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    Safe-operation-constrained reinforcement-learning-based application manager
    1.
    发明授权
    Safe-operation-constrained reinforcement-learning-based application manager 有权

    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

    IPC: G06F21/57 ,  G06N20/00

    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.

    Adversarial automated reinforcement-learning-based application-manager training
    2.
    发明授权
    Adversarial automated reinforcement-learning-based application-manager training 有权

    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

    IPC: G06N20/00 ,  G06F9/54 ,  G06N7/00

    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.

    Computationally efficient reinforcement-learning-based application manager
    3.
    发明授权
    Computationally efficient reinforcement-learning-based application manager 有权

    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

    IPC: G06F9/50 ,  G06N20/00 ,  G06N3/02

    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.

    Data-agnostic adjustment of hard thresholds based on user feedback
    4.
    发明授权
    Data-agnostic adjustment of hard thresholds based on user feedback 有权

    公开(公告)号：US10467119B2

    公开(公告)日：2019-11-05

    申请号：US15479182

    申请日：2017-04-04

    Applicant: VMware, Inc.

    Inventor： Naira Movses Grigoryan ,  Mazda A. Marvasti ,  Arnak Poghosyan ,  Ashot Nshan Harutyunyan ,  Yanislav Yankov

    IPC: G06F11/34 ,  G06F11/07 ,  G06F11/32

    Abstract: This disclosure is directed to data-agnostic computational methods and systems for adjusting hard thresholds based on user feedback. Hard thresholds are used to monitor time-series data generated by a data-generating entity. The time-series data may be metric data that represents usage of the data-generating entity over time. The data is compared with a hard threshold associated with usage of the resource or process and when the data violates the threshold, an alert is typically generated and presented to a user. Methods and systems collect user feedback after a number of alerts to determine the quality and significance of the alerts. Based on the user feedback, methods and systems automatically adjust the hard thresholds to better represent how the user perceives the alerts.

    Policy Framework User Interface
    5.
    发明申请
    Policy Framework User Interface 审中-公开
    策略框架用户界面

    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。

    Data-agnostic methods and systems for ranking and updating beliefs
    6.
    发明授权
    Data-agnostic methods and systems for ranking and updating beliefs 有权
    数据无关的方法和系统，用于排名和更新信念

    公开(公告)号：US09466031B1

    公开(公告)日：2016-10-11

    申请号：US14104351

    申请日：2013-12-12

    Applicant: VMware, Inc.

    Inventor： Ashot Nshan Harutyunyan ,  Naira Movses Grigoryan ,  Mazda A. Marvasti ,  Arnak Poghosyan ,  Yanislav Yankov

    IPC: G06N99/00 ,  G06N5/02

    CPC classification number: G06N99/005 ,  G06N5/02 ,  G06N7/005 ,  G06Q30/00

    Abstract: This disclosure is directed to computational, closed-loop user feedback systems and methods for ranking or updating beliefs for a user based on user feedback. The systems and methods are based on a data-agnostic user feedback formulation that uses user feedback to automatically rank beliefs for a user or update the beliefs. The methods and systems are based on a general statistical inference model, which, in turn, is based on an assumption of convergence in user opinion. The closed-loop user feedback methods and systems may be used to rank or update beliefs prior to inputting the beliefs to a recommender engine. As a result, the recommender engine is expected to be more responsive to customer environments and efficient at deployment and reducing the level of unnecessary user recommendations.

    Abstract translation: 本公开涉及用于基于用户反馈来对用户的信念进行排名或更新的计算，闭环用户反馈系统和方法。 系统和方法基于数​​据无关的用户反馈公式，其使用用户反馈自动对用户的信念进行排名或更新信念。 方法和系统基于一般的统计推理模型，反过来，它是基于用户意见收敛的假设。 闭环用户反馈方法和系统可以在将信念输入推荐者引擎之前对信念进行排名或更新。 因此，预计推荐者引擎将更能响应客户环境并且在部署时效率高，并降低不必要的用户建议的水平。

    Administrator-monitored reinforcement-learning-based application manager
    8.
    发明授权
    Administrator-monitored reinforcement-learning-based application manager 有权

    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

    IPC: G06F9/445 ,  G06F17/16 ,  G06F9/48 ,  G06N20/00

    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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