ASTRA: Low-Overhead Runtime Architecture for STReam Adaptation in Video Analytics

RESUMO

Real-time video analytics is crucial for smart city applications and cloud-connected vehicle control. To improve analytics accuracy, it is desirable to process the video at the highest resolution and frame rate. However, due to limited resources, streaming and processing video at the highest resolution and frame rate from all cameras is not feasible and adversely affects the analytics latency. Intelligent adaptation of cameras' resolutions and frame rates based on network conditions and the video content is crucial in order to optimize the performance. In this paper, we present ASTRA, a low-overhead runtime architecture for online adaptation of live camera analytics at the edge. ASTRA can execute various online algorithms as a black box. We deployed ASTRA in the realistic NSF COSMOS testbed and uniquely assessed its real-time performance using COSMOS' street-level cameras. We further evaluated ASTRA with up to eight emulated cameras by streaming a comprehensive video dataset under real-world network conditions. We used ASTRA's architecture to evaluate the practical performance of several classes of adaptation algorithms, including theoretical and empirical methods. The results indicate that ASTRA can provide system reliability (i.e., the probability of meeting accuracy and latency requirements) of more than 90% while maintaining performance within a deviation of less than 10% from optimal offline performance with average GPU utilization overhead of around 2% per camera.