title: Self-adaptive prediction of cloud resource demands using ensemble model and subtractive-fuzzy clustering based fuzzy neural network.
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.
authors.affiliation: Department of Electronic and Optics, Mechanical Engineering College, Shijiazhuang 050003, China.
authors.forename: Zhijia
authors.initials: Z
authors.lastname: Chen
authors.affiliation: Department of Electronic and Optics, Mechanical Engineering College, Shijiazhuang 050003, China.
authors.forename: Yuanchang
authors.initials: Y
authors.lastname: Zhu
authors.affiliation: Department of Electronic and Optics, Mechanical Engineering College, Shijiazhuang 050003, China.
authors.forename: Yanqiang
authors.initials: Y
authors.lastname: Di
authors.affiliation: Department of Electronic and Optics, Mechanical Engineering College, Shijiazhuang 050003, China.
authors.forename: Shaochong
authors.initials: S
authors.lastname: Feng
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D001519:Behavior; D016000:Cluster Analysis; D017143:Fuzzy Logic; D006801:Humans; D016571:Neural Networks, Computer