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Title	Allocating Edge Service Resources To The Up-Offloaded Vehicle Tasks In Icv Environment
ID_Doc	7252
Authors	Guo H.; Shi R.-C.; Gu P.-L.; Li J.-L.; Wang S.-L.
Year	2023
Published	Computer Networks, 227
DOI	http://dx.doi.org/10.1016/j.comnet.2023.109715
Abstract	Inspired by the rapid proliferation of intelligent transportation and smart city, ICV (Intelligent and Connected Vehicle) has drawn much attention, which not only brings great opportunities but also poses new challenges to the dynamic vehicle task serving mode. In this paper, we focus on the edge resource allocation process of the overloaded vehicle tasks that offloaded to edge servers in ICV environment. First of all, we introduce MEC and SDN technology into the traditional IoV (Internet of Vehicle) architecture to construct an SDN-assisted ICV network model. Next, a BiGRU+Attention edge service resource demands predicting model is designed, and based on the above model, we are able to further assess the future edge service resources availability and perceive the future vehicle mobility. Then, we build a task serving delay minimization problem for the edge service resource allocation process, where the impact of resource allocation decision-makings on the global network performance is also considered in the form of soft constraints. Furthermore, we put forward a timeslot-based edge service resource allocation algorithm with three phases. Finally, we simulate our scheme and another three schemes on NS3 platform, the results show that our algorithm outperforms in terms of average task serving delay, success ratio and load distribution. © 2023 Elsevier B.V.
Author Keywords	Availability assessment; Edge service resource allocation; Intelligent and Connected Vehicle

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