Service migration in mobile edge computing: A deep reinforcement learning approach

Abstract

Abstract In mobile edge computing, service migration can not only reduce the access latency but also reduce the network costs for users. However, due to bandwidth bottleneck, migration costs should also be considered during service migration. In this way, the trade‐off between benefits of service migration and total service costs is very important for the cloud service providers. In this paper, we propose an efficient dynamic service migration algorithm named SMDQN, which is based on reinforcement learning. We consider each mobile application service can be hosted on one or more edge nodes and each edge node has limited resources. SMDQN takes total delay and migration costs into consideration. And to reduce the size of Markov decision process space, we devise the deep reinforcement learning algorithm to make a fast decision. We implement the algorithm and test the performance and stability of it. The simulation result shows that it can minimize the service costs and adapt well to different mobile access patterns.