Research Article
Delay-Sensitive Slicing Resources Scheduling Based on Multi-MEC Collaboration in IoV
@INPROCEEDINGS{10.1007/978-3-030-92638-0_4, author={Yan Liang and Xin Chen and Shengcheng Ma and Libo Jiao}, title={Delay-Sensitive Slicing Resources Scheduling Based on Multi-MEC Collaboration in IoV}, proceedings={Collaborative Computing: Networking, Applications and Worksharing. 17th EAI International Conference, CollaborateCom 2021, Virtual Event, October 16-18, 2021, Proceedings, Part II}, proceedings_a={COLLABORATECOM PART 2}, year={2022}, month={1}, keywords={Internet of Vehicle (IoV) Vehicle to Infrastructure (V2I) Network slicing Mobile Edge Computing (MEC) Resource scheduling}, doi={10.1007/978-3-030-92638-0_4} }- Yan Liang
Xin Chen
Shengcheng Ma
Libo Jiao
Year: 2022
Delay-Sensitive Slicing Resources Scheduling Based on Multi-MEC Collaboration in IoV
COLLABORATECOM PART 2
Springer
DOI: 10.1007/978-3-030-92638-0_4
Abstract
The emerging Vehicle to Infrastructure (V2I) technology supports the service of task offloading under the Internet of vehicle (IoV), which improves the computational efficiency of the task. Facing tasks with different demands, network slicing technology builds a variety of logic private networks on a unified infrastructure, divides and allocates resources according to different user needs, which improves the vehicle transmission efficiency. Nevertheless, the diversity of demand resources and the randomness of tasks make the network scenario of IoV more complex. It is still a challenge to consider how to combine the network slicing technology to reduce the cost of offloading the computing task. In this paper, we study the scenario of autonomous vehicle offloading the computing task to Roadside Units (RSUs), and consider the multi-Mobile Edge Computing (multi-MEC) collaborative computing task to ensure that the task can be completed within tolerable delay. We consider the computing power and resource occupancy rate of MEC servers to ensure the user experience, and formulate a resource pricing scheme. Then, we propose a Performance-Price Ratio Task Scheduling (PPRTS) algorithm, which aims to complete the computing task within the maximum tolerable delay and reduce the cost of user. Simulation results show that the algorithm can effectively reduce the cost of the user.
