Abstract
Mobile edge computing, enabling cloud computing capabilities at edge servers, is shown to be an effective way to enhance network performance as well as user experience. However, due to user mobility and limited coverage of a single edge server, service migration is inevitable to guarantee the quality of service (QoS). The service migration problem is to decide when, where and how to migrate the ongoing service from an edge server to another. In this paper, we formulate the problem as a partially observable markov decision process (POMDP) based on the fact that an edge server can obtain only partial user’s information. A learning based intelligent service migration algorithm, named iSMA, is proposed to minimize the long-term average service delay of all users. iSMA consists of two function modules, a latent space model and a cross-entropy planning algorithm. 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 28% and 24% when compared with two well-known deep learning based solutions.
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