Paper Info
Title
Memetic Algorithm Based on Community Detection for Energy-Efficient Service Migration Optimization in 5G Mobile Edge Computing
Abstract
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.
Year
DOI
Venue
2021
10.1109/PIMRC50174.2021.9569577
2021 IEEE 32ND ANNUAL INTERNATIONAL SYMPOSIUM ON PERSONAL, INDOOR AND MOBILE RADIO COMMUNICATIONS (PIMRC)
Keywords
DocType
Citations 
Evolutionary optimization, 5G energy efficiency, Mobile edge computing, Service migration, Community detection
Conference
0
PageRank 
References 
Authors
0.34
0
5
Name
Order
Citations
PageRank
Li Guo1188.06
Ling Liu25020344.35
Zhengping Liang300.34
Xiaoliang Ma418218.51
Zexuan Zhu598957.41