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A utility-based resource allocation scheme in cloud-assisted vehicular network architecture

机译:云辅助车载网络架构中基于实用程序的资源分配方案

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In the era of the Internet-of-Vehicles (IoV), all components in an Intelligent Transportation System (ITS) can be connected to improve the traffic safety and transportation efficiency. In order to maximize the utilization of the resources, e.g., computation, communication and storage resources, the cloud computing technique could be integrated into vehicular networks. Meanwhile, a cloud-assisted vehicular network could be proposed for effective resource management. In this paper, the resource allocation problem in the cloud-assisted vehicular network architecture is investigated. Each cloud in the architecture has its own specific features, e.g., the remote cloud has sufficient resources but experience high end-to-end delay while the local cloud and vehicular cloud have limited resources but a lower transmission delay is attained. The optimal problem to maximize the system expected average reward is formulated as a Semi-Markov Decision Process (SMDP). Consquently, the corresponding optimal scheme is obtained by solving the SMDP problem via an iteration algorithm. The proposed scheme can provide guidelines that will be helpful to decide to which cloud a request should be admitted and how many resources are needed to be allocated. Numerical results indicate that the proposed scheme outperforms other resource allocation schemes and improves system rewards as well as the obtained experience by the vehicular users.
机译:在车载互联网(IoV)时代,可以连接智能运输系统(ITS)中的所有组件,以提高交通安全性和运输效率。为了最大程度地利用资源,例如计算,通信和存储资源,可以将云计算技术集成到车辆网络中。同时,可以提出云辅助的车辆网络以进行有效的资源管理。本文研究了云辅助车载网络体系结构中的资源分配问题。架构中的每个云具有其自身的特定特征,例如,远程云具有足够的资源,但是经历了高的端到端延迟,而本地云和车辆云具有有限的资源,但是获得了较低的传输延迟。最大化系统预期平均回报的最佳问题被表述为半马尔可夫决策过程(SMDP)。因此,通过迭代算法求解SMDP问题可获得相应的最优方案。所提出的方案可以提供指导,这将有助于确定应将请求接受到哪个云以及需要分配多少资源。数值结果表明,所提出的方案优于其他资源分配方案,并提高了系统奖励以及车辆用户获得的经验。

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