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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 Cloud-Edge Computing with Mixed Wireless and Wired Backhaul Links: Joint Task Offloading and Resource Allocation

Cite
BibTeX Plain Text
  • @INPROCEEDINGS{10.1007/978-3-031-54521-4_22,
        author={Daqing Zhang and Haifeng Sun},
        title={Collaborative Cloud-Edge Computing with Mixed Wireless and Wired Backhaul Links: Joint Task Offloading and Resource Allocation},
        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 Ultra-Dense Network task offloading resource allocation wireless backhaul},
        doi={10.1007/978-3-031-54521-4_22}
    }
    
  • Daqing Zhang
    Haifeng Sun
    Year: 2024
    Collaborative Cloud-Edge Computing with Mixed Wireless and Wired Backhaul Links: Joint Task Offloading and Resource Allocation
    COLLABORATECOM
    Springer
    DOI: 10.1007/978-3-031-54521-4_22
Daqing Zhang1, Haifeng Sun1,*
  • 1: School of Computer Science and Technology, Southwest University of Science and Technology
*Contact email: dr_hfsun@163.com

Abstract

Mobile Edge Computing (MEC) is a promising technology that provides computing services at the edge of wireless networks to reduce the latency and the energy consumption for Smart Mobile Devices (SMDs). Additionally, the Ultra-Dense Network (UDN) will play a key role in providing high transmission capacity for SMDs in 5G networks. In order to improve the edge cloud efficiency within limited communication and computing resources, this paper proposes a joint task offloading and resource allocation scheme collaborated between cloud computing and edge computing in the UDN. Since wireless backhaul is more economical than expensive wired backhaul deployments, we consider the mixed deployment of either wired or wireless backhaul between each Small Base Station (SBS) and the Macro Base Station (MBS) in UDN scenarios, then formulate an optimization problem to minimize the system-wide computation overhead, and apply the Linear Decreasing Weight Particle Swarm Optimization (LDWPSO) algorithm to solve the problem. Numerical experiments validate the effectiveness of our proposed scheme compared to other baseline schemes.

Keywords
Mobile Edge Computing Ultra-Dense Network task offloading resource allocation wireless backhaul
Published
2024-02-23
Appears in
SpringerLink
http://dx.doi.org/10.1007/978-3-031-54521-4_22
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