计算机科学
计算卸载
云计算
分布式计算
服务器
任务(项目管理)
GSM演进的增强数据速率
移动边缘计算
异步通信
边缘计算
计算
方案(数学)
互联网
计算机网络
算法
电信
数学分析
数学
管理
万维网
经济
操作系统
作者
Chen Chen,Haofei Li,Huan Li,Rufei Fu,Yangyang Liu,Shaohua Wan
出处
期刊:IEEE transactions on green communications and networking
[Institute of Electrical and Electronics Engineers]
日期:2022-09-01
卷期号:6 (3): 1481-1493
被引量:41
标识
DOI:10.1109/tgcn.2022.3167643
摘要
The rapid development of the Internet of Vehicles (IoV) leads to various on-board applications including delay-sensitive and compute-intensive applications. However, vehicles with limited computing resources cannot meet the requirements of the delay for these applications. The application of edge computing on the IoV can deal with the above problems. However, most existing edge computing offloading work only considers static scenarios. These schemes are difficult to adapt to dynamic processes under continuous time. To solve this problem, in this article, we propose an end-edge-cloud architecture of vehicles for task computation offloading, where considers three task computing methods. For the dynamically changing environment in the IoV, we utilize an Asynchronous Advantage Actor-Critic (A3C) based computation offloading algorithm to solve the problem and seek optimal offloading decisions. While considering efficiency and fairness, our solution enables vehicle users to obtain computing services from edge servers in real-time and conveniently. The reward function in this paper contains a relative efficiency factor and a relative fairness factor, which are indicators to measure the efficiency of task completion as well as the relative fairness between vehicles. These indicators can be adjusted to obtain more targeted offloading decisions in different scenarios. The experimental results show that the scheme proposed in this paper can achieve good convergence performance and adapt to the dynamic offloading scene. Compared with the other scheme, our scheme can achieve better performance in terms of efficiency of task completion and fairness among tasks. The numerical results show that compared to the Deep Q Network-based scheme, the local computing scheme, the edge computing scheme, and the random scheme, our algorithm can save 7.2%, 18.5%, 41.8%, and 33.5% of the mean for average delay, respectively.
科研通智能强力驱动
Strongly Powered by AbleSci AI