发表机构
Columbia University（哥伦比亚大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
ASTRA是一种低开销边缘运行时架构，通过黑盒在线自适应摄像头分辨率与帧率，在真实测试平台上实现超90%系统可靠性，性能偏差小于10%，GPU开销约2%。
AI 中文摘要
实时视频分析对于智慧城市应用和云连接车辆控制至关重要。为了提高分析准确性，理想情况下应以最高分辨率和帧率处理视频。然而，由于资源有限，从所有摄像头以最高分辨率和帧率进行流式传输和处理并不可行，并且会对分析延迟产生不利影响。基于网络条件和视频内容对摄像头的分辨率和帧率进行智能自适应，对于优化性能至关重要。在本文中，我们提出了ASTRA，一种用于边缘实时摄像头分析在线自适应的低开销运行时架构。ASTRA可以将各种在线算法作为黑盒执行。我们在真实的NSF COSMOS测试平台上部署了ASTRA，并利用COSMOS的街道级摄像头独特地评估了其实时性能。我们进一步通过在实际网络条件下流式传输一个综合视频数据集，使用多达八个模拟摄像头对ASTRA进行了评估。我们利用ASTRA的架构评估了几类自适应算法的实际性能，包括理论方法和经验方法。结果表明，ASTRA可以提供超过90%的系统可靠性（即满足准确性和延迟要求的概率），同时保持性能与最优离线性能的偏差小于10%，每摄像头的平均GPU利用率开销约为2%。
英文摘要
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.
