Self-Adaptive Prediction of Cloud Resource Demands Using Ensemble Model and Subtractive-Fuzzy Clustering Based Fuzzy Neural Network

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 en...

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Bibliographic Details
Published in:Computational Intelligence and Neuroscience 2015-01, Vol.2015 (2015), p.1509-1522
Main Authors: Feng, Shaochong, Di, Yanqiang, Zhu, Yuanchang, Chen, Zhijia
Format: Article
Language:English
Subjects:
Algorithms
Artificial Intelligence
Artificial neural networks
Behavior
Cloud computing
Cluster Analysis
Clustering
Demand
Fuzzy
Fuzzy algorithms
Fuzzy Logic
Fuzzy systems
Humans
Learning curves
Mathematical models
Mathematical research
Networks
Neural networks
Neural Networks (Computer)
Workloads
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Summary: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.
ISSN:1687-5265
1687-5273
DOI:10.1155/2015/919805