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公开(公告)号：US20220318062A1
公开(公告)日：2022-10-06
申请号：US17337524
申请日：2021-06-03
Applicant: VMWARE, INC.
Inventor： PIYUSH PARMAR , Anant Agarwal , Vikram Nair , Aalap Desai , Rahul Chandrasekaran , Ravi Kant Cherukupalli
Abstract: A system and method for scaling resources of a secondary network for disaster recovery uses a disaster recovery notification from a primary resource manager of a primary network to a secondary resource manager of the secondary network to generate a scale-up recommendation for additional resources to the secondary network. The additional resources are based on latest resource demands of workloads on the primary network included in the disaster recovery notification. A scale-up operation for the additional resources is then executed based on the scale-up recommendation from the secondary resource manager to operate the secondary network with the additional resources to run the workloads on the secondary network.
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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.
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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：US11698819B2
公开(公告)日：2023-07-11
申请号：US17337524
申请日：2021-06-03
Applicant: VMWARE, INC.
Inventor： Piyush Parmar , Anant Agarwal , Vikram Nair , Aalap Desai , Rahul Chandrasekaran , Ravi kant Cherukupalli
CPC classification number: G06F9/505 , G06F9/5016 , G06F11/203 , G06F11/3414 , G06F11/3452
Abstract: A system and method for scaling resources of a secondary network for disaster recovery uses a disaster recovery notification from a primary resource manager of a primary network to a secondary resource manager of the secondary network to generate a scale-up recommendation for additional resources to the secondary network. The additional resources are based on latest resource demands of workloads on the primary network included in the disaster recovery notification. A scale-up operation for the additional resources is then executed based on the scale-up recommendation from the secondary resource manager to operate the secondary network with the additional resources to run the workloads on the secondary network.
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15.发明公开公开(公告)号：US20230176924A1
公开(公告)日：2023-06-08
申请号：US17542258
申请日：2021-12-03
Applicant: VMware, Inc.
Inventor： Amita Vasudev Kamat , Piyush Hasmukh Parmar , Aalap Desai , Keith Istvan Farkas
IPC: G06F9/50
CPC classification number: G06F9/5077 , G06F9/505 , G06F2209/503
Abstract: System and computer-implemented method for autoscaling clusters of host computers in a cloud-based computing environment uses an aggressive scale-in resource utilization threshold that is greater than a corresponding standard scale-in resource utilization threshold to search for any target clusters of host computers in response to a scale-out recommendation for a cluster of host computers to select a candidate cluster of host computers when the number of available reserved resource instance for the cloud-based computing environment is below a predefined value. A scale-in operation is executed on the candidate cluster of host computers to remove an existing resource instance from the candidate cluster of host computers. A scale-out operation is executed on the cluster of host computers using an available resource instance for the cloud-based computing environment.
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Patent Agency Ranking
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：US10430261B2
公开(公告)日：2019-10-01
申请号：US15677691
申请日：2017-08-15
Applicant: VMware, Inc.
Inventor： Keith Farkas , Kevin Scott Christopher , Aalap Desai , Manoj Krishnan , Jesse Andrew Mendonca , Rohan Patil
Abstract: The subject matter described herein is generally directed towards detection and remediation of virtual computing instance (VCI) failure on host devices. Monitoring is performed to detect suspected failures of different guest operating systems, identify failure information, and perform remediation to provide high availability for the VCI.
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公开(公告)号：US09411658B2
公开(公告)日：2016-08-09
申请号：US13772556
申请日：2013-02-21
Applicant: VMware, Inc.
Inventor： Aalap Desai , Chirag Bhatt
CPC classification number: G06F9/5083 , G06F9/5077
Abstract: Embodiments perform adaptive throttling of tasks into a virtual datacenter having dynamically changing resources. Tasks are processed concurrently in batches. The rate of change in throughput at different batch sizes is calculated. With each iteration, the batch size is increased or decreased based on the rate of change to achieve a maximum throughput for given resources and load on the virtual datacenter.
Abstract translation: 实施例将任务自适应地调节成具有动态变化的资源的虚拟数据中心。 批处理同时处理任务。 计算不同批量大小的吞吐量变化率。 通过每次迭代，基于变化率来增加或减少批量大小，以实现给定资源和虚拟数据中心上的负载的最大吞吐量。
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公开(公告)号：US20140237468A1
公开(公告)日：2014-08-21
申请号：US13772556
申请日：2013-02-21
Applicant: VMWARE, INC.
Inventor： Aalap Desai , Chirag Bhatt
IPC: G06F9/455
CPC classification number: G06F9/5083 , G06F9/5077
Abstract: Embodiments perform adaptive throttling of tasks into a virtual datacenter having dynamically changing resources. Tasks are processed concurrently in batches. The rate of change in throughput at different batch sizes is calculated. With each iteration, the batch size is increased or decreased based on the rate of change to achieve a maximum throughput for given resources and load on the virtual datacenter.
Abstract translation: 实施例将任务自适应地调节成具有动态变化的资源的虚拟数据中心。 批处理同时处理任务。 计算不同批量大小的吞吐量变化率。 通过每次迭代，基于变化率来增加或减少批量大小，以实现给定资源和虚拟数据中心上的负载的最大吞吐量。
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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
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.
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