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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 performance without clustering.
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fig10: The performance without clustering.

Mentions: In Figure 10, the performance of FNN without using clustering algorithm is depicted. Figure 10(a) shows the convergence procedure. We can see that the convergence speed is slowed down. After more than 30 steps, the error falls down to 0.05. While Figure 9(b) shows that, with clustering algorithm, this procedure only needs less than 10 steps. Figure 10(b) shows the training error of FNN without using clustering algorithm. Compared with Figure 5, the training error of FNN without using clustering algorithm is greater. From the above comparison, the performance is improved using clustering algorithm.


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 performance without clustering.
© Copyright Policy
Related In: Results  -  Collection

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

fig10: The performance without clustering.
Mentions: In Figure 10, the performance of FNN without using clustering algorithm is depicted. Figure 10(a) shows the convergence procedure. We can see that the convergence speed is slowed down. After more than 30 steps, the error falls down to 0.05. While Figure 9(b) shows that, with clustering algorithm, this procedure only needs less than 10 steps. Figure 10(b) shows the training error of FNN without using clustering algorithm. Compared with Figure 5, the training error of FNN without using clustering algorithm is greater. From the above comparison, the performance is improved using clustering algorithm.

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