公开(公告)号：US11347521B2

公开(公告)日：2022-05-31

申请号：US16744896

申请日：2020-01-16

Applicant: VMware, Inc.

Inventor： Anant Agarwal ,  Rahul Chandrasekaran ,  Aalap Desai ,  Vikram Nair ,  Zhelong Pan

IPC: G06F9/4401 ,  G06F9/455 ,  G06F11/34

Abstract: A method of restarting a virtual machine running in a cluster of hosts in a first data center, in a second data center, wherein each virtual machine is assigned a priority level, includes: transmitting virtual machines images running in the cluster at a first time to the second data center; selecting virtual machines to be restarted in the second data center according to priority levels assigned; and for each selected virtual machine, (a) generating difference data in an image of the selected virtual machine at a second time and at the first time, (b) transmitting the difference data to the second data center, (c) setting the virtual machine inactive in the first data center, and (d) communicating with the second data center to set as active; and power on, a virtual machine in the second data center using the image of the virtual machine transmitted to the second data center.

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

IPC: G06F11/34 ,  G06F9/455 ,  G06K9/62 ,  G06N20/00

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.

公开(公告)号：US10346191B2

公开(公告)日：2019-07-09

申请号：US15368381

申请日：2016-12-02

Applicant: VMware, Inc.

Inventor： Manoj Krishnan ,  Anant Agarwal ,  Rahul Chandrasekaran ,  Prafulla Mahindrakar ,  Ravi Cherukupalli

IPC: G06F9/455 ,  G06F9/50

Abstract: A number of hosts in a logical cluster is adjusted up or down in an elastic manner by tracking membership of hosts in the cluster using a first data structure and tracking membership of hosts in a spare pool using a second data structure, and upon determining that a triggering condition for adding another host is met and that all hosts in the cluster are being used, selecting a host from the spare pool, and programmatically adding an identifier of the selected host to the first data structure and programmatically deleting the identifier of the selected host from the second data structure.

公开(公告)号：US11669735B2

公开(公告)日：2023-06-06

申请号：US16751127

申请日：2020-01-23

Applicant: VMware, Inc.

Inventor： Ala Shaabana ,  Arvind Mohan ,  Vikram Nair ,  Anant Agarwal ,  Aalap Desai ,  Ravi Kant Cherukupalli ,  Pawan Saxena

IPC: G06N3/08 ,  G06F11/14 ,  G06N3/044 ,  G06N3/045 ,  G06N3/006 ,  G06N3/088 ,  G06N3/082

CPC classification number: G06N3/08 ,  G06F11/1476 ,  G06N3/006 ,  G06N3/044 ,  G06N3/045 ,  G06N3/088 ,  G06N3/082

Abstract: A system and method for automatically generating recurrent neural networks for log anomaly detection uses a controller recurrent neural network that generates an output set of hyperparameters when an input set of controller parameters is applied to the controller recurrent neural network. The output set of hyperparameters is applied to a target recurrent neural network to produce a child recurrent neural network with an architecture that is defined by the output set of hyperparameters. The child recurrent neural network is then trained, and a log classification accuracy of the child recurrent neural network is computed. Using the log classification accuracy, at least one of the controller parameters used to generate the child recurrent neural network is adjusted to produce a different input set of controller parameters to be applied to the controller recurrent neural network so that a different child recurrent neural network for log anomaly detection can be generated.

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

IPC: G06F11/34 ,  G06F9/455 ,  G06K9/62 ,  G06N20/00

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.

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

IPC: G06F11/34 ,  G06F9/455 ,  G06K9/62 ,  G06N20/00

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.

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

IPC: G06N3/04 ,  G06N3/08 ,  G06F9/50

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.