Energy Saving Scheduling Method for Data Center

Abstract

The invention relates to an energy consumption management method of a data center and discloses an energy-saving scheduling method of the data center. The energy-saving scheduling method comprises the following specific steps: scheduling method generation: randomly generating a plurality of virtual machine scheduling methods, wherein the virtual machine scheduling methods refer to corresponding relationships between virtual machines and a server; scheduling optimization: adjusting the virtual machine scheduling methods through a cross algorithm and a mutation algorithm to obtain a final scheduling result, and executing the final scheduling result, wherein the cross algorithm comprises the step of selecting a higher-fitness virtual machine scheduling method to conduct cross operation; the mutation algorithm comprises the step of selecting a lower-fitness virtual machine scheduling method for conduct cross operation. The energy-saving scheduling method of the data center has the advantages that the problem of difficult energy consumption optimization of the data center is solved, the accuracy is high, the energy-saving effect is good, the calculation steps are fewer and online real-time calculation can be supported.

Description

technical field [0001] The invention relates to a data center energy consumption management method, in particular to an energy-saving scheduling method for a data center, through the optimization of an improved genetic algorithm-based model scheduling method, so as to achieve energy-saving effects. Background technique [0002] With the advent of the era of cloud computing and big data, more resources are concentrated in the cloud, and the energy consumption and environmental problems of data centers are becoming increasingly prominent. Building a green data center and realizing energy saving and emission reduction has become a hot spot in the industry in recent years. The energy consumption of the data center is mainly composed of computing energy consumption and cooling energy consumption. Therefore, most of the previous methods mainly consider these two aspects separately to achieve energy-saving optimization. [0003] In terms of reducing computing energy consumption, ...

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Problems solved by technology

However, the current work that comprehensively considers the energy consumption of the two has shortcomings such as rough modeling granularity and failure to consider virtualization scenarios.

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