Task Adaptation Algorithm Based on Edge Computing in Internet of Vehicles
Haitao Zhao, Xiang Ren, Qixing Zhu, Yinyang Zhu, Chen He · 2019
As vehicle applications continue to become more complex, the computational performance requirements in the Internet of Vehicles continue to increase. Traditional cloud computing faces bottlenecks such as limited computing resources, high latency, low user experience and so on. The introduction of edge computing in vehicle networks can solve these limitations. In this paper, we present an effective solution for periodically adapting incoming tasks in an edge computing network, thereby increasing the number of tasks that can be processed in an edge computing network and simplifying our approach through linear programming. The simulation results show that the task adaptation generated by our method can improve the task adaptation efficiency, save energy consumption and improve quality of experience.