Abstract
A vehicular cloud (VC) can reduce latency and improve resource utilization of the Internet of vehicles by effectively using the underutilized computing resources of nearby vehicles. Although the task offloading of the VC enhances road safety and traffic management on the Internet of vehicles and meets the low-latency requirements for driving safety services on the Internet of vehicles business, there are still some key challenges such as the resource allocation mechanism of differentiated services (DiffServ) and task offloading mechanism of improving user experience. To address these issues, we study the task offloading and resource allocation strategy of the VC system where tasks generated by vehicles can be offloaded and executed cooperatively by vehicles in VC. Specifically, the computing task is further divided into independent subtasks and executed in different vehicles in VC to maximize the offloading utility. Considering the mobility of vehicles, the deadline of tasks, and the limited computing resources, we propose the optimization problem of task offloading in the VC system in the cause of improved user experience. To characterize the difference in service requirements resulting from the diversity of tasks, a DiffServ model focusing on the pricing of a task is utilized. The initial pricing of a task is tailored by the characteristics of the task and the uniqueness of the network status. In this model, tasks are sorted and processed in order according to task pricing, so as to optimize resource allocation. Numerical results show that the proposed scheme can effectively increase the resource utilization and task completion ratio.
Highlights
In a vehicle network [1], the cloud composed of vehicles with strong computing power is called vehicular cloud (VC) [2]
Unlike the resource management [17] scheme that considers the priority of a task in the general VC computing scenario, we propose a game theory-based DiffServ resource allocation mechanism that DiffServ through a task’s differentiated pricing
(i) To meet the requirements of the diversity of tasks while effectively using computing resources, we propose a resource allocation mechanism for differentiated services and offload each task according to the pricing order (ii) To improve the completion ratio of whole tasks in the vehicular cloud system, a task offloading mechanism that maximizes the offloading utility is proposed and considers the mobility of vehicles and the deadline of tasks (iii) To address the problem of task offloading and resource allocation, a distributed resource allocation algorithm and Lagrange dual method are used, respectively
Summary
In a vehicle network [1], the cloud composed of vehicles with strong computing power is called VC [2]. In order to solve the users’ selfishness, Liu et al have established a kind of new type of computing sharing market in the vehicle network [12] Several other works, such as [13, 14], use the method of task replication to allow a task to be executed by multiple vehicles so as to maximize the possibility of completing the task before a given deadline and improve the reliability. (i) To meet the requirements of the diversity of tasks while effectively using computing resources, we propose a resource allocation mechanism for differentiated services and offload each task according to the pricing order (ii) To improve the completion ratio of whole tasks in the vehicular cloud system, a task offloading mechanism that maximizes the offloading utility is proposed and considers the mobility of vehicles and the deadline of tasks (iii) To address the problem of task offloading and resource allocation, a distributed resource allocation algorithm and Lagrange dual method are used, respectively
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