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Collaborative Mobile Edge Computing Through UPF Selection

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Collaborative Computing: Networking, Applications and Worksharing (CollaborateCom 2022)

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Abstract

The distributed deployment and the relatively limited resource of one edge node make it quite challenging to effectively manage resources at the edge. Inappropriate scheduling may result in a quality of service deterioration and brings significant cost. In this paper, we propose a per-user level management mechanism for joint scheduling of user requests and container resources at the edge and study how to minimize average cost as well as satisfy delay constraints. The cost model of the system consists of operating cost, switching cost and delay violation cost. The key idea is to deploy a deep reinforcement learning-based scheduler in the core network to conduct joint network and computation management. To evaluate the performance, we build a test bed namely MiniEdgeCore that contains a full user plane protocol stack and deploy a real-time video inference application on it. A real-world dataset is used as the workload sequence to conduct experiments. The results show that the proposed method can reduce average costs effectively.

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Notes

  1. 1.

    https://docs.docker.com/engine/api/.

  2. 2.

    https://github.com/BuptMecMigration/Edge-Computing-Dataset.

  3. 3.

    https://www.openvswitch.org/.

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Acknowledgments

This work was supported in part by the National Key R &D Program of China (No. 2020YFB1805502) and NSFC (U21B2016, 62032003 and 61922017).

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Authors and Affiliations

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

    Yuanzhe Li, Ao Zhou, Xiao Ma & Shangguang Wang

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  1. Yuanzhe Li
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  2. Ao Zhou
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  3. Xiao Ma
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  4. Shangguang Wang
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Corresponding author

Correspondence to Yuanzhe Li .

Editor information

Editors and Affiliations

  1. Shanghai University, Shanghai, China

    Honghao Gao

  2. Xi’an Jiaotong-Liverpool University, Suzhou, China

    Xinheng Wang

  3. Zhejiang University City College, Hangzhou, China

    Wei Wei

  4. London South Bank University, London, UK

    Tasos Dagiuklas

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Li, Y., Zhou, A., Ma, X., Wang, S. (2022). Collaborative Mobile Edge Computing Through UPF Selection. In: Gao, H., Wang, X., Wei, W., Dagiuklas, T. (eds) Collaborative Computing: Networking, Applications and Worksharing. CollaborateCom 2022. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 461. Springer, Cham. https://doi.org/10.1007/978-3-031-24386-8_19

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  • DOI: https://doi.org/10.1007/978-3-031-24386-8_19

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-24385-1

  • Online ISBN: 978-3-031-24386-8

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