标题： 克洛伊索斯：边缘云系统中视频分析的多阶段处理与事务 显示英文标题

标题： Croesus: Multi-Stage Processing and Transactions for Video-Analytics in Edge-Cloud Systems

摘要： 新兴的边缘应用需要快速响应延迟和复杂的处理能力。 没有能够处理复杂操作（如物体检测）的昂贵硬件，在短时间内完成任务是不可行的。 许多人通过解决模型的复杂性——通过模型压缩、剪枝和量化——或压缩输入来解决这个问题。 在本文中，我们提出了一个不同的视角来应对性能挑战。 Croesus 是一种多阶段的边缘云系统方法，提供在准确性和性能之间找到平衡的能力。 Croesus 包含两个阶段（可以推广到多个阶段）：初始阶段和最终阶段。 初始阶段使用边缘设备上的近似/尽力而为的实时计算执行计算。 最终阶段在云端执行完整的计算，并利用结果纠正初始阶段可能产生的错误。 在本文中，我们展示了这种方法在视频分析用例中的影响，并展示了多阶段处理如何在准确性和性能之间取得更好的平衡。 此外，我们通过两种提议研究了多阶段事务的安全性：多阶段可序列化（MS-SR）和带歉意的多阶段不变量一致性（MS-IA）。 显示英文摘要

摘要： Emerging edge applications require both a fast response latency and complex processing. This is infeasible without expensive hardware that can process complex operations -- such as object detection -- within a short time. Many approach this problem by addressing the complexity of the models -- via model compression, pruning and quantization -- or compressing the input. In this paper, we propose a different perspective when addressing the performance challenges. Croesus is a multi-stage approach to edge-cloud systems that provides the ability to find the balance between accuracy and performance. Croesus consists of two stages (that can be generalized to multiple stages): an initial and a final stage. The initial stage performs the computation in real-time using approximate/best-effort computation at the edge. The final stage performs the full computation at the cloud, and uses the results to correct any errors made at the initial stage. In this paper, we demonstrate the implications of such an approach on a video analytics use-case and show how multi-stage processing yields a better balance between accuracy and performance. Moreover, we study the safety of multi-stage transactions via two proposals: multi-stage serializability (MS-SR) and multi-stage invariant confluence with Apologies (MS-IA).