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

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 strate...

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Bibliographic Details
Published in:EURASIP journal on advances in signal processing 2021-07, Vol.2021 (1), p.1-15, Article 36
Main Authors: Feng, Siling, Chen, Yinjie, Zhai, Qianhao, Huang, Mengxing, Shu, Feng
Format: Article
Language:English
Subjects:
Algorithms
Analysis
Computation offloading
Edge computing
Engineering
Genetic algorithms
Grey wolf optimizer
Integrated Space-Ground-Sea Wireless Networks for B5G
Internet of Things
Mathematical optimization
Mobile computing
Mobile edge computing
Normalizing
Particle swarm optimization
Quantum Information Technology
Signal,Image and Speech Processing
Spintronics
Swarm intelligence
Whale optimization algorithm
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Summary: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.
ISSN:1687-6180
1687-6172
1687-6180
DOI:10.1186/s13634-021-00751-5