Service consumption planning for efficient service migration in mobile edge computing environments

Abstract

Mobile Edge Computing (MEC) is the concept of placing a cloud computing server to run at the network edge near the user. MEC is expected to achieve the QoS requirements of latency-sensitive applications, compute-intensive applications, and high-bandwidth applications. If users leave the limited coverage of an MEC server, they cannot receive services directly from that through a radio access network (RAN). The faster the user moves, the higher the probability of leaving the coverage of an MEC server. Seamless service migration should be implemented to assure service continuity. However, the time required for service migration is not negligible

thus, the frequent service migration should be avoided. In this study, we define the service consumption plan optimization problem and prove its NP-completeness. To solve the problem, we proposed a genetic algorithm-based method and partial re-planning methods. We conducted vast experiments to evaluate the algorithm with respect to other baselines based on real-world data sources. The results showed that the proposed method generates improved service consumption plans than other baselines in all the scenarios we set out.