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公开(公告)号：US20120203912A1
公开(公告)日：2012-08-09
申请号：US13450789
申请日：2012-04-19
申请人： David Michael Chess , Rajashi Das , James Edwin Hanson , Alla Segal , Gerald James Tesauro , Ian Nicholas Whalley
发明人： David Michael Chess , Rajashi Das , James Edwin Hanson , Alla Segal , Gerald James Tesauro , Ian Nicholas Whalley
IPC分类号： G06G7/48 , G06F15/173
CPC分类号： H04L41/145
摘要： Methods and systems are provided for autonomic control and optimization of computing systems. A plurality of component models for one or more components in an autonomic computing system are maintained in a system level database. These component models are obtained from a source external to the management server including the components associated with the models. Component models are added or removed from the database or updated as need. A system level management server in communication with the database utilizes the component models maintained in the system level database and generic component models as needed to compute an optimum state of the autonomic computing system. The autonomic computing system is managed in accordance with the computed optimum state.
摘要翻译： 提供了方法和系统用于计算系统的自主控制和优化。 在自动计算系统中的一个或多个组件的多个组件模型被保持在系统级数据库中。 这些组件模型从管理服务器外部的源获得，包括与模型相关联的组件。 组件模型从数据库中添加或删除，或根据需要进行更新。 与数据库通信的系统级管理服务器根据需要利用维护在系统级数据库和通用组件模型中的组件模型来计算自主计算系统的最佳状态。 根据计算出的最优状态管理自主计算系统。
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公开(公告)号：US08214474B2
公开(公告)日：2012-07-03
申请号：US11406019
申请日：2006-04-18
申请人： David Michael Chess , Rajashi Das , James Edwin Hanson , Alla Segal , Gerald James Tesauro , Ian Nicholas Whalley
发明人： David Michael Chess , Rajashi Das , James Edwin Hanson , Alla Segal , Gerald James Tesauro , Ian Nicholas Whalley
IPC分类号： G06G7/48 , G06F9/45 , G06F15/173
CPC分类号： H04L41/145
摘要： Methods and systems are provided for autonomic control and optimization of computing systems. A plurality of component models for one or more components in an autonomic computing system are maintained in a system level database. These component models are obtained from a source external to the management server including the components associated with the models. Component models are added or removed from the database or updated as need. A system level management server in communication with the database utilizes the component models maintained in the system level database and generic component models as needed to compute an optimum state of the autonomic computing system. The autonomic computing system is managed in accordance with the computed optimum state.
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公开(公告)号：US08554898B2
公开(公告)日：2013-10-08
申请号：US13450789
申请日：2012-04-19
申请人： David Michael Chess , Rajashi Das , James Edwin Hanson , Alla Segal , Gerald James Tesauro , Ian Nicholas Whalley
发明人： David Michael Chess , Rajashi Das , James Edwin Hanson , Alla Segal , Gerald James Tesauro , Ian Nicholas Whalley
IPC分类号： G06G7/48
CPC分类号： H04L41/145
摘要： Methods and systems are provided for autonomic control and optimization of computing systems. A plurality of component models for one or more components in an autonomic computing system are maintained in a system level database. These component models are obtained from a source external to the management server including the components associated with the models. Component models are added or removed from the database or updated as need. A system level management server in communication with the database utilizes the component models maintained in the system level database and generic component models as needed to compute an optimum state of the autonomic computing system. The autonomic computing system is managed in accordance with the computed optimum state.
摘要翻译： 提供了方法和系统用于计算系统的自主控制和优化。 在自动计算系统中的一个或多个组件的多个组件模型被保持在系统级数据库中。 这些组件模型从管理服务器外部的源获得，包括与模型相关联的组件。 组件模型从数据库中添加或删除，或根据需要进行更新。 与数据库通信的系统级管理服务器根据需要利用维护在系统级数据库和通用组件模型中的组件模型来计算自主计算系统的最佳状态。 根据计算出的最优状态管理自主计算系统。
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4.发明授权Method and apparatus for utility-based dynamic resource allocation in a distributed computing system 失效在分布式计算系统中基于实用程序的动态资源分配的方法和装置公开(公告)号：US08352951B2
公开(公告)日：2013-01-08
申请号：US12164896
申请日：2008-06-30
IPC分类号： G06F9/46 , G06F15/173
CPC分类号： G06F9/5027 , G06F9/5083
摘要： In one embodiment, the present invention is a method for allocation of finite computational resources amongst multiple entities, wherein the method is structured to optimize the business value of an enterprise providing computational services. One embodiment of the inventive method involves establishing, for each entity, a service level utility indicative of how much business value is obtained for a given level of computational system performance. The service-level utility for each entity is transformed into a corresponding resource-level utility indicative of how much business value may be obtained for a given set or amount of resources allocated to the entity. The resource-level utilities for each entity are aggregated, and new resource allocations are determined and executed based upon the resource-level utility information. The invention is thereby capable of making rapid allocation decisions, according to time-varying need or value of the resources by each of the entities.
摘要翻译： 在一个实施例中，本发明是一种用于在多个实体之间分配有限计算资源的方法，其中该方法被构造为优化提供计算服务的企业的商业价值。 本发明方法的一个实施例涉及为每个实体建立一个服务级别实用程序，其指示针对给定级别的计算系统性能获得多少商业价值。 每个实体的服务级别实用程序被转换成相应的资源级实用程序，指示可以为给定的集合或分配给该实体的资源量获得多少商业价值。 聚合每个实体的资源级实用程序，并根据资源级实用程序信息确定和执行新的资源分配。 因此，本发明能够根据每个实体的时间变化需要或资源价值进行快速分配决定。
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5.发明授权Active sampling collaborative prediction method for end-to-end performance prediction 有权端到端性能预测的主动采样协同预测方法公开(公告)号：US07640224B2
公开(公告)日：2009-12-29
申请号：US11691251
申请日：2007-03-26
申请人： Irina Rish , Gerald James Tesauro
发明人： Irina Rish , Gerald James Tesauro
CPC分类号： G06N5/003
摘要： Active sample collaborative prediction method, system and program storage device are provided. A method in one aspect may include determining approximation X for matrix Y using collaborative prediction, said matrix Y being sparse initially and representing pairwise measurement values; selecting one or more unobserved entries from said matrix Y representing active samples using said approximation X and an active sample heuristic; obtaining values associated with said unobserved entries; inserting said values to said matrix Y; and repeating the steps of determining, selecting, obtaining and inserting until a predetermined condition is satisfied.
摘要翻译： 提供了主动样本协同预测方法，系统和程序存储设备。 一方面的方法可以包括使用协同预测来确定矩阵Y的近似X，所述矩阵Y最初稀疏并且表示成对测量值; 使用所述近似X和活动样本启发式从表示活动样本的所述矩阵Y中选择一个或多个未观察到的条目; 获得与所述未观察到的条目相关联的值; 将所述值插入到所述矩阵Y中; 并重复确定，选择，获取和插入的步骤，直到满足预定条件。
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公开(公告)号：US07890952B2
公开(公告)日：2011-02-15
申请号：US10960572
申请日：2004-10-07
申请人： Neal Richard Marion , Shawn Patrick Mullen , George Francis Ramsay, III , Gerald James Tesauro , James Stanley Tesauro
发明人： Neal Richard Marion , Shawn Patrick Mullen , George Francis Ramsay, III , Gerald James Tesauro , James Stanley Tesauro
IPC分类号： G06F9/445
CPC分类号： G06F9/5011 , G06F8/61 , G06F11/36
摘要： Methods, systems, and products are provided for peer-to-peer computer software installation. Embodiments include receiving, by an observing install agent running on an observing host from a test install agent running on a test host, performance information describing the performance of software installed on the test host; determining, by the observing install agent, whether the performance information meets performance criteria for the observing host; and if the performance information meets the performance criteria for the observing host, installing the software on the observing host. In some embodiments, determining, by the observing install agent, whether the performance information meets performance criteria for the observing host is carried out by determining, whether the performance information meets performance criteria for the observing host in dependence upon a rule.
摘要翻译： 为对等计算机软件安装提供了方法，系统和产品。 实施例包括：从在测试主机上运行的测试安装代理在观察主机上运行的观察安装代理接收描述安装在测试主机上的软件的性能的性能信息; 由观察安装代理确定性能信息是否符合观测主机的性能标准; 并且如果性能信息符合观测主机的性能标准，则在观察主机上安装该软件。 在一些实施例中，通过观察安装代理确定性能信息是否符合观测主机的性能标准，通过确定性能信息是否符合观测主机的规则来执行。
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7.发明申请METHOD AND APPARATUS FOR UTILITY-BASED DYNAMIC RESOURCE ALLOCATION IN A DISTRIBUTED COMPUTING SYSTEM 失效分布式计算系统中基于应用的动态资源分配的方法与装置公开(公告)号：US20080263559A1
公开(公告)日：2008-10-23
申请号：US12164896
申请日：2008-06-30
IPC分类号： G06F9/50
CPC分类号： G06F9/5027 , G06F9/5083
摘要： In one embodiment, the present invention is a method for allocation of finite computational resources amongst multiple entities, wherein the method is structured to optimize the business value of an enterprise providing computational services. One embodiment of the inventive method involves establishing, for each entity, a service level utility indicative of how much business value is obtained for a given level of computational system performance. The service-level utility for each entity is transformed into a corresponding resource-level utility indicative of how much business value may be obtained for a given set or amount of resources allocated to the entity. The resource-level utilities for each entity are aggregated, and new resource allocations are determined and executed based upon the resource-level utility information. The invention is thereby capable of making rapid allocation decisions, according to time-varying need or value of the resources by each of the entities.
摘要翻译： 在一个实施例中，本发明是一种用于在多个实体之间分配有限计算资源的方法，其中该方法被构造为优化提供计算服务的企业的商业价值。 本发明方法的一个实施例涉及为每个实体建立一个服务级别实用程序，其指示针对给定级别的计算系统性能获得多少商业价值。 每个实体的服务级别实用程序被转换成相应的资源级实用程序，指示可以为给定的集合或分配给该实体的资源量获得多少商业价值。 聚合每个实体的资源级实用程序，并根据资源级实用程序信息确定和执行新的资源分配。 因此，本发明能够根据每个实体的时间变化需要或资源价值进行快速分配决定。
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8.发明授权Method and apparatus for reward-based learning of improved policies for management of a plurality of application environments supported by a data processing system 失效用于基于奖励学习的改进策略的方法和装置，用于管理由数据处理系统支持的多个应用环境公开(公告)号：US08001063B2
公开(公告)日：2011-08-16
申请号：US12165144
申请日：2008-06-30
IPC分类号： G06F15/18
CPC分类号： G06Q10/06
摘要： In one embodiment, the present invention is a method for reward-based learning of improved systems management policies. One embodiment of the inventive method involves obtaining a decision-making entity and a reward mechanism. The decision-making entity manages a plurality of application environments supported by a data processing system, where each application environment operates on data input to the data processing system. The reward mechanism generates numerical measures of value responsive to actions performed in states of the application environments. The decision-making entity and the reward mechanism are applied to the application environments, and results achieved through this application are processed in accordance with reward-based learning to derive a policy. The reward mechanism and the policy are then applied to the application environments, and the results of this application are processed in accordance with reward-based learning to derive a new policy.
摘要翻译： 在一个实施例中，本发明是改进的系统管理策略的基于奖励学习的方法。 本发明方法的一个实施例涉及获得决策实体和奖励机制。 决策实体管理由数据处理系统支持的多个应用环境，其中每个应用环境对输入到数据处理系统的数据进行操作。 奖励机制响应于在应用环境的状态中执行的动作产生数值的值测量。 决策实体和奖励机制适用于应用环境，通过本申请实现的结果根据奖励学习进行处理，得出政策。 然后将奖励机制和政策应用于应用环境，并根据奖励学习处理本应用的结果，以得出新的策略。
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公开(公告)号：US5907834A
公开(公告)日：1999-05-25
申请号：US619866
申请日：1996-03-18
CPC分类号： G06F21/564
摘要： A data string is a sequence of atomic units of data that represent information. In the context of computer data, examples of data strings include executable programs, data files, and boot records consisting of sequences of bytes, or text files consisting of sequences of bytes or characters. The invention solves the problem of automatically constructing a classifier of data strings, i.e., constructing a classifier which, given a string, determines which of two or more class labels should be assigned to it. From a set of (string, class-label) pairs, this invention provides an automated technique for extracting features of data strings that are relevant to the classification decision, and an automated technique for developing a classifier which uses those features to classify correctly the data strings in the original examples and, with high accuracy, classify correctly novel data strings not contained in the example set. The classifier is developed using "adaptive" or "learning" techniques from the domain of statistical regression and classification, such as, e.g., multi-layer neural networks. As an example, the technique can be applied to the task of distinguishing files or boot records that are infected by computer viruses from files or boot records that are not infected.
摘要翻译： 数据串是表示信息的数据的原子单元的序列。 在计算机数据的上下文中，数据串的示例包括由字节序列组成的可执行程序，数据文件和引导记录，或由字节或字符序列组成的文本文件。 本发明解决了自动构建数据串分类器的问题，即，构建一个分类器，给定一个字符串，确定应该分配两个或多个类标签中的哪一个。 本发明从一组（串，类标签）对提供了一种用于提取与分类决定相关的数据串的特征的自动化技术，以及用于开发分类器的自动化技术，其使用这些特征来正确地分类数据 原始示例中的字符串，并且具有高精度，正确地分类示例集中未包含的新颖数据字符串。 分类器是使用“自适应”或“学习”技术从统计回归和分类领域开发的，例如多层神经网络。 例如，该技术可以应用于区分受计算机病毒感染的文件或引导记录的文件或未被感染的引导记录的任务。
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10.发明申请ACTIVE SAMPLING COLLABORATIVE PREDICTION METHOD FOR END-TO-END PERFORMANCE PREDICTION 有权用于端到端性能预测的主动采样协同预测方法公开(公告)号：US20080243735A1
公开(公告)日：2008-10-02
申请号：US11691251
申请日：2007-03-26
申请人： Irina Rish , Gerald James Tesauro
发明人： Irina Rish , Gerald James Tesauro
IPC分类号： G06E1/00
CPC分类号： G06N5/003
摘要： Active sample collaborative prediction method, system and program storage device are provided. A method in one aspect may include determining approximation X for matrix Y using collaborative prediction, said matrix Y being sparse initially and representing pairwise measurement values; selecting one or more unobserved entries from said matrix Y representing active samples using said approximation X and an active sample heuristic; obtaining values associated with said unobserved entries; inserting said values to said matrix Y; and repeating the steps of determining, selecting, obtaining and inserting until a predetermined condition is satisfied.
摘要翻译： 提供了主动样本协同预测方法，系统和程序存储设备。 一方面的方法可以包括使用协同预测来确定矩阵Y的近似X，所述矩阵Y最初稀疏并且表示成对测量值; 使用所述近似X和活动样本启发式从表示活动样本的所述矩阵Y中选择一个或多个未观察到的条目; 获得与所述未观察到的条目相关联的值; 将所述值插入到所述矩阵Y中; 并重复确定，选择，获取和插入的步骤，直到满足预定条件。
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