公开(公告)号：US20250036443A1

公开(公告)日：2025-01-30

申请号：US18378220

申请日：2023-10-10

Applicant: VMware, Inc.

Inventor： AMITA VASUDEV KAMAT ,  Piyush Hasmukh Parmar ,  Pawan Saxena

IPC: G06F9/455

Abstract: System and method for provide a computing infrastructure as a service creates a flexible cloud namespace in a software-defined data center (SDDC) in response to a first instruction from a user and deploys virtual computing instances in the flexible cloud namespace in response to a second instruction from the user. The flexible cloud namespace comprises a logical construct with resources in the SDDC that are supported by underlying SDDC management entities, where the virtual computing instances execute in the flexible cloud namespace of the SDDC using the resources.

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：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.

公开(公告)号：US20230342176A1

公开(公告)日：2023-10-26

申请号：US17728172

申请日：2022-04-25

Applicant: VMware, Inc.

Inventor： Vikram Nair ,  Pawan Saxena ,  Ravi Cherukupalli ,  Larry Henderson

IPC: G06F9/455

CPC classification number: G06F9/45558 ,  G06F9/45545 ,  G06F2009/4557 ,  G06F2009/45583 ,  G06F2009/45595

Abstract: System and computer-implemented method for managing on-prem hyperconverged systems automatically detects a condition for additional resources in an on-prem hyperconverged system with a cluster of active host computers and at least one dark capacity host computer that is not part of the cluster. The resources of at least one dark capacity host computer in the on-prem hyperconverged system is used to address the condition for additional resources after the dark capacity host computer has been added to the cluster.

公开(公告)号：US11789800B2

公开(公告)日：2023-10-17

申请号：US17548635

申请日：2021-12-13

Applicant: VMWARE, INC.

Inventor： Piyush Parmar ,  Pawan Saxena ,  Gabriel Tarasuk-Levin ,  Dhaval Shah ,  Umesha Margi

IPC: G06F11/00 ,  G06F11/07 ,  G06F11/30 ,  G06F11/20

CPC classification number: G06F11/0772 ,  G06F11/2041 ,  G06F11/3006 ,  G06F11/3072

Abstract: System and computer-implemented method for managing multi-availability zone (AZ) clusters of host computers in a cloud computing environment automatically detects a degraded state of a first AZ in the cloud computing environment based on host failure events for host computers in a first cluster section of a multi-AZ cluster of host computers located in the first AZ and a recovered state of the first AZ based a successful scale-in operation of another multi-AZ cluster located partially in the first AZ. In response to the detection of the degraded state of the first AZ, a second cluster section of the multi-AZ cluster of host computers located in a second AZ is scaled out. In response to the detection of the recovered state of the first AZ, the second cluster section of the multi-AZ cluster of host computers located in the second AZ is scaled in.