Handover prediction integrated with service migration in 5G systems

Abstract

As the research community inclines toward adopting increasingly complex techniques for future networks, and simple methods are often ignored, being labeled as trivial. In this paper, we argue that simple methods can sometimes outperform more sophisticated ones. We demonstrate that by evaluating two prediction mechanisms to forecast mobile user's handovers exploiting user-network association patterns. We perform a series of experiments on real-world data, evaluating the performance characteristics of such methods over more sophisticated and complex prediction techniques. Furthermore, we discuss how to easily bootstrap these mechanisms into the 5G network architecture. We suggest the use of these methods associated with Multi-access Edge Computing (MEC) scenarios, as a mean to identify favorable edge nodes to host the mobile applications, to best provide continuous and QoS-aware service for mobile users.