-
公开(公告)号：US10075343B2
公开(公告)日：2018-09-11
申请号：US14828443
申请日：2015-08-17
Applicant: VMware, Inc.
Inventor： Gregory T. Burk , Lachlan T. Coote
IPC: H04L12/24 , G06F15/16 , H04L12/813
CPC classification number: H04L41/0893 , G06F15/161 , G06F16/2365 , G06F17/30598 , H04L41/08 , H04L41/20 , H04L47/20 , H04L47/762 , H04L47/827
Abstract: Some embodiments provide a method for managing policies for a set of computing resources. The method imports several sets of resource management policy rules from several heterogeneous sources. The method stores each set of imported policy rules as a primitive policy. The primitive policies are (i) applicable to resources in the set of computing resources and (ii) combinable into composite policies that are applicable to resources in the set of computing resources. Composite policies are combinable into additional composite policies with primitive policies and other composite policies.
-
公开(公告)号：US20170033996A1
公开(公告)日：2017-02-02
申请号：US14828443
申请日：2015-08-17
Applicant: VMware, Inc.
Inventor： Gregory T. Burk , Lachlan T. Coote
IPC: H04L12/24
CPC classification number: G06F17/30371 , G06F15/161 , G06F17/30598 , H04L41/08 , H04L41/0893 , H04L41/20 , H04L47/20 , H04L47/762 , H04L47/827
Abstract: Some embodiments provide a method for managing policies for a set of computing resources. The method imports several sets of resource management policy rules from several heterogeneous sources. The method stores each set of imported policy rules as a primitive policy. The primitive policies are (i) applicable to resources in the set of computing resources and (ii) combinable into composite policies that are applicable to resources in the set of computing resources. Composite policies are combinable into additional composite policies with primitive policies and other composite policies.
Abstract translation: 一些实施例提供了一种用于管理一组计算资源的策略的方法。 该方法从多个异构源中导入了几套资源管理策略规则。 该方法将每组导入的策略规则存储为原始策略。 原始策略是（i）适用于该组计算资源中的资源，（ii）可组合为适用于该组计算资源中的资源的复合策略。 组合策略可与具有原始策略和其他组合策略的其他组合策略组合。
-
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.
-
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.
-
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.
-
Patent Agency Ranking
公开(公告)号：US20200065128A1
公开(公告)日：2020-02-27
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.
-
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.
-
公开(公告)号：US20170033997A1
公开(公告)日：2017-02-02
申请号：US14828447
申请日：2015-08-17
Applicant: VMware, Inc.
Inventor： Gregory T. Burk , Lachlan T. Coote
IPC: H04L12/24
CPC classification number: H04L41/0893 , G06F15/161 , G06F16/2365 , 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 receives several computing resource management policies from a first several heterogeneous sources. The method receives descriptions of several computing resources from a second several heterogeneous sources, wherein the descriptions specify categories for the computing resources. The method uses a set of binding rules to automatically apply the policies to the computing resources. The binding rules specify to which categories of computing resource each policy is applied.
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。 该方法从前几个异构源接收多个计算资源管理策略。 该方法从第二个多个异构源接收几个计算资源的描述，其中描述指定计算资源的类别。 该方法使用一组绑定规则来自动将策略应用于计算资源。 绑定规则指定每个策略应用于哪些类别的计算资源。
-
公开(公告)号：US20170031970A1
公开(公告)日：2017-02-02
申请号：US14828460
申请日：2015-08-17
Applicant: VMware, Inc.
Inventor： Gregory T. Burk , Lachlan T. Coote
IPC: G06F17/30
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 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.
Abstract translation: 一些实施例提供了用于管理一组计算资源的方法。 该方法接收一组资源的信息。 每个资源的信息表示绑定到资源的一组策略。 与资源相关的策略是由几个策略引擎应用的。 对于几个资源中的每一个，该方法确定绑定到资源的策略是否违反一组策略验证规则。 对于存在冲突的资源的子集，该方法将至少一个策略禁止由多个策略引擎应用于资源。
-
公开(公告)号：US20170031956A1
公开(公告)日：2017-02-02
申请号：US14828455
申请日：2015-08-17
Applicant: VMware, Inc.
Inventor： Gregory T. Burk , Lachlan T. Coote
IPC: G06F17/30
CPC classification number: G06F17/30371 , G06F15/161 , G06F17/30598 , H04L41/08 , H04L41/0893 , H04L41/20 , H04L47/20 , H04L47/762 , H04L47/827
Abstract: Some embodiments provide, for a policy framework, a method for managing policies for a set of resources in a computing environment. The method stores several imported policy rules as primitive policies, each of which includes a policy data structure that includes a set of fields. One of the fields of each primitive policy stores the imported policy rule for the primitive policy. The method defines several composite policies based at least in part on the primitive policies. The method stores the defined composite policies as policy data structures. Each policy data structure for a composite policy includes a set of fields and references at least one additional policy data structure.
Abstract translation: 一些实施例为策略框架提供了一种用于管理计算环境中的一组资源的策略的方法。 该方法将多个导入的策略规则存储为原始策略，每个策略包括包含一组字段的策略数据结构。 每个原始策略的一个字段存储原始策略的导入策略规则。 该方法至少部分地基于原始策略来定义几个复合策略。 该方法将定义的组合策略存储为策略数据结构。 组合策略的每个策略数据结构包括一组字段和引用，至少一个额外的策略数据结构。
-
-
-
-
-
-
-
-
-
Search Results
31
records
(took 0.028 seconds)
