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公开(公告)号：US10116510B2
公开(公告)日：2018-10-30
申请号：US14828446
申请日：2015-08-17
Applicant: VMware, Inc.
Inventor： Gregory T. Burk , Lachlan T. Coote
IPC: G06F17/30 , H04L12/24 , G06F15/16 , H04L12/813
Abstract: Some embodiments provide a method for managing a set of computing resources. The method imports descriptions of computing resources from several heterogeneous sources. The descriptions specify categories for the computing resources. The different sources use different types of categories for the resource descriptions. Based on the categories specified for the computing resources in the set, the method derives additional categories for at least a subset of the computing resources. The method stores each imported computing resource tagged according to its specified and derived categories, wherein the category tags are used for binding policies to the computing resources.
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公开(公告)号：US20170034075A1
公开(公告)日：2017-02-02
申请号：US14828441
申请日：2015-08-17
Applicant: VMware, Inc.
Inventor： Gregory T. Burk , Lachlan T. Coote , Yanislav Yankov , Alain Dumesny
IPC: H04L12/923 , H04L12/813
CPC classification number: H04L41/0893 , G06F15/161 , G06F17/30371 , G06F17/30598 , H04L41/08 , H04L41/20 , H04L47/20 , H04L47/762 , H04L47/827
Abstract: Some embodiments provide, for a policy framework that manages application of a plurality of policies to a plurality of resources in a computing environment, a method for providing a user interface. The method displays a first display area for viewing and editing policies imported by the policy framework from a first several heterogeneous sources. The method displays a second display area for viewing and editing information regarding computing resources imported by the policy framework from a second several heterogeneous sources. The method displays a third display area for viewing and editing binding rules for binding the policies to the computing resources.
Abstract translation: 一些实施例为管理计算环境中的多个资源的多个策略的应用的策略框架提供了提供用户界面的方法。 该方法显示第一个显示区域，用于查看和编辑策略框架从前几个异构源导入的策略。 该方法显示第二显示区域，用于从第二个多个异构源显示和编辑关于由策略框架导入的计算资源的信息。 该方法显示用于查看和编辑用于将策略绑定到计算资源的绑定规则的第三显示区域。
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公开(公告)号：US11037058B2
公开(公告)日：2021-06-15
申请号：US16518831
申请日：2019-07-22
Applicant: VMware, Inc.
Inventor： Dev Nag , Yanislav Yankov , Dongni Wang , Gregory T. Burk , Nicholas Mark Grant Stephen
Abstract: The current document is directed to transfer of training received by a first automated reinforcement-learning-based application manager while controlling a first application is transferred to a second automated reinforcement-learning-based application manager which controls a second application different from the first application. Transferable training provides a basis for automated generation of applications from application components. Transferable training is obtained from composition of applications from application components and composition of reinforcement-learning-based-control-and-learning constructs from reinforcement-learning-based-control-and-learning constructs of application components.
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公开(公告)号：US10922092B2
公开(公告)日：2021-02-16
申请号：US16518617
申请日：2019-07-22
Applicant: VMware, Inc.
Inventor： Dev Nag , Yanislav Yankov , Dongni Wang , Gregory T. Burk , Nicholas Mark Grant Stephen
Abstract: The current document is directed to an administrator-monitored reinforcement-learning-based application manager that can be deployed in various different computational environments to manage the computational environments with respect to one or more reward-specified goals. Certain control actions undertaken by the administrator-monitored reinforcement-learning-based application manager are first proposed, to one or more administrators or other users, who can accept or reject the proposed control actions prior to their execution. The reinforcement-learning-based application manager can therefore continue to explore the state/action space, but the exploration can be parametrically constrained as well as by human-administrator oversight and intervention.
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公开(公告)号：US20170032020A1
公开(公告)日：2017-02-02
申请号：US14828446
申请日：2015-08-17
Applicant: VMware, Inc.
Inventor： Gregory T. Burk , Lachlan T. Coote
IPC: G06F17/30 , H04L12/911
CPC classification number: H04L41/0893 , G06F15/161 , G06F17/30371 , G06F17/30598 , H04L41/08 , H04L41/20 , H04L47/20 , H04L47/762 , H04L47/827
Abstract: Some embodiments provide a method for managing a set of computing resources. The method imports descriptions of computing resources from several heterogeneous sources. The descriptions specify categories for the computing resources. The different sources use different types of categories for the resource descriptions. Based on the categories specified for the computing resources in the set, the method derives additional categories for at least a subset of the computing resources. The method stores each imported computing resource tagged according to its specified and derived categories, wherein the category tags are used for binding policies to the computing resources.
Abstract translation: 一些实施例提供了一种用于管理一组计算资源的方法。 该方法从多个异构源导入计算资源的描述。 这些描述指定计算资源的类别。 不同的来源为资源描述使用不同类型的类别。 基于为集合中的计算资源指定的类别，该方法为计算资源的至少一个子集导出其他类别。 该方法存储根据其指定和派生类别标记的每个导入的计算资源，其中类别标签用于将策略绑定到计算资源。
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Patent Agency Ranking
公开(公告)号：US11042640B2
公开(公告)日：2021-06-22
申请号：US16502587
申请日：2019-07-03
Applicant: VMware, Inc.
Inventor： Dev Nag , Gregory T. Burk , Yanislav Yankov , Nicholas Mark Grant Stephen , Dongni Wang
Abstract: The current document is directed to a safe-operation-constrained reinforcement-learning-based application manager that can be deployed in various different computational environments, without extensive manual modification and interface development, to manage the computational environments with respect to one or more reward-specified goals. Control actions undertaken by the safe-operation-constrained reinforcement-learning-based application manager are constrained, by stored action filters, to constrain state/action-space exploration by the safe-operation-constrained reinforcement-learning-based application manager to safe actions and thus prevent deleterious impact to the managed computational environment.
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公开(公告)号：US10977579B2
公开(公告)日：2021-04-13
申请号：US16518807
申请日：2019-07-22
Applicant: VMware, Inc.
Inventor： Dev Nag , Yanislav Yankov , Dongni Wang , Gregory T. Burk , Nicholas Mark Grant Stephen
Abstract: The current document is directed to automated reinforcement-learning-based application managers that that are trained using adversarial training. During adversarial training, potentially disadvantageous next actions are selected for issuance by an automated reinforcement-learning-based application manager at a lower frequency than selection of next actions, according to a policy that is learned to provide optimal or near-optimal control over a computing environment that includes one or more applications controlled by the automated reinforcement-learning-based application manager. By selecting disadvantageous actions, the automated reinforcement-learning-based application manager is forced to explore a much larger subset of the system-state space during training, so that, upon completion of training, the automated reinforcement-learning-based application manager has learned a more robust and complete optimal or near-optimal control policy than had the automated reinforcement-learning-based application manager been trained by simulators or using management actions and computing-environment responses recorded during previous controlled operation of a computing-environment.
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公开(公告)号：US10949263B2
公开(公告)日：2021-03-16
申请号：US16518717
申请日：2019-07-22
Applicant: VMware, Inc.
Inventor： Dev Nag , Yanislav Yankov , Dongni Wang , Gregory T. Burk , Nicholas Mark Grant Stephen
Abstract: The current document is directed to automated reinforcement-learning-based application managers that obtain increased computational efficiency by reusing learned models and by using human-management experience to truncate state and observation vectors. Learned models of managed environments that receive component-associated inputs can be partially or completely reused for similar environments. Human managers and administrators generally use only a subset of the available metrics in managing an application, and that subset can be used as an initial subset of metrics for learning an optimal or near-optimal control policy by an automated reinforcement-learning-based application manager.
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公开(公告)号：US10802864B2
公开(公告)日：2020-10-13
申请号：US16261253
申请日：2019-01-29
Applicant: VMware, Inc.
Inventor： Dev Nag , Gregory T. Burk , Janislav Jankov , Nick Stephen , Dongni Wang
Abstract: The current document is directed to a modular reinforcement-learning-based application manager that can be deployed in various different computational environments without extensive modification and interface development. The currently disclosed modular reinforcement-learning-based application manager interfaces to observation and action adapters and metadata that provide a uniform and, in certain implementations, self-describing external interface to the various different computational environments which the modular reinforcement-learning-based application manager may be operated to control. In addition, certain implementations of the currently disclosed modular reinforcement-learning-based application manager interface to a user-specifiable reward-generation interface to allow the rewards that provide feedback from the computational environment to the modular reinforcement-learning-based application manager to be tailored to meet a variety of different user expectations and desired control policies.
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公开(公告)号：US10263847B2
公开(公告)日：2019-04-16
申请号：US14828460
申请日：2015-08-17
Applicant: VMware, Inc.
Inventor： Gregory T. Burk , Lachlan T. Coote
IPC: G06F17/00 , H04L12/24 , G06F17/30 , G06F15/16 , H04L12/813
Abstract: Some embodiments provide method for managing a set of computing resources. The method receives information for a set of resources. The information for each resource indicates a set of policies bound to the resource. The policies as bound to the resources are for application by several policy engines. For each of several of the resources, the method determines whether the policies bound to the resource violate a set of policy validation rules. For a subset of the resources for which a violation exists, the method disables at least one of the policies from being applied to the resource by the several policy engines.
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