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公开(公告)号：US20200034270A1
公开(公告)日：2020-01-30
申请号：US16043297
申请日：2018-07-24
Applicant: VMware, Inc.
Inventor： Aalap Desai , Anant Agarwal , Alaa Shaabana , Ravi Cherukupalli , Sourav Kumar , Vikram Nair
Abstract: Systems and methods for analyzing a customer deployment in a converged or hyper-converged infrastructure are disclosed. A machine learning model is trained based upon historical usage data of other customer deployments. A k-means clustering is performed to generate a prediction as to whether a deployment is configured for optimal failover. Recommendations to improve failover performance can also be generated.
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公开(公告)号：US11379341B2
公开(公告)日：2022-07-05
申请号：US17224201
申请日：2021-04-07
Applicant: VMware, Inc.
Inventor： Aalap Desai , Anant Agarwal , Alaa Shaabana , Ravi Cherukupalli , Sourav Kumar , Vikram Nair
Abstract: Systems and methods for analyzing a customer deployment in a converged or hyper-converged infrastructure are disclosed. A machine learning model is trained based upon historical usage data of other customer deployments. A k-means clustering is performed to generate a prediction as to whether a deployment is configured for optimal failover. Recommendations to improve failover performance can also be generated.
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Patent Agency Ranking
公开(公告)号：US10990501B2
公开(公告)日：2021-04-27
申请号：US16785039
申请日：2020-02-07
Applicant: VMware, Inc.
Inventor： Aalap Desai , Anant Agarwal , Alaa Shaabana , Ravi Cherukupalli , Sourav Kumar , Vikram Nair
Abstract: Systems and methods for analyzing a customer deployment in a converged or hyper-converged infrastructure are disclosed. A machine learning model is trained based upon historical usage data of other customer deployments. A k-means clustering is performed to generate a prediction as to whether a deployment is configured for optimal failover. Recommendations to improve failover performance can also be generated.
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公开(公告)号：US20200174904A1
公开(公告)日：2020-06-04
申请号：US16785039
申请日：2020-02-07
Applicant: VMware, Inc.
Inventor： Aalap Desai , Anant Agarwal , Alaa Shaabana , Ravi Cherukupalli , Sourav Kumar , Vikram Nair
Abstract: Systems and methods for analyzing a customer deployment in a converged or hyper-converged infrastructure are disclosed. A machine learning model is trained based upon historical usage data of other customer deployments. A k-means clustering is performed to generate a prediction as to whether a deployment is configured for optimal failover. Recommendations to improve failover performance can also be generated.
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公开(公告)号：US20210255944A1
公开(公告)日：2021-08-19
申请号：US17224201
申请日：2021-04-07
Applicant: VMware, Inc.
Inventor： Aalap Desai , Anant Agarwal , Alaa Shaabana , Ravi Cherukupalli , Sourav Kumar , Vikram Nair
Abstract: Systems and methods for analyzing a customer deployment in a converged or hyper-converged infrastructure are disclosed. A machine learning model is trained based upon historical usage data of other customer deployments. A k-means clustering is performed to generate a prediction as to whether a deployment is configured for optimal failover. Recommendations to improve failover performance can also be generated.
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公开(公告)号：US10585775B2
公开(公告)日：2020-03-10
申请号：US16043297
申请日：2018-07-24
Applicant: VMware, Inc.
Inventor： Aalap Desai , Anant Agarwal , Alaa Shaabana , Ravi Cherukupalli , Sourav Kumar , Vikram Nair
Abstract: Systems and methods for analyzing a customer deployment in a converged or hyper-converged infrastructure are disclosed. A machine learning model is trained based upon historical usage data of other customer deployments. A k-means clustering is performed to generate a prediction as to whether a deployment is configured for optimal failover. Recommendations to improve failover performance can also be generated.
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公开(公告)号：US20200019841A1
公开(公告)日：2020-01-16
申请号：US16033460
申请日：2018-07-12
Applicant: VMware, Inc.
Inventor： Alaa Shaabana , Gregory Jean-Baptiste , Anant Agarwal , Rahul Chandrasekaran , Pawan Saxena
Abstract: Systems and methods for analyzing the usage of a set of workloads in a hyper-converged infrastructure are disclosed. A neural network model is trained based upon historical usage data of the set of workloads. The neural network model can make usage predictions of future demands on the set of workloads to minimize over-allocation or under-allocation of resources to the workloads.
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