公开(公告)号：US09679029B2

公开(公告)日：2017-06-13

申请号：US12942011

申请日：2010-11-08

申请人： Richard Ayala ,  Kavita Chavda ,  Sandeep Gopisetty ,  Seshashayee S. Murthy ,  Aameek Singh ,  Sandeep M. Uttamchandani

发明人： Richard Ayala ,  Kavita Chavda ,  Sandeep Gopisetty ,  Seshashayee S. Murthy ,  Aameek Singh ,  Sandeep M. Uttamchandani

IPC分类号： G06F17/30

CPC分类号： G06F17/30557

摘要： Embodiments of the present invention provide an approach for adapting an information extraction middleware for a clustered computing environment (e.g., a cloud environment) by creating and managing a set of statistical models generated from performance statistics of operating devices within the clustered computing environment. This approach takes into account the required accuracy in modeling, including computation cost of modeling, to pick the best modeling solution at a given point in time. When higher accuracy is desired (e.g., nearing workload saturation), the approach adapts to use an appropriate modeling algorithm. Adapting statistical models to the data characteristics ensures optimal accuracy with minimal computation time and resources for modeling. This approach provides intelligent selective refinement of models using accuracy-based and operating probability-based triggers to optimize the clustered computing environment, i.e., maximize accuracy and minimize computation time.

公开(公告)号：US09329908B2

公开(公告)日：2016-05-03

申请号：US12893302

申请日：2010-09-29

申请人： Sandeep Gopisetty ,  Seshashayee S. Murthy ,  Aameek Singh ,  Sandeep M. Uttamchandani ,  David D. Weck

发明人： Sandeep Gopisetty ,  Seshashayee S. Murthy ,  Aameek Singh ,  Sandeep M. Uttamchandani ,  David D. Weck

IPC分类号： G06F15/173 ,  G06F9/50

CPC分类号： H04L43/10 ,  G06F9/5083 ,  G06F2209/508 ,  H04L67/10

摘要： The present invention proactively identifies hotspots in a cloud computing environment through cloud resource usage models that use workload parameters as inputs. In some embodiments the cloud resource usage models are based upon performance data from cloud resources and time series based workload trend models. Hotspots may occur and can be detected at any layer of the cloud computing environment, including the server, storage, and network level. In a typical embodiment, parameters for a workload are identified in the cloud computing environment and inputted into a cloud resource usage model. The model is run with the inputted workload parameters to identify potential hotspots, and resources are then provisioned for the workload so as to avoid these hotspots.

摘要翻译： 本发明通过使用工作负载参数作为输入的云资源使用模型主动地识别云计算环境中的热点。 在一些实施例中，云资源使用模型基于来自云资源和基于时间序列的工作负载趋势模型的性能数据。 热点可能发生，可以在云计算环境的任何层面检测，包括服务器，存储和网络级别。 在典型的实施例中，工作负载的参数在云计算环境中被识别并被输入到云资源使用模型中。 该模型使用输入的工作负载参数运行，以识别潜在的热点，然后为工作负载提供资源，以避免这些热点。

公开(公告)号：US08315991B2

公开(公告)日：2012-11-20

申请号：US12763934

申请日：2010-04-20

申请人： Nagapramod S. Mandagere ,  Mark J. Seaman ,  Sandeep M. Uttamchandani

发明人： Nagapramod S. Mandagere ,  Mark J. Seaman ,  Sandeep M. Uttamchandani

IPC分类号： G06F17/30

CPC分类号： G06F11/1435 ,  G06F17/30368 ,  G06F2201/84

摘要： Embodiments of the invention detect inadvertent or malicious data corruption and for recovering data including receiving a query specifying corrupted application data; analyzing transaction logs to find update operations related to the data; determining where the data are stored, by mapping the table data to locations within the file system and mapping the file system locations to volume logical blocks; and analyzing snapshot volume bitmaps to determine if the bitmaps show changes to the table data stored in the volume logical blocks. Changes which are reflected in the bitmaps for the data, but which do not have corresponding entries in the transaction logs are flagged as unauthorized changes. Snapshots of the data, from a time prior to the time at which a flagged snapshot was taken, are identified for use in restoring data to its status prior to the unauthorized change.

摘要翻译： 本发明的实施例检测无意或恶意的数据损坏并且用于恢复数据，包括接收指定损坏的应用数据的查询; 分析事务日志以查找与数据相关的更新操作; 通过将表数据映射到文件系统内的位置并将文件系统位置映射到卷逻辑块来确定数据的存储位置; 并分析快照卷位图以确定位图是否显示存储在卷逻辑块中的表数据的更改。 反映在数据的位图中但在事务日志中没有相应条目的更改将被标记为未经授权的更改。 在从被标记的快照拍摄之前的时间起，数据的快照被识别用于在未经授权的更改之前将数据恢复到其状态。

公开(公告)号：US08266280B2

公开(公告)日：2012-09-11

申请号：US12725952

申请日：2010-03-17

申请人： Seshashayee S. Murthy ,  Ramani R. Routray ,  Sandeep M. Uttamchandani

发明人： Seshashayee S. Murthy ,  Ramani R. Routray ,  Sandeep M. Uttamchandani

IPC分类号： G06F15/173

CPC分类号： H04L63/10 ,  G06F3/0613 ,  G06F3/0635 ,  G06F3/067 ,  H04L67/1097 ,  H04L69/32

摘要： A method is implemented in a computer infrastructure having computer executable code tangibly embodied on a computer readable storage medium having programming instructions. The programming instructions are operable to determine one or more optimal mappings between a server layer and a storage layer through a network layer based on performance metrics of one or more ports of at least one of the server layer, the storage layer and the network layer.

摘要翻译： 在具有计算机可执行代码的计算机基础设施中实现一种方法，其有形地体现在具有编程指令的计算机可读存储介质上 编程指令可操作以基于服务器层，存储层和网络层中的至少一个的一个或多个端口的性能度量，通过网络层确定服务器层和存储层之间的一个或多个最优映射。

公开(公告)号：US20110258164A1

公开(公告)日：2011-10-20

申请号：US12763934

申请日：2010-04-20

申请人： Nagapramod S. Mandagere ,  Mark J. Seaman ,  Sandeep M. Uttamchandani

发明人： Nagapramod S. Mandagere ,  Mark J. Seaman ,  Sandeep M. Uttamchandani

IPC分类号： G06F12/16 ,  G06F17/30

CPC分类号： G06F11/1435 ,  G06F17/30368 ,  G06F2201/84

摘要： Embodiments of the invention detect inadvertent or malicious data corruption and for recovering data including receiving a query specifying corrupted application data; analyzing transaction logs to find update operations related to the data; determining where the data are stored, by mapping the table data to locations within the file system and mapping the file system locations to volume logical blocks; and analyzing snapshot volume bitmaps to determine if the bitmaps show changes to the table data stored in the volume logical blocks. Changes which are reflected in the bitmaps for the data, but which do not have corresponding entries in the transaction logs are flagged as unauthorized changes. Snapshots of the data, from a time prior to the time at which a flagged snapshot was taken, are identified for use in restoring data to its status prior to the unauthorized change.

摘要翻译： 本发明的实施例检测无意或恶意的数据损坏并且用于恢复数据，包括接收指定损坏的应用数据的查询; 分析事务日志以查找与数据相关的更新操作; 通过将表格数据映射到文件系统内的位置并将文件系统位置映射到卷逻辑块来确定数据的存储位置; 并分析快照卷位图以确定位图是否显示存储在卷逻辑块中的表数据的更改。 反映在数据的位图中但在事务日志中没有相应条目的更改将被标记为未经授权的更改。 在从被标记的快照拍摄之前的时间起，数据的快照被识别用于在未经授权的更改之前将数据恢复到其状态。

公开(公告)号：US20110126219A1

公开(公告)日：2011-05-26

申请号：US12622737

申请日：2009-11-20

申请人： Aameek Singh ,  Sandeep M. Uttamchandani ,  Yin Wang

发明人： Aameek Singh ,  Sandeep M. Uttamchandani ,  Yin Wang

IPC分类号： G06F9/46

CPC分类号： G06F9/50

摘要： Management of a planner through use of a middleware layer. A computer system is configured with the middleware layer in communication with both a planner and a data repository. One or more modules are provided in the middleware layer to support the functionality of the planner. Application program interface calls are employed to call the modules, thereby mitigating duplication of the functionality in separate planners.

摘要翻译： 通过使用中间件层来管理计划者。 计算机系统配置有与计划者和数据存储库通信的中间件层。 在中间件层中提供一个或多个模块以支持计划者的功能。 应用程序接口调用用于调用模块，从而减轻单独规划者中功能的重复。

公开(公告)号：US20100250501A1

公开(公告)日：2010-09-30

申请号：US12411902

申请日：2009-03-26

申请人： Nagapramod Mandagere ,  Mark A. Smith ,  Sandeep M. Uttamchandani ,  Pin Zhou

发明人： Nagapramod Mandagere ,  Mark A. Smith ,  Sandeep M. Uttamchandani ,  Pin Zhou

IPC分类号： G06F12/02 ,  G06F12/00 ,  G06F17/30

CPC分类号： G06F17/30598 ,  G06F17/30489

摘要： One embodiment retrieves a first portion of a plurality of stored objects from at least one storage device. The embodiment further performs a base type deduplication estimation process on the first portion of stored objects. The embodiment still further categorizes the first portion of the plurality of stored objects into deduplication sets based on a deduplication relationship of each object of the plurality of stored objects with each of the estimated first plurality of deduplication chunk portions. The embodiment further combines deduplication sets into broad classes based on deduplication characteristics of the objects in the deduplication sets. The embodiment still further classifies a second portion of the plurality of stored objects into broad classes using classifiers. The embodiment further selects an appropriate deduplication approach for each categorized class.

摘要翻译： 一个实施例从至少一个存储设备检索多个存储对象的第一部分。 该实施例还对存储对象的第一部分执行基本类型的重复数据删除估计处理。 该实施例还基于多个存储对象中的每个对象与所估计的第一多个重复数据删除块部分中的每一个的重复数据删除关系，进一步将多个存储对象的第一部分分类为重复数据删除集合。 该实施例还将重复数据删除集合基于重复数据删除集中的对象的重复数据删除特性组合成大类。 该实施例还使用分类器进一步将多个存储对象的第二部分分类为广泛类。 该实施例进一步为每个分类的类选择适当的重复数据删除方法。

公开(公告)号：US07444272B2

公开(公告)日：2008-10-28

申请号：US11250066

申请日：2005-10-13

申请人： Guillermo A. Alvarez ,  Linda M. Duyanovich ,  John D. Palmer ,  Sandeep M. Uttamchandani ,  Li Yin

发明人： Guillermo A. Alvarez ,  Linda M. Duyanovich ,  John D. Palmer ,  Sandeep M. Uttamchandani ,  Li Yin

IPC分类号： G06F17/10 ,  G06E1/00

CPC分类号： G05B17/02 ,  G05B13/042

摘要： Embodiments herein present a method, system, computer program product, etc. for automated management using a hybrid of prediction models and feedback-based systems. The method begins by calculating confidence values of models. Next, the method selects a first model based on the confidence values and processes the first model through a constraint solver to produce first workload throttling values. Following this, workloads are repeatedly processed through a feedback-based execution engine, wherein the feedback-based execution engine is controlled by the first workload throttling values. The first workload throttling values are applied incrementally to the feedback-based execution engine, during repetitions of the processing of the workloads, with a step-size that is proportional to the confidence values. The processing of the workloads is repeated until an objective function is maximized, wherein the objective function specifies performance goals of the workloads.

摘要翻译： 本文的实施例提出了使用预测模型和基于反馈的系统的混合的自动化管理的方法，系统，计算机程序产品等。 该方法从计算模型的置信度开始。 接下来，该方法基于置信度值选择第一模型，并通过约束求解器处理第一模型以产生第一工作负载节流值。 此后，通过基于反馈的执行引擎重复处理工作负载，其中基于反馈的执行引擎由第一工作负载节流值控制。 在重复处理工作负载期间，第一个工作负载限制值将逐步应用于基于反馈的执行引擎，步长与置信度值成比例。 重复处理工作负载直到目标函数最大化，其中目标函数指定工作负载的性能目标。

公开(公告)号：US20080262890A1

公开(公告)日：2008-10-23

申请号：US11737249

申请日：2007-04-19

申请人： Madhukar R. Korupolu ,  Sandeep M. Uttamchandani ,  Kaladhar Voruganti ,  Li Yin

发明人： Madhukar R. Korupolu ,  Sandeep M. Uttamchandani ,  Kaladhar Voruganti ,  Li Yin

IPC分类号： G06F9/44

CPC分类号： G06Q10/06312 ,  G06F11/0793 ,  G06Q10/06 ,  G06Q10/06316 ,  G06Q10/0635

摘要： Disclosed is a storage management framework that integrates corrective action plans output from multiple different types of planning tools, sorts the different corrective action plans based on utility and risk values and outputs a time-based schedule for implementing one or more of the corrective action plans to resolve identified current and anticipated workload service level objective (SLO) violations.

摘要翻译： 公开了一种存储管理框架，其将来自多种不同类型的规划工具的校正行动计划的输出整合在一起，根据效用和风险值对不同的纠正行动计划进行排序，并输出基于时间的时间表，以执行一个或多个纠正措施计划 解决当前和预期的工作负载服务级别目标（SLO）违规。

公开(公告)号：US08818962B2

公开(公告)日：2014-08-26

申请号：US12788026

申请日：2010-05-26

申请人： Nagapramod S. Mandagere ,  Ramani R. Routray ,  Sandeep M. Uttamchandani ,  Pin Zhou

发明人： Nagapramod S. Mandagere ,  Ramani R. Routray ,  Sandeep M. Uttamchandani ,  Pin Zhou

IPC分类号： G06F17/00 ,  G06F17/30

CPC分类号： G06F11/1004

摘要： Embodiments of the invention relate to testing a storage system point-in-time copy of data for consistency. An aspect of the invention includes receiving system and application event information from systems and applications associated with point-in-time copies of data. The system and application event information is associated with each of point-in-time copies of data. At least one point-in-time copy of data is selected for testing. The system and application event information is compared with inconsistency classes to determine tests for testing the point-in-time copy of data. The point-in-time copy of data is tested.

摘要翻译： 本发明的实施例涉及测试用于一致性的数据的存储系统时间点副本。 本发明的一个方面包括从与数据的时间点副本相关联的系统和应用接收系统和应用事件信息。 系统和应用事件信息与数据的每个时间点副本相关联。 选择至少一个时间点的数据副本进行测试。 将系统和应用程序事件信息与不一致类进行比较，以确定测试数据的时间点副本。 测试数据的时间点副本。