With the highly increasing demands of vehicular applications, the cloud intelligence is pushed towards the edge by placing services next to vehicle users. Due to the limited resources of edge nodes and the high mobility of vehicle users, it is challenging to place vehicular services effectively at the edge to serve vehicle users with high quality of experience (QoE). In this paper, we investigate a vehicular service placement problem with unknown demands. Different from previous works, we consider that vehicle users have various service demands, which are related to their contexts. To enable on-demand service placement, we propose a context-aware vehicular service placement algorithm based on the estimated service demands. For better demand estimation, a fine-grained partition method is developed to divide the context space. The simulation results show that the proposed algorithm has superior performance in terms of cumulative system rental utility.


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    Titel :

    Context-Aware Service Placement at the Edge in Vehicular Networks


    Beteiligte:
    Zhang, Wanlu (Autor:in) / Tao, Chenhui (Autor:in) / Luo, Jingjing (Autor:in) / Zheng, Fu-Chun (Autor:in) / Gao, Lin (Autor:in)


    Erscheinungsdatum :

    2023-06-01


    Format / Umfang :

    2579929 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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