OPTIMIZING DATA CENTER CONTROLS USING NEURAL NETWORKS
First Claim
1. A method comprising:
- receiving a state input characterizing a current state of a data center;
for each data center setting slate in a first set of data center setting slates that each define a respective combination of possible data center settings that affect a resource efficiency of the data center;
processing the state input and the data center setting slate through each machine learning model in an ensemble of machine learning models, wherein each machine learning model in the ensemble is configured to;
receive the state input and the data center setting slate, andprocess the state input and the data center setting slate to generate an efficiency score that characterizes a predicted resource efficiency of the data center if the data center settings defined by the data center setting slate are adopted in response to receiving the state input; and
selecting, based on the efficiency scores for the data center setting slates in the first set of data center setting slates, new values for the data center settings.
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Accused Products
Abstract
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for improving operational efficiency within a data center by modeling data center performance and predicting power usage efficiency. An example method receives a state input characterizing a current state of a data center. For each data center setting slate, the state input and the data center setting slate are processed through an ensemble of machine learning models. Each machine learning model is configured to receive and process the state input and the data center setting slate to generate an efficiency score that characterizes a predicted resource efficiency of the data center if the data center settings defined by the data center setting slate are adopted t. The method selects, based on the efficiency scores for the data center setting slates, new values for the data center settings.
14 Citations
20 Claims
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1. A method comprising:
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receiving a state input characterizing a current state of a data center; for each data center setting slate in a first set of data center setting slates that each define a respective combination of possible data center settings that affect a resource efficiency of the data center; processing the state input and the data center setting slate through each machine learning model in an ensemble of machine learning models, wherein each machine learning model in the ensemble is configured to; receive the state input and the data center setting slate, and process the state input and the data center setting slate to generate an efficiency score that characterizes a predicted resource efficiency of the data center if the data center settings defined by the data center setting slate are adopted in response to receiving the state input; and selecting, based on the efficiency scores for the data center setting slates in the first set of data center setting slates, new values for the data center settings. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16)
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17. A system comprising:
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one or more computers; and one or more storage devices storing instructions that are operable, when executed by one or more computers, to cause the one or more computers to perform operations comprising; receiving a state input characterizing a current state of a data center; for each data center setting slate in a first set of data center setting slates that each define a respective combination of possible data center settings that affect a resource efficiency of the data center; processing the state input and the data center setting slate through each machine learning model in an ensemble of machine learning models, wherein each machine learning model in the ensemble is configured to; receive the state input and the data center setting slate, and processing the state input and the data center setting slate to generate an efficiency score that characterizes a predicted resource efficiency of the data center if the data center settings defined by the data center setting slate are adopted in response to receiving the state input; and selecting, based on the efficiency scores for the data center setting slates in the first set of data center setting slates, new values for the data center settings. - View Dependent Claims (18)
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19. One or more non-transitory computer-readable storage mediums storing instructions that are executable by a processing device and upon such execution cause the processing device to perform operations comprising:
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receiving a state input characterizing a current state of a data center; for each data center setting slate in a first set of data center setting slates that each define a respective combination of possible data center settings that affect a resource efficiency of the data center; processing the state input and the data center setting slate through each machine learning model in an ensemble of machine learning models, wherein each machine learning model in the ensemble is configured to; receive the state input and the data center setting slate, and process the state input and the data center setting slate to generate an efficiency score that characterizes a predicted resource efficiency of the data center if the data center settings defined by the data center setting slate are adopted in response to receiving the state input; and selecting, based on the efficiency scores for the data center setting slates in the first set of data center setting slates, new values for the data center settings. - View Dependent Claims (20)
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Specification