Mobile edge computing is a new computing paradigm, which pushes cloud computing capabilities away from the centralized cloud to the network edge to satisfy the increasing amounts of low-latency tasks. However, challenges such as service interruption caused by user mobility occur. In order to address this problem, in this article, we first propose a multiple service placement algorithm, which initializes the placement of each service according to the user’s initial location and their service requests. Furthermore, we build a network model and propose a based on Lyapunov optimization method with long-term cost constraints. Considering the importance of user mobility, we use the Kalman filter to correct the user’s location to improve the success rate of communication between the device and the server. Compared with the traditional scheme, extensive simulation results show that the dynamic service migration strategy can effectively improve the service efficiency of mobile edge computing in the user’s mobile scene, reduce the delay of requesting terminal nodes, and reduce the service interruption caused by frequent user movement.

移动边缘计算是一种新的计算范式，它将云计算功能从集中式云推向了网络边缘，以满足越来越多的低延迟任务。然而，出现诸如由于用户移动性引起的服务中断之类的挑战。为了解决这个问题，在本文中，我们首先提出了一种多服务放置算法，该算法根据用户的初始位置及其服务请求来初始化每个服务的放置。此外，我们建立了一个网络模型，并提出了一种基于Lyapunov的具有长期成本约束的优化方法。考虑到用户移动性的重要性，我们使用卡尔曼滤波器来校正用户的位置，以提高设备与服务器之间通信的成功率。与传统方案相比，