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Self-adaptive prediction of cloud resource demands using ensemble model and subtractive-fuzzy clustering based fuzzy neural network.

Chen Z, Zhu Y, Di Y, Feng S - Comput Intell Neurosci (2015)

Bottom Line: To allocate resources for users dynamically and effectively, accurate resource demands predicting is essential.We adopt some base predictors to compose the ensemble model.Then the structure and learning algorithm of fuzzy neural network is researched.

View Article: PubMed Central - PubMed

Affiliation: Department of Electronic and Optics, Mechanical Engineering College, Shijiazhuang 050003, China.

ABSTRACT
In IaaS (infrastructure as a service) cloud environment, users are provisioned with virtual machines (VMs). To allocate resources for users dynamically and effectively, accurate resource demands predicting is essential. For this purpose, this paper proposes a self-adaptive prediction method using ensemble model and subtractive-fuzzy clustering based fuzzy neural network (ESFCFNN). We analyze the characters of user preferences and demands. Then the architecture of the prediction model is constructed. We adopt some base predictors to compose the ensemble model. Then the structure and learning algorithm of fuzzy neural network is researched. To obtain the number of fuzzy rules and the initial value of the premise and consequent parameters, this paper proposes the fuzzy c-means combined with subtractive clustering algorithm, that is, the subtractive-fuzzy clustering. Finally, we adopt different criteria to evaluate the proposed method. The experiment results show that the method is accurate and effective in predicting the resource demands.

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The comparison of the training performance of different learning algorithms.
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fig9: The comparison of the training performance of different learning algorithms.

Mentions: Figure 9(a) depicts the training procedure without self-adjusting learning rate. After 100 training cycles, the error Pa of FNN is approximately 0.048. The error is very large. Figure 9(b) depicts the performance of FNN with self-adjusting learning rate and momentum weight. After 100 training cycles, the error Pb reaches 0.0015. The ratio of Pa to Pb is r = Pa/Pb = 0.048/0.0015 = 32.


Self-adaptive prediction of cloud resource demands using ensemble model and subtractive-fuzzy clustering based fuzzy neural network.

Chen Z, Zhu Y, Di Y, Feng S - Comput Intell Neurosci (2015)

The comparison of the training performance of different learning algorithms.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4321097&req=5

fig9: The comparison of the training performance of different learning algorithms.
Mentions: Figure 9(a) depicts the training procedure without self-adjusting learning rate. After 100 training cycles, the error Pa of FNN is approximately 0.048. The error is very large. Figure 9(b) depicts the performance of FNN with self-adjusting learning rate and momentum weight. After 100 training cycles, the error Pb reaches 0.0015. The ratio of Pa to Pb is r = Pa/Pb = 0.048/0.0015 = 32.

Bottom Line: To allocate resources for users dynamically and effectively, accurate resource demands predicting is essential.We adopt some base predictors to compose the ensemble model.Then the structure and learning algorithm of fuzzy neural network is researched.

View Article: PubMed Central - PubMed

Affiliation: Department of Electronic and Optics, Mechanical Engineering College, Shijiazhuang 050003, China.

ABSTRACT
In IaaS (infrastructure as a service) cloud environment, users are provisioned with virtual machines (VMs). To allocate resources for users dynamically and effectively, accurate resource demands predicting is essential. For this purpose, this paper proposes a self-adaptive prediction method using ensemble model and subtractive-fuzzy clustering based fuzzy neural network (ESFCFNN). We analyze the characters of user preferences and demands. Then the architecture of the prediction model is constructed. We adopt some base predictors to compose the ensemble model. Then the structure and learning algorithm of fuzzy neural network is researched. To obtain the number of fuzzy rules and the initial value of the premise and consequent parameters, this paper proposes the fuzzy c-means combined with subtractive clustering algorithm, that is, the subtractive-fuzzy clustering. Finally, we adopt different criteria to evaluate the proposed method. The experiment results show that the method is accurate and effective in predicting the resource demands.

Show MeSH