摘要
文章提出一种基于贝叶斯网络的MEC随机任务迁移算法,通过将应用转换为包含多个子任务的有向图,利用子任务间的关联关系及贝叶斯网的联合概率来生成一组最小化移动设备能耗的策略。仿真结果表明,该算法相比移动设备本地执行算法在处理计算任务时节省能耗达28.7%。
A Bayesian network-based MEC stochastic task offoading algorithm is proposed in this paper, by transforming the application into a directed graph containing multiple sub-tasks, and then using the correlations between sub-tasks and the joint probabilities of the Bayesian networks, to generate a set of strategies to minimize the energy consumption of mobile devices. The simulation results show that the proposed algorithm saves energy consumption by 28.7% compared with the local execution algorithm when processing the computing tasks.
作者
薛宁
霍如
刘江
Xue Ning;Huo Ru;Liu Jiang(Beijing University of Technology,Beijing Advanced Innovation Center for Future Internet Technology,Beijing 100124,China;Beijing University of Posts and Telecommunications,State Key Laboratory Of Networking And Switching Technology,Beijing 100876,China)
出处
《信息通信技术》
2018年第5期9-15,共7页
Information and communications Technologies
关键词
移动边缘计算
随机任务迁移
贝叶斯网络
移动设备能耗
Mobile-Edge Computing
Stochastic Task Offoading
Bayesian Network
Mobile Device’s Energy Consumption