Optimizing computation offloading strategy in mobile edge computing based on swarm intelligence algorithms

EURASIP journal on advances in signal processing

article

Feng, Siling¹  Chen, Yinjie¹  Zhai, Qianhao²  Huang, Mengxing¹  Shu, Feng¹ 

[1] School of Information and Communication Engineering, Hainan University;School of Sciences, Hainan University;State Key Laboratory of Marine Resource Utilization in the South China Sea, Hainan University

关键词: Mobile edge computing;    Computation offloading;    Grey wolf optimizer;    Whale optimization algorithm;   

DOI  :  10.1186/s13634-021-00751-5

来源: SpringerOpen

【 摘 要 】

As the technology of the Internet of Things (IoT) and mobile edge computing (MEC) develops, more and more tasks are offloaded to the edge servers to be computed. The offloading strategy performs an essential role in the progress of computation offloading. In a general scenario, the offloading strategy should consider enough factors, and the strategy should be made as quickly as possible. While most of the existing model only considers one or two factors, we investigated a model considering three targets and improved it by normalizing each target in the model to eliminate the influence of dimensions. Then, grey wolf optimizer (GWO) is introduced to solve the improved model. To obtain better performance, we proposed an algorithm hybrid whale optimization algorithm (WOA) with GWO named GWO-WOA. And the improved algorithm is tested on our model. Finally, the results obtained by GWO-WOA, GWO, WOA, particle swarm optimization (PSO), and genetic algorithm (GA) are discussed. The results have shown the advantages of GWO-WOA.

【 授权许可】

CC BY