面向用户移动的边缘云服务迁移方法  

A Migration Approach to Edge Cloud Services for Mobile Users

作　　者：李苗 LI Miao(College of Computer Science and Technology,China University of Petroleum(East China),Qingdao 266580)

机构地区：[1]中国石油大学(华东)计算机科学与技术学院,青岛266580

出　　处：《计算机与数字工程》2023年第6期1333-1337,共5页Computer & Digital Engineering

摘　　要：移动边缘云以分布式计算方式将计算能力以及存储资源下沉至网络边缘,而边缘云网络覆盖的局限性以及用户移动的不确定性使得用户对实时服务提出较高的要求。边缘云服务迁移可以为移动中的用户提供连续服务,而迁移系统的普适性和迁移决策的高效性成为迁移过程的一大挑战。因此论文提出了一种无模型的深度强化学习算法来解决迁移决策问题。实验结果表明,该方法可建立通用的系统架构且在服务时延、迁移开销以及两者之间的折衷优化方面优于现有的方法。Mobile edge cloud sinks computing power and storage resources to the edge of the network in a distributed comput-ing way.However,the limitation of the coverage of the edge cloud network and the uncertainty of users'mobility make users put for-ward higher requirements for real-time services.Edge cloud service migration can provide continuous services for users on the move,but the universality of migration system and the efficiency of migration decision become a major challenge in the migration process.The experimental results show that this approach establishes a common system architecture and is superior to existing ap-proaches in terms of user service latency,migration overhead,and trade-off optimization between them.

关 键 词：移动边缘云 用户移动 服务迁移 深度强化学习 优化目标 

分 类 号：TP301.6[自动化与计算机技术—计算机系统结构]