论文标题
Croesus：边缘云系统中视频分析的多阶段处理和交易
Croesus: Multi-Stage Processing and Transactions for Video-Analytics in Edge-Cloud Systems
论文作者
论文摘要
新兴的边缘应用程序需要快速响应延迟和复杂的处理。这是不可行的，没有昂贵的硬件可以在短时间内处理复杂操作（例如对象检测）。许多人通过解决模型的复杂性（通过模型压缩，修剪和量化）来解决此问题，或压缩输入。在本文中，我们在解决绩效挑战时提出了不同的观点。 Croesus是一种用于边缘云系统的多阶段方法，它提供了在准确性和性能之间找到平衡的能力。 Croesus由两个阶段组成（可以推广到多个阶段）：初始阶段和最后一个阶段。初始阶段使用边缘的近似/最佳富度计算实时执行计算。最后阶段在云上执行完整的计算，并使用结果来纠正初始阶段的任何错误。在本文中，我们演示了这种方法对视频分析用例的含义，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，我们通过两个建议研究了多阶段交易的安全性：多阶段的序列化（MS-SR）和多阶段不变性汇合处（MS-IA）。
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).
