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1.发明公开公开(公告)号：US20230177345A1
公开(公告)日：2023-06-08
申请号：US17545403
申请日：2021-12-08
Applicant: VMware, Inc.
Inventor： Marius Vilcu , Dongni Wang , Asmitha Rathis , Greg Burk
Abstract: The current document is directed to methods and systems that determine workload characteristics of computational entities from stored data and that evaluate deployment/configuration policies in order to facilitate deploying, launching, and controlling distributed applications, distributed-application components, and other computational entities within distributed computer systems. Deployment/configuration policies are powerful tools for assisting managers and administrators of distributed applications and distributed computer systems, but constructing deployment/configuration policies and, in particular, evaluating the relative effectiveness of deployment/configuration policies in increasingly complex distributed-computer-system environments may be difficult or practically infeasible for many administrators and managers and may be associated with undesirable or intolerable levels of risk. The currently disclosed machine-learning-based deployment/configuration-policy evaluation methods and systems represent a significant improvement to policy-based management and control that address both of these problems.
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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：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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Patent Agency Ranking
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：US20240419457A1
公开(公告)日：2024-12-19
申请号：US18210032
申请日：2023-06-14
Applicant: VMware, Inc.
Inventor： Dongni Wang , Aiswaryaa Venugopalan , Arnav Chakravarthy , Marius Vilcu , Asmitha Rathis , Greg Burk
Abstract: The disclosure provides a method for determining a target configuration for a container-based cluster. The method generally includes determining, by a virtualization management platform configured to manage components of the cluster, a current state of the cluster, determining, by the virtualization management platform, at least one of performance metrics or resource utilization metrics for the cluster based on the current state of the cluster, processing, with a model configured to generate candidate configurations recommended for the cluster, the current state and at least one of the performance metrics or the resource utilization metrics and thereby generate the candidate configurations, calculating a reward score for each of the candidate configurations, selecting the target configuration as a candidate configuration from the candidate configurations based on the reward score of the target configuration, and adjusting configuration settings for the cluster based on the target configuration to alter the current state of the cluster.
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9.发明公开公开(公告)号：US20230161635A1
公开(公告)日：2023-05-25
申请号：US17532876
申请日：2021-11-22
Applicant: VMware, Inc.
Inventor： Marius VILCU , Dongni Wang , Asmitha Rathis , Greg Burk
CPC classification number: G06F9/5055 , G06K9/6262 , G06N3/0454
Abstract: The current document is directed to a reinforcement-learning-based application manager that controls the operation of one or more applications and that employs transfer learning to improve initialization and operation of the reinforcement-learning-based application manager and to improve operation of the one or more distributed computer systems that host the applications controlled by the reinforcement-learning-based application manager. Transfer learning, in the disclosed implementations, is achieved by logically decomposing machine-learning-based function approximators for reinforcement-learning functions into component-specific function approximators, storing pre-trained function approximators and pre-trained component-specific function approximators, and initializing function approximators for reinforcement-learning-based application managers using the stored pre-trained function approximators and pre-trained component-specific function approximators.
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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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