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.
The resources in cloud environment have features such as large-scale, diversity, and heterogeneity. Moreover, the user requirements for cloud computing resources are commonly characterized by uncertainty and imprecision. Hereby, to improve the quality of cloud computing service, not merely should the traditional standards such as cost and bandwidth be satisfied, but also particular emphasis should be laid on some extended standards such as system friendliness. This paper proposes a dynamic resource scheduling method based on fuzzy control theory. Firstly, the resource requirements prediction model is established. Then the relationships between resource availability and the resource requirements are concluded. Afterwards fuzzy control theory is adopted to realize a friendly match between user needs and resources availability. Results show that this approach improves the resources scheduling efficiency and the quality of service (QoS) of cloud computing.
In order to implement fast, precise and stationarily tracking, digital air tracking servo system is designed. The system uses the MPU STM32F103 as the control center, UBA2032 H-bridge driver as the motor driver, velocity-measuring motor unit as the power and velocity sensor, rotary transformer as the angle sensor. ADC MAX1270 converts the analog data to digital data, which is transformed to STM32F103 through SPI interface. Angle, velocity and current which compose three-closed loop are measured and feedback and Fuzzy-PID control arithmetic is adopted. Route tracking experiments prove that the stability of the system is good and its static and dynamic target can satisfy the precision requirements.
In infrastructure as a service (IaaS) cloud mode equipment simulated training, to keep the resource utilization ratio in a rational high level, improve the training effect and reduce the system running cost, the problem of training virtual machine (TVM) placement needs to be resolved first. We make analysis to the problem and give the mathematical formulation to the problem. Then, we figure out the principle and target of the TVM placement. Based on above analysis, we propose a constrained immune memory and immunodominance clone (CIMIC) TVM placement optimization algorithm. By reverse optimization of the initial antibody population, the searching range is reduced. The common antibody population and the immunodominance antibody population evolve simultaneously, which realizes the simultaneous progressing of global searching and local searching of solutions. Further, local optimal is avoided by this means. Memory antibody makes ful use of the unfeasible solutions and the diversity of antibody population is maintained. The constraint information of the problem is utilized to improve the optimization effect. Experiment results show that the CIMIC algorithm improves the overall optimization effect of TVM placement, reduces the server number and improves the resource utilization and system stability.
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