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公开(公告)号：US20200065704A1
公开(公告)日：2020-02-27
申请号：US16518845
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 methods and systems for simulation-based training of automated reinforcement-learning-based application managers. Simulators are generated from data collected from controlled computing environments controlled and may employ any of a variety of different machine-learning models to learn state-transition and reward models. The current disclosed methods and systems provide facilities for visualizing aspects of the models learned by a simulator and for initializing simulator models using domain information. In addition, the currently disclosed simulators employ weighted differences computed from simulator-generated and training-data state transitions for feedback to the machine-learning models to address various biases and deficiencies of commonly employed difference metrics in the context of training automated reinforcement-learning-based application managers.
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公开(公告)号：US20200065703A1
公开(公告)日：2020-02-27
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：US20200065156A1
公开(公告)日：2020-02-27
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：US10198467B2
公开(公告)日：2019-02-05
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: G06F15/173 , G06F17/30 , H04L12/24 , G06F15/16 , H04L12/813
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.
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公开(公告)号：US11238372B2
公开(公告)日：2022-02-01
申请号：US16518845
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 methods and systems for simulation-based training of automated reinforcement-learning-based application managers. Simulators are generated from data collected from controlled computing environments controlled and may employ any of a variety of different machine-learning models to learn state-transition and reward models. The current disclosed methods and systems provide facilities for visualizing aspects of the models learned by a simulator and for initializing simulator models using domain information. In addition, the currently disclosed simulators employ weighted differences computed from simulator-generated and training-data state transitions for feedback to the machine-learning models to address various biases and deficiencies of commonly employed difference metrics in the context of training automated reinforcement-learning-based application managers.
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Patent Agency Ranking
公开(公告)号：US10970649B2
公开(公告)日：2021-04-06
申请号：US16518785
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 use local agents. Local agents provide finer-granularity monitoring of an application or application subcomponents and provide continued application management in the event of interruption of network traffic between an automated reinforcement-learning-based application manager and the application or application subcomponents managed by the automated reinforcement-learning-based application manager.
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17.发明授权公开(公告)号：US10963313B2
公开(公告)日：2021-03-30
申请号：US16518763
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 learn and improve the reward function that steers reinforcement-learning-based systems towards optimal or near-optimal policies. Initially, when the automated reinforcement-learning-based application manager is first installed and launched, the automated reinforcement-learning-based application manager may rely on human-application-manager action inputs and resulting state/action trajectories to accumulate sufficient information to generate an initial reward function. During subsequent operation, when it is determined that the automated reinforcement-learning-based application manager is no longer following a policy consistent with the type of management desired by human application managers, the automated reinforcement-learning-based application manager may use accumulated trajectories to improve the reward function.
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公开(公告)号：US20200065702A1
公开(公告)日：2020-02-27
申请号：US16518785
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 use local agents. Local agents provide finer-granularity monitoring of an application or application subcomponents and provide continued application management in the event of interruption of network traffic between an automated reinforcement-learning-based application manager and the application or application subcomponents managed by the automated reinforcement-learning-based application manager.
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19.发明申请公开(公告)号：US20200065670A1
公开(公告)日：2020-02-27
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：US20200065495A1
公开(公告)日：2020-02-27
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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