Joint Task Offloading and Resource Allocation Strategy for DiffServ in Vehicular Cloud System

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.

Computing Resource Allocation Strategy Based on Mobile Edge Computing in Internet of Vehicles Environment

“…Ref. [19] Ref. [15] Figure 8: e relationship between the total number of tasks and the total cost of tasks for different methods.…”

“…e convergence speed of the algorithm, the relationship between the average cost and amount of data, the relationship between the average delay and number of tasks, and the relationship between the total task cost and the total number of tasks are important evaluation indicators to measure the performance of the strategy. In the following, for the above-given four evaluation indicators, the resource allocation strategy based on mobile edge computing in the IoV environment proposed in this paper and the methods in reference [15], reference [16], and reference [19] are compared under the same conditions.…”

“…However, the convergence rate of this method was slow. Reference [19] divided the tasks generated by vehicles in view of problems such as the differentiated resource allocation mechanism in the in-vehicle cloud system. e divided subtasks were downloaded to an on-board cloud system and executed collaboratively by different vehicles, thereby improving the offloading efficiency of tasks.…”

An Improved ARO Model for Task Offloading in Vehicular Cloud Computing in VANET

“…{𝐹𝑇 𝑖 } ≤ 𝑇 𝑖∈{1,2,..,𝑛 𝑡 } max (11) The energy 𝐸 total spent by 𝑣 j for 𝑡 i is the quantity of the energy 𝐸 exe to achieve the task 𝑡 i and the energy 𝐸 tr to convey with 𝑡 i ' i-successors.…”

“…ITS provides a wide variety of cutting-edge services, including driverless cars, video games, and augmented and virtual realities. The majority of these services need advanced computing capacity without any tolerance for delay [11]. In addition, it is anticipated that automobiles will increasingly support both high-traffic applications and simultaneous multitasking in the not-too-distant future.…”