A dynamic service migration strategy based on mobility prediction in edge computing

Abstract

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.

A Mobility Prediction Based Adaptive Task Migration in Mobile Edge Computing

“…In comparison with existing MDP-solving algorithms, the author claims that the proposed algorithm is more efficient. In [14] the L.Rui, S.Wang et al suggested a dynamic knowledge service placement and migration scheme in edge networks (DSM), which initializes each service's placement based on the first position and service requests of the users that meet their needs. The simulation results display the dynamic service migration approach increases mobile edge computing's service efficiency in the user's mobile scene compared to the conventional scheme.…”