Cloud‐Fog Cooperative Computation Offloading and Resource Allocation in Heterogeneous Networks Based on Genetic Algorithm

In this paper, we investigate the computation offloading and resource allocation strategy of the coexistence and synergy between fog computing and cloud computing in heterogeneous networks. Consider that the reported schemes have prohibitive complexity when achieving the optimal computation offloadi...

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Veröffentlicht in:IET communications Jg. 19; H. 1
Hauptverfasser: Wang, Qiang, Zhu, Chenming, Pan, Su, Zhong, Min, Li, Zibo
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Stevenage John Wiley & Sons, Inc 01.01.2025
Schlagworte:
Cloud computing
Complexity
Computation offloading
Computing costs
Edge computing
Energy consumption
Genetic algorithms
Internet of Things
Mutation
Networks
Optimization
Penalty function
Perturbation methods
Resource allocation
ISSN:1751-8628, 1751-8636
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Zusammenfassung:In this paper, we investigate the computation offloading and resource allocation strategy of the coexistence and synergy between fog computing and cloud computing in heterogeneous networks. Consider that the reported schemes have prohibitive complexity when achieving the optimal computation offloading strategy in cloud‐fog cooperative heterogeneous networks, an improved genetic algorithm (IGA) is proposed in this paper, which can maintain a low computation complexity while obtaining the optimal solution. In the IGA algorithm, we propose to use a penalty function to express the constraint conditions of the optimisation problem and use a non‐uniform mutation operator to accelerate the convergence speed. Besides, an improved method of parameter self‐adaptation and a perturbation method of mutation probability based on population fitness standard deviation are proposed to optimise the genetic algorithm. The numerical results show that the proposed genetic algorithm can obtain a lower average cost of the system while keeping a smaller computational cost.
Bibliographie:ObjectType-Article-1
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ISSN:1751-8628
1751-8636
DOI:10.1049/cmu2.70051