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CLOUD DATA CENTRE ENERGY-SAVING SCHEDULING IMPLEMENTATION METHOD BASED ON ROLLING GREY PREDICTION MODEL
CLOUD DATA CENTRE ENERGY-SAVING SCHEDULING IMPLEMENTATION METHOD BASED ON ROLLING GREY PREDICTION MODEL
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机译：基于滚动灰色预测模型的云数据中心节能调度实现方法
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摘要
Disclosed is a cloud data centre energy-saving scheduling implementation method based on a rolling grey prediction model. In the present invention, a cloud data centre energy-saving flow is abstracted as four modules of load prediction, error checking, thermal perception classification and virtual machine scheduling. A working load of the data centre at the next moment is predicted by means of the load prediction module to obtain a load utilization rate of each host. The thermal perception classification module can divide the thermal states of all hosts according to predicted values of the load utilization rates of the hosts, wherein the utilization rate of a host in the state of being hotter is at a higher level, while the utilization rate of a host in the state of being cooler is at a lower level. In order that most of the hosts are maintained in the thermal state of being relatively mild, the virtual machine scheduling module performs operations, such as migration and integration, on a virtual machine on each of the hosts according to a classification result of the thermal states, so as to finally achieve the purposes of guaranteeing the service quality of the data centre and reducing the energy consumption thereof. The present invention overcomes the problem that a traditional grey model has the difficulty of low precision due to the deficiency of some values.
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