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    Joint Offloading and Resource Allocation for Scalable Vehicular Edge Computing

    Joint Offloading and Resource Allocation for Scalable Vehicular Edge Computing


    Edge computing assisted autonomous driving technology has become a promising method to satisfy exacting computation requirements of achieving high or even full automation. However, the computation and spectrum resources of multi-access vehicular edge computing (VEC) system are also limited, which may not guarantee the best experience for all users, so we make a tradeoff between resource consumption and user experience. First, according to the characteristics of task scalability in driving assistance applications, we model the problem as maximizing system utility under the deadline constraint and the total resource constraint. Then, we formulate a collaborative computation offloading and resource allocation optimization scheme (JORA). Since the problem is NP-Hard, the JORA scheme eventually solves the problem by the mutual iteration of the two sub-algorithms, which includes offloading strategy and resource allocation. Simulation results prove that the proposed algorithm can effectively improve the system utility.

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    Titel :

    Joint Offloading and Resource Allocation for Scalable Vehicular Edge Computing

    Beteiligte:
    Wu, Wei (Autor:in) / Wang, Qie (Autor:in) / Wu, Xuanli (Autor:in) / Zhang, Ning (Autor:in)
    Erschienen in:

    2020 IEEE 92nd Vehicular Technology Conference (VTC2020-Fall) ; 1-5

    Erscheinungsdatum :

    2020-11-01

    Format / Umfang :

    994731 byte

    ISBN :

    978-1-7281-9484-4

    ISSN :

    2577-2465

    DOI :

    https://doi.org/10.1109/VTC2020-Fall49728.2020.9348695

    Medientyp :

    Aufsatz (Konferenz)

    Format :

    Elektronische Ressource

    Sprache :

    Englisch

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