research-article

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

Authors:

Shangguang Wang,

Xuanzhe LiuAuthors Info & Claims

IMC '21: Proceedings of the 21st ACM Internet Measurement Conference

Pages 37 - 53

https://doi.org/10.1145/3487552.3487815

Published: 02 November 2021 Publication History

Abstract

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.

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Index Terms

From cloud to edge: a first look at public edge platforms
1. Computer systems organization
  1. Architectures
    1. Distributed architectures
      1. Grid computing
2. Networks
  1. Network performance evaluation
    1. Network measurement

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cover image ACM Conferences

IMC '21: Proceedings of the 21st ACM Internet Measurement Conference

November 2021

768 pages

ISBN:9781450391290

DOI:10.1145/3487552

General Chairs:
Dave Levin
University of Maryland
,
Alan Mislove
Northeastern University
,
Program Chairs:
Johanna Amann
International Computer Science Institute
,
Matthew Luckie
University of Waikato

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National Key R&D Program of China
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Li YZhao PMa XYuan HFu ZXu MWang S(2025)A Collaborative Cloud-Edge Approach for Robust Edge Workload ForecastingIEEE Transactions on Mobile Computing10.1109/TMC.2024.350268324:4(2861-2875)Online publication date: Apr-2025
https://doi.org/10.1109/TMC.2024.3502683
Li YMa XFu ZZhou AXu MWang S(2025)Rethinking Cost-Efficient VM Scheduling on Public Edge Platforms: A Service Provider’s PerspectiveIEEE Transactions on Mobile Computing10.1109/TMC.2024.348808224:3(1846-1858)Online publication date: Mar-2025
https://doi.org/10.1109/TMC.2024.3488082
Luo KZhao KOuyang TZhang XZhou ZWang HChen X(2025)Efficient Coordination of Federated Learning and Inference Offloading at the Edge: A Proactive Optimization ParadigmIEEE Transactions on Mobile Computing10.1109/TMC.2024.346684424:1(407-421)Online publication date: Jan-2025
https://doi.org/10.1109/TMC.2024.3466844
Wei ZWang ZLi QYang YLuo CWang FJiang YYang SYuan ZBagchi SZhang Y(2024)QDSRProceedings of the 2024 USENIX Conference on Usenix Annual Technical Conference10.5555/3691992.3692036(715-730)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.5555/3691992.3692036
Zhang LWang SXu MBagchi SZhang Y(2024)High-density mobile cloud gaming on edge SoC clustersProceedings of the 2024 USENIX Conference on Usenix Annual Technical Conference10.5555/3691992.3692012(339-347)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.5555/3691992.3692012
Zhang LFu ZShi BLi XLai RYang CZhou AMa XWang SXu MBagchi SZhang Y(2024)More is differentProceedings of the 2024 USENIX Conference on Usenix Annual Technical Conference10.5555/3691992.3692009(285-302)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.5555/3691992.3692009
Sha XSun WLiu XLuo YLuo C(2024)Enhancing Edge-Assisted Federated Learning with Asynchronous Aggregation and Cluster PairingElectronics10.3390/electronics1311213513:11(2135)Online publication date: 30-May-2024
https://doi.org/10.3390/electronics13112135
Tsujimoto YSato YYasukata KIshiguro KKono KSchiavoni VEdinger JCao JJin Z(2024)PvCC: A vCPU Scheduling Policy for DPDK-applied Systems at Multi-Tenant Edge Data CentersProceedings of the 25th International Middleware Conference10.1145/3652892.3700779(379-391)Online publication date: 2-Dec-2024
https://dl.acm.org/doi/10.1145/3652892.3700779
Sarma AYoo JSojan JRamachandran ULee M(2024)FEO: Efficient Resource Allocation for FaaS at the EdgeProceedings of the 18th ACM International Conference on Distributed and Event-based Systems10.1145/3629104.3666033(78-89)Online publication date: 24-Jun-2024
https://dl.acm.org/doi/10.1145/3629104.3666033
Shen SGu CLi YQiu CWang XTan RZhang CWang W(2024)Task Allocation with Geography-Context-Capacity Awareness in Distributed Burstable Billing Edge-Cloud SystemsIEEE Transactions on Services Computing10.1109/TSC.2024.3506475(1-12)Online publication date: 2024
https://doi.org/10.1109/TSC.2024.3506475
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