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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 test results and error of ESFCFNN.
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fig6: The test results and error of ESFCFNN.

Mentions: The test data is used to verify the prediction method. The test results are shown in Figure 6. In Figure 6(a), the blue curve is the prediction value and the red curve is the actual value. We can see that the most of the two curves almost overlap with each other. Figure 6(b) shows the prediction error. We can see that the maximum normalized error is 8%. Most of the normalized error data falls within −8%~+8%. The prediction results are acceptable.


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 test results and error of ESFCFNN.
© Copyright Policy
Related In: Results  -  Collection

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

fig6: The test results and error of ESFCFNN.
Mentions: The test data is used to verify the prediction method. The test results are shown in Figure 6. In Figure 6(a), the blue curve is the prediction value and the red curve is the actual value. We can see that the most of the two curves almost overlap with each other. Figure 6(b) shows the prediction error. We can see that the maximum normalized error is 8%. Most of the normalized error data falls within −8%~+8%. The prediction results are acceptable.

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