An online energy-saving control method and device for a cloud computing data center

Abstract

The invention discloses an on-line energy-saving control method and device for a cloud computing data center. The method comprises the steps of monitoring and collecting load energy consumption data of every node in the cloud computing data center; filtering the load energy consumption data according to preset performance constraint conditions, and enabling the filtered data to serve as an input sample of an adaptive neural network model; adopting the adaptive neural network model to generate an energy-saving strategy according to the input sample, and performing energy-saving control operation on every node according to the energy-saving strategy. According to the method and the device, simulation matching is performed on the load energy consumption data of the nodes and the energy-saving strategy on the basis of the adaptive neural network model, the appropriate energy-saving strategy is selected for the cloud computing data center, the input sample which is the load energy consumption data is filtered, input data not needing to undergo energy-saving planning are removed, the model input sample space scale is reduced, and on-line energy-saving control efficiency of the data center is improved.

Description

technical field [0001] The invention relates to the field of communications, in particular to an online energy-saving control method and device for a cloud computing data center. Background technique [0002] Cloud computing is an important form of information infrastructure and application service mode in the Internet age, and it is the trend of intensive development of the new generation of information technology. Data centers are the foundation of cloud computing. At present, my country's cloud data centers generally use servers, storage, and network equipment, supplemented by virtualization software, management software, operating systems, and application software. On average, the resource scale that my country's existing technology can manage and schedule is more than 10 times smaller than that of Google, and the large-scale data processing capability is more than 100 times worse. An important reason for the above gap is that Google has done a lot of research on data c...

AI Technical Summary

Problems solved by technology

[0006] In the initial stage of data center launch, there is often a lack of empirical and historical data for energy-saving control, and only a small amount of custom strategies can be relied on, which also lacks data samples for training neural network models, resulting in the inability of the data center to perform adaptive energy-saving control

[0007] Due to the large number of characteristic parameters representing data center resources, load and operating status, the sample space is huge, and even the adaptive neural network model with good convergence effect needs to be trained for a long time in the case of complex nonlinear relationships.

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