  As a key technology in the 5G era, Mobile Edge Computing (MEC) has developed
rapidly in recent years. MEC aims to reduce the service delay of mobile users,
while alleviating the processing pressure on the core network. MEC can be
regarded as an extension of cloud computing on the user side, which can deploy
edge servers and bring computing resources closer to mobile users, and provide
more efficient interactions. However, due to the user's dynamic mobility, the
distance between the user and the edge server will change dynamically, which
may cause fluctuations in Quality of Service (QoS). Therefore, when a mobile
user moves in the MEC environment, certain approaches are needed to schedule
services deployed on the edge server to ensure the user experience. In this
paper, we model service scheduling in MEC scenarios and propose a delay-aware
and mobility-aware service management approach based on concise probabilistic
methods. This approach has low computational complexity and can effectively
reduce service delay and migration costs. Furthermore, we conduct experiments
by utilizing multiple realistic datasets and use iFogSim to evaluate the
performance of the algorithm. The results show that our proposed approach can
optimize the performance on service delay, with 8% to 20% improvement and
reduce the migration cost by more than 75% compared with baselines during the
rush hours.
