Original scientific paper
https://doi.org/10.1080/00051144.2020.1837499
A novel edge server selection method based on combined genetic algorithm and simulated annealing algorithm
Yi-wen Zhang
; School of Computer Science and Technology, Anhui University, Hefei, People’s Republic of China
Wen-ming Zhang
; School of Computer Science and Technology, Anhui University, Hefei, People’s Republic of China
Kai Peng
; College of Engineering, Huaqiao University, Quanzhou, Fujian, People’s Republic of China
Deng-cheng Yan
; Institutes of Physical Science and Information Technology, Anhui University, Hefei, People’s Republic of China
Qi-lin Wu
; School of Information Engineering, Chaohu University, Chaohu, Anhui, People’s Republic of China
Abstract
Mobile edge computing is a new paradigm which provides cloud computing capabilities at the edge of pervasive radio access networks in close proximity to users. The problem of edge server selection in mobile edge environment in terms of user’s overhead is investigated in this paper. Due to the limited resources of edge server, we firstly study the task completion probability of edge servers. Secondly, we formally model the problem of edge server selection in terms of time
latency and energy consumption. More especially, the computation overhead method for completing the task in cases of both service migration and non-migration is investigated. Then, a new optimized edge server selection algorithm, called combined Genetic algorithm and simulated Annealing algorithm for edge Server Selection (GASS) is designed. Finally, a series of experiments on a real-word data-trace are conducted to evaluate the performance of GASS. The results show that GASS can effectively minimize the overhead of the user and outperform traditional heuristic algorithms.
Keywords
Mobile edge computing; edge server selection; time latency; energy consumption; service migration
Hrčak ID:
258411
URI
Publication date:
30.3.2021.
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