Memetic Algorithm Based on Community Detection for Energy-Efficient Service Migration Optimization in 5G Mobile Edge Computing

Mobile edge computing (MEC) can supplement cloud computing by helping to overcome the limitations of long physical transmission distances and accelerating the responsiveness of edge computing servers. In 5G (fifth generation) cellular networks, adopting MEC can guarantee ultralow latency. To enhance the MEC quality, optimization of the user service profile migration according to the user mobility is essential. However, this optimization establishes an NP-hard problem. Moreover, high-speed 5G base stations with MEC servers often experience high energy consumption. As conventional service migration algorithms such as those based on profile tracking and game theory tend to fall in local optima and neglect energy consumption constraints, we propose a memetic algorithm based on community detection local search (MA-CDLS) to continuously optimize the service migration in 5G MEC scenarios. During busy periods or in crowded areas, MA-CDLS adopts a single-objective optimization of user-perceived latency to achieve high-performance 5G services. During light-load periods or in uncrowded areas, MA-CDLS uses two measures, namely the user-perceived latency and energy consumption, to realize energy-efficient 5G services. MA-CDLS effectively reduces the search space and speeds up the elite selection in the meme operator. Experiments in simulated scenarios show that MA-CDLS achieves a lower user-perceived latency and energy consumption, than the traditional profile tracking and game theory methods, especially during congestion.

Automated summary

Mobile edge computing (MEC) complements cloud computing by overcoming long physical transmission distances and accelerating edge computing servers. Implementing MEC in 5G networks ensures ultralow latency, but optimizing user service migration poses an NP-hard problem. By proposing the MA-CDLS algorithm, we aim to continually optimize service migration in 5G MEC scenarios, achieving lower user-perceived latency and energy consumption compared to traditional methods like profile tracking and game theory, particularly during congestion.