Cost-Efficient End-Edge-Cloud Collaboration for Real-Time Multi-Task Video Analytics

As a killer app of edge computing, real-time video analytics has found its wide usage in diverse applications, such as security surveillance and manufacturing automation. Unlike state-of-the-art efforts in edge video analytics, which primarily focus on single-task scenarios, we address multi-task video analytics, enabling concurrent execution of multiple tasks on a single video stream. Specifically, our approach aims to minimize monetary costs for the edge service provider through efficient query and resource scheduling, while meeting accuracy and latency requirements of diverse video analytics tasks. A crucial prerequisite for this is to determine the relationship between video analytics accuracy and system configuration parameters. We design a Transformer-aided configuration-accuracy predictor to capture both the current video content and inter-frame temporal dependencies, generating precise configuration-accuracy profiles in real-time. To better exploit the scarce communication and computing resources, a query merging technique is employed, which allows queries from the same camera to share the network bandwidth and neural network models, leading to reduced resource consumption. A heuristic algorithm is then readily proposed to schedule video queries and resources, which dynamically adapts video configurations, query merging, video analytics model selection, task placement, and GPU provisioning. Experimental results show that our system achieves near-optimal performance and outperforms state-of-the-art methods, demonstrating the superiority of our collaboration scheme.