Cloud data center energy-saving scheduling implementation method based on rolling grey prediction model

Abstract

The invention discloses a cloud data center energy-saving scheduling implementation method based on a rolling grey prediction model. According to the method, the energy-saving process of the cloud data center is abstracted to a load prediction module, an error checking module, a thermal perception classification module and a virtual machine scheduling module, wherein the load prediction module is used for predicting the workload of the data center at the next moment to obtain the load utilization rate of each host; the thermal perception classification module divides the thermal states of all hosts according to the prediction values of the load utilization rate of the hosts, and determines that the utilization rate of the hosts in a higher thermal state is at a higher level, and the utilization rate of the hosts in a cooler state is at a lower level; and in order to maintain that most of the hosts are in a relatively mild thermal state, the virtual machine scheduling module carries out migration, integration and other operations to the virtual machines on each host according to the classification results of the thermal state to finally achieve the purposes of guaranteeing the service quality of the data center and reducing the energy consumption of the data center. According to the energy-saving scheduling implementation method disclosed by the invention, the problem that a traditional grey model has the difficulty of low precision due to partial value deficiency can be solved.

Description

Technical field [0001] The invention belongs to the field of cloud computing energy-saving scheduling, and specifically relates to a cloud data center energy-saving scheduling implementation method based on a rolling gray prediction model. Background technique [0002] As an emerging technology in the information and communication technology industry, cloud computing has gradually entered millions of households. It has been favored by the majority of enterprises and individual users for its high efficiency, low threshold, and high scalability. With the gradual maturity of cloud computing and the continuous enrichment of user needs, the scale of a series of supporting facilities such as data center servers is also growing rapidly. Large-scale cloud computing data centers containing tens of thousands of service nodes have also been established all over the world, which has allowed more computing and storage resources to be stored in the cloud, but this has also caused a series of e...

AI Technical Summary

Problems solved by technology

At present, the starting point of research on cloud computing energy saving at home and abroad is different, and there are certain deficiencies. At present, there are still major deficiencies in the workload monitoring and prediction of cloud data centers in the case of traffic bursts. There are flaws in the scheduling integration strategy of

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