Abstract: Mobile Edge Computing(MEC),as an innovative technology,deploys computing resources at the network edge to provide users with low-latency computing and storage services.In this research field,user mobility has consistently been a focal point,with existing work primarily focusing on analyzing and utilizing the movement trajectories between users and edge servers.However,such approaches often overlook users’ points of interest(POI) data and lack effective handling of migration failures,resulting in low service hit rates and high migration costs.Recent research has discovered that beyond mobility information,users’ points of interest data can also be effectively integrated and utilized.Addressing this finding,this paper proposes an Interest and Mobility-aware Service Path Migration(IMSPM) method.This approach fuses trajectory prediction models with user interest prediction models to achieve optimized target server selection and reliable,low-cost service migration path planning.Experimental results demonstrate that compared to traditional methods that rely solely on mobility information,IMSPM exhibits significant advantages in multiple performance metrics,including service hit rate and service migration frequency

Key words: Mobile edge computing, Trajectory prediction, Points of interest, Service migration, Migration path