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US Patent 10355946 Capacity planning

Patent 10355946 was granted and assigned to Hortonworks on July, 2019 by the United States Patent and Trademark Office.

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Contents

Is a
Patent
Patent
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Patent attributes

Patent Applicant
Hortonworks
Hortonworks
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Current Assignee
Hortonworks
Hortonworks
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Patent Jurisdiction
United States Patent and Trademark Office
United States Patent and Trademark Office
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Patent Number
103559460
Patent Inventor Names
Paul Daniel Codding0
Sheetal Dinkar Dolas0
Date of Patent
July 16, 2019
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Patent Application Number
148718890
Date Filed
September 30, 2015
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Patent Citations Received
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US Patent 12086710 Predictive data capacity planning
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US Patent 11989429 Recommending changes to a storage system
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US Patent 12020070 Managing computer workloads across distributed computing clusters
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US Patent 12061822 Utilizing volume-level policies in a storage system
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US Patent 12086650 Workload placement based on carbon emissions
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US Patent 12086651 Migrating workloads using active disaster recovery
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US Patent 10656700 Power management in an integrated circuit
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US Patent 10853148 Migrating workloads between a plurality of execution environments
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Patent Primary Examiner
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Michael A Keller
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Patent abstract

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for identifying a plurality of software components deployed on a cluster of computing nodes, each component operating on one or more respective nodes within the cluster to process a workload, each component utilizing at least the following resources of the one or more respective nodes: storage, processing power, memory, and disk and network input and output operations; determining, for each identified component, current and historical usages of the resources of the one or more respective nodes by the component when processing the workload; receiving data specifying a future workload; and calculating, from the current and historical usages of each component, an estimated capacity required for the cluster to process the future workload, where the estimated capacity specifies, at least, how many nodes should be in the cluster to process the future workload.

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