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US Patent 11954527 Machine learning system and resource allocation method thereof

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Is a
Patent
Patent
0

Patent attributes

Patent Jurisdiction
United States Patent and Trademark Office
United States Patent and Trademark Office
0
Patent Number
119545270
Patent Inventor Names
Yi-Fang Lu0
Shih-Chang Chen0
Yi-Chin Chu0
Date of Patent
April 9, 2024
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Patent Application Number
172178460
Date Filed
March 30, 2021
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Patent Citations
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US Patent 9311157 Method and apparatus for dynamic resource allocation of processing units on a resource allocation plane having a time axis and a processing unit axis
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US Patent 9411659 Data processing method used in distributed system
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US Patent 10140161 Workload aware dynamic CPU processor core allocation
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US Patent 10205675 Dynamically adjusting resources to meet service level objectives
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US Patent 10268513 Computing resource allocation optimization
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US Patent 10467129 Measuring and optimizing test resources and test coverage effectiveness through run time customer profiling and analytics
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US Patent 10565020 Adjustment of the number of central processing units to meet performance requirements of an I/O resource
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US Patent 10540206 Dynamic virtual processor manager
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...
Patent Primary Examiner
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Diem K Cao
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CPC Code
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G06F 2209/501
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G06F 9/50
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Patent abstract

A resource allocation method comprises using resources with a used resource quantity of a machine learning system to execute a first experiment which has a first minimum resource demand, receiving an experiment request associated with a target dataset, deciding a second experiment according to the target dataset, deciding a second minimum resource demand of the second experiment, allocating resources with a quantity equal to the second minimum resource demand for an execution of the second experiment when a total resource quantity of the machine learning system meets a sum of the first minimum resource demand and the second minimum resource demand and a difference between the total resource quantity and the used resource quantity meets the second minimum resource demand, determining that the machine learning system has an idle resource, and selectively allocating said the idle resource for at least one of the first experiment and the second experiment.

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