# Real-Time Edge Classification: Optimal Offloading under Token Bucket Constraints

arXiv: https://arxiv.org/abs/2010.13737 · HTML: https://mlsanity.com/paper/2010.13737/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Oct 2020.

## Built on by (most cited first, 4 of 4)

- [Edge Video Analytics: A Survey on Applications, Systems and Enabling Techniques](https://mlsanity.com/paper/2211.15751/) (2022)
- [Progressive Neural Compression for Adaptive Image Offloading under Timing Constraints](https://mlsanity.com/paper/2310.05306/) (2023)
- [Adaptive Edge Offloading for Image Classification Under Rate Limit](https://mlsanity.com/paper/2208.00485/) (2022)
- [Optimizing Edge Offloading Decisions for Object Detection](https://mlsanity.com/paper/2410.18919/) (2024)

## Abstract (arXiv)

To deploy machine learning-based algorithms for real-time applications with strict latency constraints, we consider an edge-computing setting where a subset of inputs are offloaded to the edge for processing by an accurate but resource-intensive model, and the rest are processed only by a less-accurate model on the device itself. Both models have computational costs that match available compute resources, and process inputs with low-latency. But offloading incurs network delays, and to manage these delays to meet application deadlines, we use a token bucket to constrain the average rate and burst length of transmissions from the device. We introduce a Markov Decision Process-based framework to make offload decisions under these constraints, based on the local model's confidence and the token bucket state, with the goal of minimizing a specified error measure for the application. Beyond isolated decisions for individual devices, we also propose approaches to allow multiple devices connected to the same access switch to share their bursting allocation. We evaluate and analyze the policies derived using our framework on the standard ImageNet image classification benchmark.