Soudain: Online Adaptive Profile Configuration for Real-time Video Analytics

Since the real-time video analytics with high accuracy requirement is resource-consuming, the profiles regarding such resource-accuracy trade-off are needed before the analytics for better resource allocation at resource-constrained edges. With the inner changes of the video contents, outdated profiles fail to capture the trade-off dynamically over time, which requires the profiles to be updated periodically and incurs an overwhelming resource overhead. Thus, we present Soudain, which dynamically adjusts the configurations in profiles and corresponding profiling intervals to capture the inner changes of multiple video streams at edges. Upon the fine-grained decisions for profiles, we propose an integer program to maximize the accuracy of video analytics in a long-term scope with resource constraint, and then design an algorithm to adjust the profiles in an online manner. We implement Soudain upon the server with GPU. Our testbed evaluations confirm that, by using the live video streams derived from real-world traffic cameras, Soudain ensures the real-time requirement and achieves up to 25% improvement on the detection accuracy, compared with multiple state-of-the-art alternatives.