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

• Published in: IEEE access Vol. 9; pp. 162581 - 162589
• Main Authors: Hou, Ke, Guo, Mingcheng, Li, Xinhao, Zhang, He
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• 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
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2021.3132052

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.