Mobile edge computing(MEC) is an innovative technology that deploys computing resources around the demand side to provide near-request and responsiveness-guaranteed computing and storage services. A major attention paid by related works in this direction is mobility, where mobile traces of both edge users and servers are analyzed and exploited for accommodating offloading and migration requests for computation resources in a highly dynamic MEC environment. Our research in this work suggests that information of user interests, in terms of points of interest (POI), can be exploited in conjunction with mobility as well and proposes a hybrid method for for interest-informed and mobility-aware service migration path selection(HIMS). It synthesizes a trajectory prediction model and user interests prediction one for selecting target servers and reliable service migration paths. Experimental results demonstrate that our approach outperforms traditional methods across multiple performance metrics, especially those with sole input of mobility.

Mobile edge computing (MEC) is a forward-looking technology that provides services through resources to meet the needs of cloud-edge Internet of Things (IoT) devices. It provides computing and storage data facilities for IoT users and further renders services through resources in vicinity to fulfill the needs from IoT devices at the cloud edge. However, a major difficulty in guaranteeing reliable resource provisioning is mobility, which brings in chances of service migrations among difference distributed edge nodes and thus causes potential risks of service failures or disruptions. Existing solutions in this direction can be ineffective since they tend to consider that stability of inter-edge-node data transmission to be irrelevant to user mobility and are thus in lack of a comprehensive model for estimating effectiveness of migration paths selected. In this article, instead, we consider that the effectiveness of migrations paths to be selected are highly dependent on user mobility as well as inter-edge-node stability propose a novel predictive and mobile track-aware approach to fault-tolerant service migration path selection in MEC (PTSM). It is capable of exploiting uses trajectories for accurate predictions of future tracks and selecting target servers as well as migration paths with guaranteed migration reliability and performance in terms of multiple metrics. We demonstrate with extensive simulations and numerical results that our proposed method outperforms its peers in terms of migration reliability and performance.