Cost-Effective Task Scheduling Strategy in Vehicular Cloud Computing
Jie Wang, Qiang Zhang · 2022
With the development of vehicle cloud computing technology, vehicle cloud has more extensive application scenarios. It can provide users with computing resources to process requests of mobile devices, so as to improve the defect of weak computing and storage capacity of mobile devices. Due to the distributed resources of vehicle cloud, it is a challenge to schedule workflow tasks with deadline constraint and high cost efficiency. In this paper, we propose a cost-effective and deadline-constrained strategy (CEDCS) to implement task scheduling in vehicular cloud computing. Our strategy firstly proposes an initial task allocation strategy to create a schedule to meet deadline constraint. Then, a task rescheduling algorithm is proposed to achieve lower service cost. Our strategy is compared with existing genetic algorithm (GA) through simulation experiments. Experimental results show that our strategy can achieve higher service success ratio and lower service cost.