Dynamic Service Migration with Partially Observable Information in Mobile Edge Computing

Service migration, determining when, where and how to migrate the ongoing service, is of paramount importance in mobile edge computing (MEC) for provisioning high quality of service to mobile users. With respect to high network dynamics and stringent delay requirements, service migration is a rather challenging issue in MEC. In this paper, we formulate service migration as a partially observable Markov decision process (POMDP) based on the fact that an edge server can only obtain partial users' information, or the information of its own serving users. A learning-based intelligent service migration algorithm, named iSMA, is proposed to minimize the long-term service delay of all users. iSMA consists of two function modules, a latent space model and a cross-entropy planning algorithm, where the latent space model is used to infer the full state of the environment based on the partial information observed, and the cross-entropy planning algorithm is used to search the best service migration strategy. Numerical results show that our proposed iSMA reduces the service delay by about 58% when compared with a well-known deep learning-based solution.