# From Cloud to Edge: A First Look at Public Edge Platforms

arXiv: https://arxiv.org/abs/2109.03395 · HTML: https://mlsanity.com/paper/2109.03395/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Sep 2021.

## Built on by (most cited first, 9 of 9)

- [FedAdapter: Efficient Federated Learning for Modern NLP](https://mlsanity.com/paper/2205.10162/) (2022)
- [HiFlash: Communication-Efficient Hierarchical Federated Learning with Adaptive Staleness Control and Heterogeneity-aware Client-Edge Association](https://mlsanity.com/paper/2301.06447/) (2023)
- [Federated Few-Shot Learning for Mobile NLP](https://mlsanity.com/paper/2212.05974/) (2022)
- [Benchmarking of DL Libraries and Models on Mobile Devices](https://mlsanity.com/paper/2202.06512/) (2022)
- [Demystifying Swarm Learning: A New Paradigm of Blockchain-based Decentralized Federated Learning](https://mlsanity.com/paper/2201.05286/) (2022)
- [Boosting DNN Cold Inference on Edge Devices](https://mlsanity.com/paper/2206.07446/) (2022)
- [TS-EoH: An Edge Server Task Scheduling Algorithm Based on Evolution of Heuristic](https://mlsanity.com/paper/2409.09063/) (2024)
- [Serving Graph Neural Networks With Distributed Fog Servers For Smart IoT Services](https://mlsanity.com/paper/2307.01684/) (2023)
- [ConsRoute:Consistency-Aware Adaptive Query Routing for Cloud-Edge-Device Large Language Models](https://mlsanity.com/paper/2603.21237/) (2026)

## Abstract (arXiv)

Public edge platforms have drawn increasing attention from both academia and industry. In this study, we perform a first-of-its-kind measurement study on a leading public edge platform that has been densely deployed in China. Based on this measurement, we quantitatively answer two critical yet unexplored questions. First, from end users' perspective, what is the performance of commodity edge platforms compared to cloud, in terms of the end-to-end network delay, throughput, and the application QoE. Second, from the edge service provider's perspective, how are the edge workloads different from cloud, in terms of their VM subscription, monetary cost, and resource usage. Our study quantitatively reveals the status quo of today's public edge platforms, and provides crucial insights towards developing and operating future edge services.