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Collaborative Computing: Networking, Applications and Worksharing. 19th EAI International Conference, CollaborateCom 2023, Corfu Island, Greece, October 4-6, 2023, Proceedings, Part I

Research Article

Collaborative Task Processing and Resource Allocation Based on Multiple MEC Servers

Cite
BibTeX Plain Text
  • @INPROCEEDINGS{10.1007/978-3-031-54521-4_21,
        author={Lei Shi and Shilong Feng and Rui Ji and Juan Xu and Xu Ding and Baotong Zhan},
        title={Collaborative Task Processing and Resource Allocation Based on Multiple MEC Servers},
        proceedings={Collaborative Computing: Networking, Applications and Worksharing. 19th EAI International Conference, CollaborateCom 2023, Corfu Island, Greece, October 4-6, 2023, Proceedings, Part I},
        proceedings_a={COLLABORATECOM},
        year={2024},
        month={2},
        keywords={Mobile Edge Computing Lyapunov Optimization Collaborative Task Processing Resource Allocation},
        doi={10.1007/978-3-031-54521-4_21}
    }
    
  • Lei Shi
    Shilong Feng
    Rui Ji
    Juan Xu
    Xu Ding
    Baotong Zhan
    Year: 2024
    Collaborative Task Processing and Resource Allocation Based on Multiple MEC Servers
    COLLABORATECOM
    Springer
    DOI: 10.1007/978-3-031-54521-4_21
Lei Shi1, Shilong Feng1,*, Rui Ji1, Juan Xu1, Xu Ding1, Baotong Zhan2
  • 1: School of Computer Science and Information Engineering, Hefei University of Technology
  • 2: Taian Hualu Metalforming Machine Tool Co., Ltd., Taian
*Contact email: fsl@mail.hfut.edu.cn

Abstract

Mobile Edge Computing (MEC), an emerging computing paradigm, shifts computing and storage capabilities from the cloud to the network edge, aiming to meet the delay requirements of emerging applications and save backhaul network bandwidth. However, compared to cloud servers, MEC servers have limited computing and storage capabilities, which cannot meet the massive offloading demands of users during high-load periods. In this context, this paper proposes a multi-ENs collaborative task processing model. The model aims to formulate optimal offloading decisions and allocate computing resources for tasks to minimize system delay and cost. To solve this problem, we propose an online algorithm based on Lyapunov optimization called OKMTA, which can work online without the need for predicting future information. Specifically, the problem is formulated as a mixed-integer nonlinear programming (MINLP) problem and decomposed into two subproblems for solution. By using the Lagrange multiplier method to solve the computing resource allocation problem of tasks, and by using matching theory to solve the offloading decision problem of tasks. The simulation results show that our algorithm can achieve near-optimal delay performance while satisfying the long-term system average cost constraint.

Keywords
Mobile Edge Computing Lyapunov Optimization Collaborative Task Processing Resource Allocation
Published
2024-02-23
Appears in
SpringerLink
http://dx.doi.org/10.1007/978-3-031-54521-4_21
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