Data center job scheduling method and system based on temperature prediction
A data center and job scheduling technology, which is applied in electrical digital data processing, digital data processing components, multi-programming devices, etc., can solve the problems of one-sided optimization, low energy saving efficiency, etc. The effect of reducing energy consumption
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Embodiment 1
[0029] This embodiment provides a data center job scheduling method based on temperature prediction;
[0030] like figure 1 As shown, the data center job scheduling method based on temperature prediction includes:
[0031] S100: Obtain the relevant parameters of the data center cabinet, the relevant parameters of the servers in the cabinet, the size of the resources required for the job to be scheduled in the job queue, and the relevant parameters of the cooling equipment;
[0032] S200: Preprocess the acquired data, and perform feature screening on the preprocessed data; based on the trained machine learning model and the features obtained by screening, predict the temperature of the cabinet in the future set time period, and select the one with the lowest temperature cabinet;
[0033] S300: Perform initial scheduling and optimal scheduling of the job to be scheduled in several servers in the cabinet with the lowest temperature, and select the optimal mapping scheme between...
Embodiment 2
[0096] This embodiment provides a data center job scheduling system based on temperature prediction;
[0097] like figure 2 As shown, the data center job scheduling system based on temperature prediction includes:
[0098] a resource monitoring module, which is configured to: obtain relevant parameters of the data center cabinet, relevant parameters of the servers in the cabinet, the size of the resources required by the jobs to be scheduled in the job queue, and the relevant parameters of the cooling equipment;
[0099] The temperature prediction module is configured to: preprocess the acquired data, and perform feature screening on the preprocessed data; based on the trained machine learning model and the features obtained by screening, predict the temperature of the cabinet in a set time period in the future. temperature, select the cabinet with the lowest temperature;
[0100] The job scheduling module is configured to: perform initial scheduling and optimal scheduling ...
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