Hybrid learning based service migration for cost minimization with deadlines in multi-user mobile edge computing systems

Mobile edge computing, a new computing model, aims to reduce network latency and improve user experience by placing computing resources closer to the users. However, when users move away from the current service areas, the quality of services will decline. To guarantee the quality of services, service migration technique is proposed and studied. Due to the wireless resource contention among users, there is service migration decisionmaking coupling among multiple users. Meanwhile, making real-time service migration decisions for multiple users to minimize service cost with application deadlines remains an open challenge because of the huge state space. In this paper, we formalize the service migration problem with deadlines as a Markov decision process (MDP) and propose a hybrid learning based service migration strategy. The proposed approach integrates Monte Carlo method and DQN learning, which adopts online training without Q-values and neural network approximation to generate service migration decisions for multiple users. Experimental results demonstrate that the proposed hybrid learning based service migration strategy outperforms the existing migration strategies in terms of service success ratio and service cost.