-
公开(公告)号：US09753826B2
公开(公告)日：2017-09-05
申请号：US14804528
申请日：2015-07-21
Applicant: International Business Machines Corporation
Inventor： Yu Deng , Ruchi Mahindru , Anca Sailer , Soumitra Sarkar , Long Wang
IPC: G06F11/263 , G06F11/34
CPC classification number: G06F11/263 , G06F11/3414
Abstract: Methods, systems, and computer program products for providing fault injection to Cloud-provisioned machines are provided herein. A method includes determining one or more fault conditions to be associated with a fault injection implementation based on one or more parameters associated with a request for the fault injection implementation; generating a specification for a lifecycle of the fault injection implementation based on the one or more fault conditions; and executing the fault injection implementation in a target system, wherein said executing comprises effecting the lifecycle of the fault injection implementation according to the generated specification.
-
公开(公告)号：US09667510B2
公开(公告)日：2017-05-30
申请号：US14627596
申请日：2015-02-20
Applicant: International Business Machines Corporation
Inventor： Yu Deng , Murthy V. Devarakonda , Michael Reuben Head , Rafah A. Hosn , Andrzej Kochut , Jonathan Paul Munson , Hidayatullah Habeebullah Shaikh
CPC classification number: H04L47/82 , G06F9/5011 , G06F9/5072 , G06F17/30893 , G06Q10/06 , H04L29/06 , H04L41/5054
Abstract: Techniques are disclosed for integration, provisioning and management of entities and processes in a computing system such as, by way of example only, business entities and business processes. In particular, techniques are disclosed for implementing an extensible support system for multiple service offerings. For example, such a support system can be a business support system which may be employed in conjunction with a cloud computing environment.
-
公开(公告)号：US20170103212A1
公开(公告)日：2017-04-13
申请号：US15385009
申请日：2016-12-20
Applicant: International Business Machines Corporation
Inventor： Yu Deng , Ruchi Mahindru , HariGovind V. Ramasamy , Lakshminarayanan Renganarayana , Soumitra Sarkar , Long Wang
CPC classification number: G06F21/577 , G06F3/0619 , G06F3/065 , G06F3/0685 , G06F9/45558 , G06F2009/45562 , G06F2009/45575 , G06F2009/45587 , G06F2221/034
Abstract: Determining which snapshot deltas tend to occur in: (i) healthy virtual machines (VMs) that have been subject to an attack yet remained healthy, and/or (ii) unhealthy VMs that have apparently been adversely affected by an attack. Snapshot deltas that occur in at least some (or more preferably all) of the healthy VM subset provide information about software changes (for example, updates, configuration changes) that may be helpful. Snapshot deltas that occur in at least some (or more preferably all) of the unhealthy VM subsets provide information about software changes (for example, updates, configuration changes) that may be unhelpful.
-
公开(公告)号：US09589229B2
公开(公告)日：2017-03-07
申请号：US14634146
申请日：2015-02-27
Applicant: International Business Machines Corporation
Inventor： Yu Deng , Rafah A. Hosn , Ruchi Mahindru , Harigovind V. Ramasamy , Soumitra Sarkar
Abstract: Techniques for model-based analysis of a data center. A method includes creating a metamodel based on domain knowledge to represent a type of object and/or relationship of a data center, using static and dynamic configuration and data analysis techniques to discover topology of elements of the data center and represent the topology as a model that is an instance of the metamodel, extending the metamodel, using the model to perform analysis of the data center in connection with a specified task, leveraging domain knowledge represented in nodes of the extended metamodel to guide the analysis in terms of determining guidelines to apply to each node and determining relationships to traverse to continue the analysis, extending the domain knowledge by updating the extended metamodel upon discovery of additional knowledge for use in improving analysis tasks, and extending the model on-demand using dynamic analysis techniques upon detection of multiple analysis failures.
Abstract translation: 数据中心模型分析技术。 一种方法包括使用静态和动态配置以及数据分析技术来创建基于领域知识的元模型来表示数据中心的类型和/或关系，以发现数据中心的元素的拓扑并将拓扑表示为模型 这是元模型的一个实例，扩展了元模型，使用该模型来执行与指定任务相关的数据中心分析，利用扩展元模型节点中所代表的领域知识来指导分析，以确定适用的准则 到每个节点并确定关系以遍历以继续分析，通过在发现用于改进分析任务的附加知识的更新扩展元模型来扩展域知识，并且在检测到多个分析时使用动态分析技术来扩展模型 失败。
-
35.发明授权公开(公告)号：US09563777B2
公开(公告)日：2017-02-07
申请号：US14699251
申请日：2015-04-29
Applicant: International Business Machines Corporation
Inventor： Yu Deng , Ruchi Mahindru , HariGovind V. Ramasamy , Lakshminarayanan Renganarayana , Soumitra Sarkar , Long Wang
CPC classification number: G06F21/577 , G06F3/0619 , G06F3/065 , G06F3/0685 , G06F9/45558 , G06F2009/45562 , G06F2009/45575 , G06F2009/45587 , G06F2221/034
Abstract: Determining which snapshot deltas tend to occur in: (i) healthy virtual machines (VMs) that have been subject to an attack yet remained healthy, and/or (ii) unhealthy VMs that have apparently been adversely affected by an attack. Snapshot deltas that occur in at least some (or more preferably all) of the healthy VM subset provide information about software changes (for example, updates, configuration changes) that may be helpful. Snapshot deltas that occur in at least some (or more preferably all) of the unhealthy VM subsets provide information about software changes (for example, updates, configuration changes) that may be unhelpful.
Abstract translation: 确定哪些快照增量趋于发生在：（i）已经受到攻击的健康虚拟机（VM）保持健康，和/或（ii）显然受到攻击不利影响的不健康的虚拟机。 发生在健康VM子集的至少一些（或更优选全部））的快照增量提供关于可能有帮助的软件改变（例如，更新，配置改变）的信息。 发生在至少一些（或更优选全部）不健康的VM子集中的快照三角形提供关于可能无益的软件改变（例如，更新，配置改变）的信息。
-
Patent Agency Ranking
公开(公告)号：US20160277269A1
公开(公告)日：2016-09-22
申请号：US14662479
申请日：2015-03-19
Applicant: International Business Machines Corporation
Inventor： Al Chakra , Christopher J. Dawson , Yu Deng , Rick A. Hamilton, II , Jenny S. Li , Liangzhao Zeng
CPC classification number: H04L41/0853 , G06F9/453
Abstract: An approach for assisting a user with a service request is provided. In the approach, it is determined that an error has occurred on a computing device of a user. A processor identifies information about the computing device. A processor determines that the computing device corresponds to a group of one or more additional computing devices, within a predetermined threshold, based on at least, on the information about the computing device. A processor causes the user to enter into a communication with at least a second user of a second computing device, wherein the second computing device is one of the group of one or more additional computing devices.
Abstract translation: 提供了一种帮助用户提供服务请求的方法。 在该方法中，确定在用户的计算设备上发生错误。 处理器识别关于计算设备的信息。 至少基于关于计算设备的信息，处理器确定计算设备在预定阈值内对应于一个或多个附加计算设备的组。 处理器使得用户进入与第二计算设备的至少第二用户的通信，其中第二计算设备是一个或多个附加计算设备的组之一。
-
37.发明申请INFORMATION THEORY BASED RESULT MERGING FOR SEARCHING HIERARCHICAL ENTITIES ACROSS HETEROGENEOUS DATA SOURCES 审中-公开基于异构数据源搜索分层实体的信息理论结果合并公开(公告)号：US20160147770A1
公开(公告)日：2016-05-26
申请号：US15012415
申请日：2016-02-01
Applicant: International Business Machines Corporation
Inventor： Yu Deng , Murthy V. Devarakonda , Rafah A. Hosn , Ruchi Mahindru , Nithya Rajamani , Soumitra Sarkar
IPC: G06F17/30
Abstract: A method, system, and computer program product are disclosed for merging search results. In one embodiment, the method comprises identifying a query, splitting the query into sub-queries, and calculating information content for each of the sub-queries. This method also comprises executing each of the sub-queries to obtain a plurality of search results, and combining the search results based on the information content calculated for the sub-queries. In an embodiment, the execution of each of the sub-queries includes identifying a multitude of search results for at least one of the sub-queries; and the combining includes grouping said multitude of search results into a plurality of clusters, and computing a relevance score for each of said clusters. In the embodiment the combining further includes merging the clusters based on the relevance scores computed for the clusters as well as the information content calculated for the sub-queries.
Abstract translation: 公开了一种用于合并搜索结果的方法，系统和计算机程序产品。 在一个实施例中，该方法包括识别查询，将查询分割成子查询，以及计算每个子查询的信息内容。 该方法还包括执行每个子查询以获得多个搜索结果，并且基于为子查询计算的信息内容来组合搜索结果。 在一个实施例中，每个子查询的执行包括识别至少一个子查询的大量搜索结果; 并且所述组合包括将所述多个搜索结果分组为多个群集，以及计算每个所述群集的相关性得分。 在该实施例中，组合还包括基于针对集群计算的相关性分数以及为子查询计算的信息内容来合并集群。
-
公开(公告)号：US20150264076A1
公开(公告)日：2015-09-17
申请号：US14727906
申请日：2015-06-02
Applicant: International Business Machines Corporation
Inventor： Al Chakra , Christopher J. Dawson , Yu Deng , Rick A. Hamilton, II , Jenny S. Li , Liangzhao Zeng
CPC classification number: H04L63/1433 , G06F21/55 , G06F21/577 , H04L63/02
Abstract: Embodiments of the present invention provide an approach to repair vulnerabilities (e.g., security vulnerabilities) in images (e.g., application images) in a networked computing environment (e.g., a cloud computing environment). Specifically, an image is checked for vulnerabilities using a database of known images and/or vulnerabilities. If a vulnerability is found, a flexible/elastic firewall is established around the image so as to isolate the vulnerability. Once the firewall has been put in place, the vulnerability can be repaired by a variety of means such as upgrading the image, quarantining the image, discarding the image, and/or generating a new image. Once the image has been repaired, the firewall can be removed.
Abstract translation: 本发明的实施例提供了在网络计算环境（例如，云计算环境）中修复图像（例如，应用图像）中的漏洞（例如，安全漏洞）的方法。 具体来说，使用已知图像和/或漏洞的数据库检查图像的漏洞。 如果发现一个漏洞，则会围绕图像建立一个灵活/有弹性的防火墙，从而隔离漏洞。 一旦防火墙已经到位，该漏洞可以通过各种手段进行修复，例如升级图像，隔离图像，丢弃图像和/或生成新图像。 一旦图像被修复，防火墙可以被删除。
-
39.发明授权公开(公告)号：US09077613B2
公开(公告)日：2015-07-07
申请号：US13859956
申请日：2013-04-10
Applicant: International Business Machines Corporation
Inventor： Yu Deng , Alexei A. Karve , Andrzej Kochut , Randy A. Rendahl , Anca Sailer , Hidayatullah Shaikh , Angel E. Tomala-Reyes
CPC classification number: H04L41/0604 , G06F9/45533 , G06F11/008 , H04L41/0663 , H04L41/12 , H04L41/5019 , H04L67/1095
Abstract: A method for enabling resiliency for cloud computing systems is described. The method includes modifying a topology graph of a network architecture by mapping processes flows onto the topology graph. A resiliency graph is created based on the modified topology graph. The method includes modifying the resiliency graph by translating at least one SLA into the resiliency graph. Overlaps and dependencies in the modified resiliency graph are identified. Apparatus and computer readable instructions are also described.
Abstract translation: 描述了一种用于实现云计算系统的弹性的方法。 该方法包括通过将过程流映射到拓扑图上来修改网络架构的拓扑图。 基于修改的拓扑图创建弹性图。 该方法包括通过将至少一个SLA转换成弹性图来修改弹性图。 识别修改弹性图中的重叠和依赖关系。 还描述了装置和计算机可读指令。
-
公开(公告)号：US20150186779A1
公开(公告)日：2015-07-02
申请号：US14634146
申请日：2015-02-27
Applicant: International Business Machines Corporation
Inventor： Yu Deng , Rafah A. Hosn , Ruchi Mahindru , Harigovind V. Ramasamy , Soumitra Sarkar
Abstract: Techniques for model-based analysis of a data center. A method includes creating a metamodel based on domain knowledge to represent a type of object and/or relationship of a data center, using static and dynamic configuration and data analysis techniques to discover topology of elements of the data center and represent the topology as a model that is an instance of the metamodel, extending the metamodel, using the model to perform analysis of the data center in connection with a specified task, leveraging domain knowledge represented in nodes of the extended metamodel to guide the analysis in terms of determining guidelines to apply to each node and determining relationships to traverse to continue the analysis, extending the domain knowledge by updating the extended metamodel upon discovery of additional knowledge for use in improving analysis tasks, and extending the model on-demand using dynamic analysis techniques upon detection of multiple analysis failures.
Abstract translation: 数据中心模型分析技术。 一种方法包括使用静态和动态配置以及数据分析技术来创建基于领域知识的元模型来表示数据中心的类型和/或关系，以发现数据中心的元素的拓扑并将拓扑表示为模型 这是元模型的一个实例，扩展了元模型，使用该模型来执行与指定任务相关的数据中心分析，利用扩展元模型节点中所代表的领域知识来指导分析，以确定适用的准则 到每个节点并确定关系以遍历以继续分析，通过在发现用于改进分析任务的附加知识的更新扩展元模型来扩展域知识，并且在检测到多个分析时使用动态分析技术来扩展模型 失败。
-
-
-
-
-
-
-
-
-
Search Results
125
records
(took 0.034 seconds)
