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

TL;DR

Croesus introduces a multi-stage edge-cloud processing framework for video analytics that balances accuracy and performance by combining real-time approximate edge computation with cloud-based correction.

Contribution

The paper presents Croesus, a novel multi-stage approach enabling efficient, accurate video analytics in edge-cloud systems through staged processing and transaction safety mechanisms.

Findings

Multi-stage processing improves accuracy-performance trade-off.

Croesus effectively balances real-time edge computation with cloud correction.

Proposed safety protocols ensure transaction consistency in multi-stage systems.

Abstract

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).