From cloud to edge: A first look at public edge platforms

Mengwei Xu, Zhe Fu, Xiao Ma, Li Zhang, Yanan Li, Feng Qian, Shangguang Wang, Ke Li, Jingyu Yang, Xuanzhe Liu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

31 Scopus citations


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.

Original languageEnglish (US)
Title of host publicationIMC 2021 - Proceedings of the 2021 ACM Internet Measurement Conference
PublisherAssociation for Computing Machinery
Number of pages17
ISBN (Electronic)9781450391290
StatePublished - Nov 2 2021
Event21st ACM Internet Measurement Conference, IMC 2021 - Virtual, Online, United States
Duration: Nov 2 2021Nov 4 2021

Publication series

NameProceedings of the ACM SIGCOMM Internet Measurement Conference, IMC


Conference21st ACM Internet Measurement Conference, IMC 2021
Country/TerritoryUnited States
CityVirtual, Online

Bibliographical note

Funding Information:
Mengwei Xu was supported by National Key R&D Program of China under grant number 2020YFB1805500, the Fundamental Research Funds for the Central Universities, and National Natural Science Foundation of China under grant number 61922017. Xuanzhe Liu was supported in part by Alibaba University Joint Research Program. We hereby give special thanks to Alibaba Group for their contribution to this paper. We also thank our shepherd, Aaron Schulman, and the anonymous IMC reviewers for their useful suggestions. Shangguang Wang is the corresponding author of this work.

Publisher Copyright:
© 2021 ACM.


  • edge computing
  • measurement study
  • workloads analysis


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