm for VA modules based on accuracy-throughput tradeoff to maximize accuracy in response to varying load conditions. To further improve performance, OVIDA employs a central-queue-based design (instead of the usual push-based design), which also obviates the need for complex load balancing algorithms. We implement OVIDA on top of Kubernetes and evaluate its performance for three VA applications, supported over a heterogeneous edge cluster under varying network conditions. When compared against several baselines in our evaluation, we achieve throughput and accuracy gains of at least 51% and 28%.