Energy-Efficient Service Migration for Multi-User Heterogeneous Dense Cellular Networks

Mobile edge computing (MEC) is a key enabler for ultra-low latency in heterogeneous dense cellular networks in the 5G era and beyond, by deploying services at the network edge. Due to high user mobility, the services are usually migrated to follow the users by predicting the user trajectory to achieve a balance between energy consumption and service latency. However, service migration for multi-user heterogeneous dense cellular networks is challenging because (1) the user trajectory prediction, which is crucial for service migration, becomes intractable with a large number of users, and (2) making service migration decisions for each user independently is subjected to interference among the users. Therefore, in this study, we formulated the service migration of all the users in MEC-enabled heterogeneous dense cellular networks as an optimization problem, with the objective of minimizing the average energy consumption while satisfying the service latency requirements, taking into account the interference among different users. Next, we developed an efficient energy-efficient online algorithm based on the Lyapunov and particle swarm optimizations, called EGO, to resolve the original problem without predicting the trajectories of the users. Finally, a series of simulations based on real-world mobility traces of vehicles in Bologna were conducted to establish the superiority of the EGO algorithm over state-of-the-art solutions.

Automated summary

Mobile edge computing is crucial for achieving ultra-low latency in 5G and beyond, by deploying services at the network edge. However, service migration in multi-user heterogeneous networks is challenging due to the difficulty in predicting user trajectories and interference among users. In this study, an optimization problem was formulated to minimize energy consumption and satisfy service latency requirements, and an efficient online algorithm called EGO was developed to solve the problem without predicting user trajectories.