CPU usage prediction for cloud resource provisioning based on deep belief network and particle swarm optimization

2020 ◽  
Vol 32 (14) ◽  
Author(s):  
Yiping Wen ◽  
Yuan Wang ◽  
Jianxun Liu ◽  
Buqing Cao ◽  
Qi Fu
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Resource Provisioning ◽  
Deep Belief Network ◽  
Swarm Optimization ◽  
Belief Network ◽  
Cloud Resource ◽  
Cpu Usage

scholarly journals Design and Application of Deep Belief Network Based on Stochastic Adaptive Particle Swarm Optimization

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Jianjian Yang ◽  
Boshen Chang ◽  
Xiaolin Wang ◽  
Qiang Zhang ◽  
Chao Wang ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Fault Diagnosis ◽  
Particle Swarm ◽  
Deep Belief Network ◽  
Support Vector ◽  
Swarm Optimization ◽  
Deep Fault ◽  
Belief Network ◽  
Adaptive Particle Swarm Optimization ◽  
Fitness Value

Due to the problem of poor recognition of data with deep fault attribute in the case of traditional superficial network under semisupervised and weak labeling, a deep belief network (DBN) was proposed for deep fault detection. Due to the problems of deep belief network (DBN) network structure and training parameter selection, a stochastic adaptive particle swarm optimization (RSAPSO) algorithm was proposed in this study to optimize the DBN. A stochastic criterion was proposed in this method to make the particles jump out of the original position search with a certain probability and reduce the probability of falling into the local optimum. The RSAPSO-DBN method used sample data to train the DBN and used the final diagnostic error rate to construct the fitness value function of the particle swarm algorithm. By comparing the minimum fitness value of each particle to determine the advantages and disadvantages of the model, the corresponding minimum fitness value was selected. Using the number of network nodes, learning rate, and momentum parameters, the optimal DBN classifier was generated for fault diagnosis. Finally, the validity of the method was verified by bearing data from Case Western Reserve University in the United States and data collected in the laboratory. Comparing BP (BP neural network), support vector machine, and heterogeneous particle swarm optimization DBN methods, the proposed method demonstrated the highest recognition rates of 87.75% and 93.75%. This proves that the proposed method possesses universality in fault diagnosis and provides new ideas for data identification with different fault depth attributes.

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