A Novel Deep Reinforcement Learning based service migration model for Mobile Edge Computing

Cloud Computing has emerged as a foundation of smart environments by encapsulating and virtualizing the underlying design and implementation details. Concerning the inherent latency and deployment issues, Mobile Edge Computing seeks to migrate services in the vicinity of mobile users. However, the current migration-based studies lack the consideration of migration cost, transaction cost, and energy consumption on the system-level with discussion on the impact of personalized user mobility. In this paper, we implement an enhanced service migration model to address user proximity issues. We formalize the migration cost, transaction cost, energy consumption related to the migration process. We model the service migration issue as a complex optimization problem and adapt Deep Reinforcement Learning to approximate the optimal policy. We compare the performance of the proposed model with the recent Q-learning method and other baselines. The results demonstrate that the proposed model can estimate the optimal policy with complicated computation requirements.