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A large-scale holistic measurement of crowdsourced edge cloud platform

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Abstract

Edge clouds have become a de-facto paradigm to deliver low and stable networks to delay-critical applications such as Web services and AR/VR. A unique form of edge clouds is those crowdsourced from third parties, e.g., idle PCs or workstations. Such crowdsourced edge platforms can better sink computations closer to users, reduce the purchase cost, and eliminates the carbon generated during manufacturing. Yet, they also face the challenge of out-of-control hardware, e.g., a server dropping in/out anytime. In this paper, we perform the first-of-its-kind measurement of Quality of Service (QoS) for a large-scale crowdsourced edge platform, which covers over 10,000 edge servers, 100,000 users and 10,000,000 user requests. The measurement takes a holistic QoS view: First, we look at how much hardware resources are provided by edge servers, how much time they are available for service deployment, how geographic distance affects network performance, and what are the major abnormal behaviors. Second, we analyze the factors affecting service stability and quantify the resource utilization pattern of containerized services hosted on those edge servers. Third, we investigate the spatial and temporal features of user requests handled by the platform. Many useful and somehow surprising findings are obtained through the above measurements. We also derive insightful implications that could help edge platforms and edge applications to better deliver their services to users.

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Data Availibility

Not applicable.

Code Availability

Codes related to the model generator in this research are available.

Notes

  1. Abbreviated as edge platform in the following.

  2. PPIO Edge Cloud, Paiou Cloud Computing (Shanghai) Co., Ltd., https://www.ppio.cn.

  3. ESMG focuses on providing a modelling approach that approximates real business application scenarios rather than an optimal deployment solution for edge servers.

  4. Due to the limited amount of data for ISP-D, the credibility of its performance analysis is low.

  5. Since our dataset does not contain the actual routing data of the transmission, we calculate the straight-line distance by computing the geographic coordinates as an approximate substitute.

  6. The numbers here and below correspond to the indexes in Figure 12.

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Funding

This research was supported by the National Key R &D Program of China (Grant 2021ZD0113001), the Guangdong Pearl River Talent Recruitment Program (Grant 2019ZT08X603), the Guangdong Pearl River Talent Plan (Grant 2019JC01X235), Shenzhen Science and Technology Innovation Commission (Grant R2020A045), the Canadian Natural Sciences and Engineering Research Council (Grant RGPIN-2019-06348), the National Science Foundation of China (Grant 62072332), the China NSFC (Youth) (Grant 62002260), the China Postdoctoral Science Foundation (Grant 2020M670654), and the Tianjin Xinchuang Haihe Lab (Grant 22HHXCJC00002).

Author information

Authors and Affiliations

  1. College of Intelligence and Computing, Tianjin University, Tianjin, China

    Yicheng Feng, Shihao Shen, Xin Wang & Xiaofei Wang

  2. Beijing University of Posts and Telecommunications, Beijing, China

    Mengwei Xu

  3. Institute of Technology, Tianjin University of Finance and Economics, Tianjin, China

    Cheng Zhang

  4. Paiou Cloud Computing (Shanghai) Co., Ltd., Shanghai, China

    Wenyu Wang

  5. College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China

    Victor C. M. Leung

  6. Department of Electrical and Computer Engineering, The University of British Columbia, Vancouver, BC, Canada

    Victor C. M. Leung

Authors
  1. Yicheng Feng
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  2. Shihao Shen
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  3. Mengwei Xu
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  4. Cheng Zhang
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  5. Xin Wang
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  6. Xiaofei Wang
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  7. Wenyu Wang
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  8. Victor C. M. Leung
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Contributions

Yicheng Feng and Shihao Shen were primarily responsible for the writing of the paper, conducting experimental tests, and plotting experimental graphs. Mengwei Xu, Cheng Zhang, Xin Wang, Xiaofei Wang, Wenyu Wang and Victor C.M. Leung provided guidance on the design and writing of the paper. All authors reviewed the manuscript.

Corresponding author

Correspondence to Xiaofei Wang.

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Feng, Y., Shen, S., Xu, M. et al. A large-scale holistic measurement of crowdsourced edge cloud platform. World Wide Web 26, 3561–3584 (2023). https://doi.org/10.1007/s11280-023-01201-y

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  • DOI: https://doi.org/10.1007/s11280-023-01201-y

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