From Cloud to Edge: A First Look at Public Edge Platforms
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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.
Key facts. Cited by 9 papers in the map (data through 14 Oct 2026). First posted on arXiv 8 Sep 2021.
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Citing papers per year: 2022: 5; 2023: 2; 2024: 1; 2025: 0; 2026: 1.
9 papers.
- 2022FedAdapter: Efficient Federated Learning for Modern NLPcited by 25
- 2023HiFlash: Communication-Efficient Hierarchical Federated Learning with Adaptive Staleness Control and Heterogeneity-aware Client-Edge Associationcited by 17
- 2022Federated Few-Shot Learning for Mobile NLPcited by 15
- 2022Benchmarking of DL Libraries and Models on Mobile Devicescited by 12
- 2022Demystifying Swarm Learning: A New Paradigm of Blockchain-based Decentralized Federated Learningcited by 7
- 2022Boosting DNN Cold Inference on Edge Devicescited by 6
- 2024TS-EoH: An Edge Server Task Scheduling Algorithm Based on Evolution of Heuristiccited by 4
- 2023Serving Graph Neural Networks With Distributed Fog Servers For Smart IoT Servicescited by 2
- 2026ConsRoute:Consistency-Aware Adaptive Query Routing for Cloud-Edge-Device Large Language Modelscited by 1
- 2026ConsRoute:Consistency-Aware Adaptive Query Routing for Cloud-Edge-Device Large Language Models22 Mar 2026
- 2024TS-EoH: An Edge Server Task Scheduling Algorithm Based on Evolution of Heuristic4 Sep 2024
- 2023Serving Graph Neural Networks With Distributed Fog Servers For Smart IoT Services4 Jul 2023
- 2023HiFlash: Communication-Efficient Hierarchical Federated Learning with Adaptive Staleness Control and Heterogeneity-aware Client-Edge Association16 Jan 2023
- 2022Federated Few-Shot Learning for Mobile NLP12 Dec 2022
- 2022Boosting DNN Cold Inference on Edge Devices15 Jun 2022
- 2022FedAdapter: Efficient Federated Learning for Modern NLP20 May 2022
- 2022Benchmarking of DL Libraries and Models on Mobile Devices14 Feb 2022
- 2022Demystifying Swarm Learning: A New Paradigm of Blockchain-based Decentralized Federated Learning14 Jan 2022