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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. [ABSTRACT FROM AUTHOR]

As an extension of cloud computing, the edge computing has become an important pattern to deal with novel service scenarios of the Internet of Everything (IoE), especially for the rapidly increasing different kinds of service requests from edge equipment. It is still a great challenge to satisfy the demands of delay-sensitive applications, so as to optimize the service provision delay for edge equipment under 5G. In this paper, by introducing virtualized service functions (VSFs) into edge computing pattern, the mechanism of service function detection and placement among multiple Edge Computing Servers (ECSs) is proposed. We firstly improve the Ant colony optimization (ACO) method to adapt to the situation that the service requests may frequently change from different edge network domains. Based on the improved ACO, a scheme of searching for the locations (i.e., ECSs) of the requested service functions is devised, so as to optimize the service searching delay. Then, a service function placement scheme is presented, and it deploys most of appropriate service functions in each ECS by predicting the future requested frequencies of functions, which further reduces the overall service provision delay. In addition, it also improves the ECS computing capacity utilization. The simulation results show that the proposed mechanism is feasible and effective. [ABSTRACT FROM AUTHOR]