Cloud-edge collaborative computing paradigm is a promising solution to high-resolution video analytics systems. The key lies in reducing redundant data and managing fluctuating inference workloads effectively. Previous work has focused on extracting regions of interest (RoIs) from videos and transmitting them to the cloud for processing. However, a naive Infrastructure as a Service (IaaS) resource configuration falls short in handling highly fluctuating workloads, leading to violations of Service Level Objectives (SLOs) and inefficient resource utilization. Besides, these methods neglect the potential benefits of RoIs batching to leverage parallel processing. In this work, we introduce Tangram, an efficient serverless cloud-edge video analytics system fully optimized for both communication and computation. Tangram adaptively aligns the RoIs into patches and transmits them to the scheduler in the cloud. The system employs a unique ``stitching'' method to batch the patches with various sizes from the edge cameras. Additionally, we develop an online SLO-aware batching algorithm that judiciously determines the optimal invoking time of the serverless function. Experiments on our prototype reveal that Tangram can reduce bandwidth consumption and computation cost up to 74.30\% and 66.35\%, respectively, while maintaining SLO violations within 5\% and the accuracy loss negligible.

翻译：云边协同计算范式是高分辨率视频分析系统的一个有前景的解决方案。其关键在于有效减少冗余数据并管理波动的推理工作负载。以往的研究侧重于从视频中提取感兴趣区域（RoI）并将其传输至云端进行处理。然而，简单的基础设施即服务（IaaS）资源配置难以应对高度波动的工作负载，导致服务等级目标（SLO）违反及资源利用效率低下。此外，这些方法忽略了通过RoI批处理以利用并行处理的潜在优势。本文提出Tangram，一个针对通信与计算两方面均进行充分优化的高效无服务器云边视频分析系统。Tangram可将RoI自适应地对齐为图像块（patches）并传输至云端的调度器。该系统采用一种独特的“拼接”方法，用于将来自边缘摄像头的不同尺寸图像块进行批处理。同时，我们开发了一种在线SLO感知批处理算法，能够明智地确定无服务器函数的最优调用时机。原型系统实验表明，与现有方法相比，Tangram可分别降低高达74.30%的带宽消耗和66.35%的计算成本，同时将SLO违反率控制在5%以内，且精度损失可忽略不计。