Research on Optimization of GWO-BP Model for Cloud Server Load Prediction

To improve the accuracy of cloud server resource load prediction, particle swarm optimization (PSO) algorithm, gray wolf optimization (GWO) algorithm and BP neural network are studied in-depth and applied. Firstly, the PSO algorithm is introduced to optimize the location update method in the search...

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Bibliographic Details
Published in:IEEE access 2021, Vol.9, p.162581-162589
Main Authors: Hou, Ke, Guo, Mingcheng, Li, Xinhao, Zhang, He
Format: Article
Language:English
Subjects:
Algorithms
Back propagation networks
BP neural network
Cloud computing
cloud server
Data models
gray wolf optimizer
Load modeling
Neural networks
Optimization
Particle swarm optimization
Prediction algorithms
Prediction models
Predictive models
Search process
Servers
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Summary:To improve the accuracy of cloud server resource load prediction, particle swarm optimization (PSO) algorithm, gray wolf optimization (GWO) algorithm and BP neural network are studied in-depth and applied. Firstly, the PSO algorithm is introduced to optimize the location update method in the search process of gray wolf. Secondly, the convex function is introduced to improve the linear convergence of the traditional GWO algorithm. Then the optimized GWO algorithm is used to further improve the assignment of weights and thresholds in the traditional BP neural network model, to construct a multi-stage optimized cloud server load prediction model, referred to as PSO- GWO-BP prediction model. Finally, the performance of the PSO- GWO-BP prediction model is verified by comparison experiments.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2021.3132052