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1. 发明授权

US07444272B2 Self-modulation in a model-based automated management framework 失效
标题翻译：基于模型的自动化管理框架中的自调制
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。

2. 发明授权

US07640231B2 Approach based on self-evolving models for performance guarantees in a shared storage system 失效
标题翻译：在共享存储系统中基于自演进模型进行性能保证的方法
公开(公告)号：US07640231B2
公开(公告)日：2009-12-29
申请号：US11280012
申请日：2005-11-16
申请人： Guillermo A. Alvarez , John D. Palmer , Sandeep M. Uttamchandani , Li Yin
发明人： Guillermo A. Alvarez , John D. Palmer , Sandeep M. Uttamchandani , Li Yin
IPC分类号： G06F7/00
CPC分类号： G06F9/50 , G06F9/5083 , Y10S707/99932
摘要： A technique of allocating shared resources in a computer network-based storage system comprises taking periodic performance samples on a running storage system; evaluating an objective function that takes as input the performance samples to quantify how aligned a current state of the storage system is with organizational objectives; building and maintaining models of behavior and capabilities of the storage system by using the performance samples as input; determining how resources of the storage system should be allocated among client computers in the storage system by selecting one among many possible allocations based on predictions generated by the models in order to maximize a value of the objective function; calculating a confidence statistic value for a chosen resource allocation based on an accuracy of the models; and enforcing the chosen resource allocation on the running storage system when the confidence statistic value is at or above a predetermined threshold value.
摘要翻译：在基于计算机网络的存储系统中分配共享资源的技术包括在正在运行的存储系统上采取周期性的性能样本; 评估一个目标函数，作为输入性能样本，以量化存储系统的当前状态与组织目标的一致性; 通过使用性能样本作为输入，建立和维护存储系统的行为和功能模型; 通过基于由模型生成的预测来选择许多可能的分配中的一个，以便最大化目标函数的值来确定应该在存储系统中的客户端计算机之间分配存储系统的资源; 基于模型的精度计算所选资源分配的置信度统计值; 以及当所述置信度统计值处于或高于预定阈值时，在所述运行存储系统上执行所选择的资源分配。

3. 发明申请

US20080262890A1 SYSTEM AND METHOD FOR SELECTING AND SCHEDULING CORRECTIVE ACTIONS FOR AUTOMATED STORAGE MANAGEMENT 失效
标题翻译：用于选择和调度自动存储管理的纠正措施的系统和方法
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。

4. 发明申请

US20080222218A1 RISK-MODULATED PROACTIVE DATA MIGRATION FOR MAXIMIZING UTILITY IN STORAGE SYSTEMS 失效
公开(公告)号：US20080222218A1
公开(公告)日：2008-09-11
申请号：US11681971
申请日：2007-03-05
申请人： Elizabeth S. Richards , Sandeep M. Uttamchandani , Li Yin
发明人： Elizabeth S. Richards , Sandeep M. Uttamchandani , Li Yin
IPC分类号： G06F17/30
CPC分类号： G06F3/0653 , G06F3/0605 , G06F3/0647 , G06F3/0683 , Y10S707/99953 , Y10S707/99955
摘要： The embodiments of the invention provide a method, computer program product, etc. for risk-modulated proactive data migration for maximizing utility. More specifically, a method of planning data migration for maximizing utility of a storage infrastructure that is running and actively serving at least one application includes selecting a plurality of potential data items for migration and selecting a plurality of potential migration destinations to which the potential data items can be moved. Moreover, the method selects a plurality of potential migration speeds at which the potential data items can be moved and selects a plurality of potential migration times at which the potential data items can be moved to the potential data migration destinations. The selecting of the plurality of potential migration speeds selects a migration speed below a threshold speed, wherein the threshold speed defines a maximum system utility loss permitted.

5. 发明授权

US07752239B2 Risk-modulated proactive data migration for maximizing utility in storage systems 有权
标题翻译：风险调控的主动数据迁移，以最大限度地提高存储系统的效用
公开(公告)号：US07752239B2
公开(公告)日：2010-07-06
申请号：US12061764
申请日：2008-04-03
申请人： Elizabeth S. Richards , Sandeep M. Uttamchandani , Li Yin
发明人： Elizabeth S. Richards , Sandeep M. Uttamchandani , Li Yin
IPC分类号： G06F7/00
CPC分类号： G06F3/0653 , G06F3/0605 , G06F3/0647 , G06F3/0683 , Y10S707/99953 , Y10S707/99955
摘要： The embodiments of the invention provide a method, computer program product, etc. for risk-modulated proactive data migration for maximizing utility. More specifically, a method of planning data migration for maximizing utility of a storage infrastructure that is running and actively serving at least one application includes selecting a plurality of potential data items for migration and selecting a plurality of potential migration destinations to which the potential data items can be moved. Moreover, the method selects a plurality of potential migration speeds at which the potential data items can be moved and selects a plurality of potential migration times at which the potential data items can be moved to the potential data migration destinations. The selecting of the plurality of potential migration speeds selects a migration speed below a threshold speed, wherein the threshold speed defines a maximum system utility loss permitted.
摘要翻译：本发明的实施例提供了一种用于风险调节的主动数据迁移的方法，计算机程序产品等，以最大化效用。更具体地说，一种规划数据迁移以最大化正在运行并主动地为至少一个应用服务的存储基础设施的效用的方法包括：选择多个潜在数据项以进行迁移并选择多个潜在迁移目的地，潜在数据项可以移动此外，该方法选择可移动潜在数据项的多个潜在迁移速度，并选择潜在数据项能够移动到潜在数据迁移目的地的多个潜在迁移时间。选择多个潜在迁移速度选择低于阈值速度的迁移速度，其中阈值速度定义允许的最大系统效用损失。

6. 发明授权

US08326669B2 System and method for selecting and scheduling corrective actions for automated storage management 失效
标题翻译：用于选择和调度自动化存储管理的纠正措施的系统和方法
公开(公告)号：US08326669B2
公开(公告)日：2012-12-04
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。

7. 发明授权

US07552152B2 Risk-modulated proactive data migration for maximizing utility in storage systems 失效
标题翻译：风险调控的主动数据迁移，以最大限度地提高存储系统的效用
公开(公告)号：US07552152B2
公开(公告)日：2009-06-23
申请号：US11681971
申请日：2007-03-05
申请人： Elizabeth S. Richards , Sandeep M. Uttamchandani , Li Yin
发明人： Elizabeth S. Richards , Sandeep M. Uttamchandani , Li Yin
IPC分类号： G06F12/00
CPC分类号： G06F3/0653 , G06F3/0605 , G06F3/0647 , G06F3/0683 , Y10S707/99953 , Y10S707/99955
摘要： The embodiments of the invention provide a method, computer program product, etc. for risk-modulated proactive data migration for maximizing utility. More specifically, a method of planning data migration for maximizing utility of a storage infrastructure that is running and actively serving at least one application includes selecting a plurality of potential data items for migration and selecting a plurality of potential migration destinations to which the potential data items can be moved. Moreover, the method selects a plurality of potential migration speeds at which the potential data items can be moved and selects a plurality of potential migration times at which the potential data items can be moved to the potential data migration destinations. The selecting of the plurality of potential migration speeds selects a migration speed below a threshold speed, wherein the threshold speed defines a maximum system utility loss permitted.
摘要翻译：本发明的实施例提供了一种用于风险调节的主动数据迁移的方法，计算机程序产品等，以最大化效用。更具体地说，一种规划数据迁移以最大化正在运行并主动地为至少一个应用服务的存储基础设施的效用的方法包括：选择多个潜在数据项以进行迁移并选择多个潜在迁移目的地，潜在数据项可以移动此外，该方法选择可移动潜在数据项的多个潜在迁移速度，并选择潜在数据项能够移动到潜在数据迁移目的地的多个潜在迁移时间。选择多个潜在迁移速度选择低于阈值速度的迁移速度，其中阈值速度定义允许的最大系统效用损失。

8. 发明申请

US20080222644A1 RISK-MODULATED PROACTIVE DATA MIGRATION FOR MAXIMIZING UTILITY IN STORAGE SYSTEMS 有权
标题翻译：用于最大化存储系统中的实用程序的风险调整的主动数据迁移
公开(公告)号：US20080222644A1
公开(公告)日：2008-09-11
申请号：US12061764
申请日：2008-04-03
申请人： Elizabeth S. Richards , Sandeep M. Uttamchandani , Li Yin
发明人： Elizabeth S. Richards , Sandeep M. Uttamchandani , Li Yin
IPC分类号： G06F9/50 , G06F12/00
CPC分类号： G06F3/0653 , G06F3/0605 , G06F3/0647 , G06F3/0683 , Y10S707/99953 , Y10S707/99955
摘要： The embodiments of the invention provide a method, computer program product, etc. for risk-modulated proactive data migration for maximizing utility. More specifically, a method of planning data migration for maximizing utility of a storage infrastructure that is running and actively serving at least one application includes selecting a plurality of potential data items for migration and selecting a plurality of potential migration destinations to which the potential data items can be moved. Moreover, the method selects a plurality of potential migration speeds at which the potential data items can be moved and selects a plurality of potential migration times at which the potential data items can be moved to the potential data migration destinations. The selecting of the plurality of potential migration speeds selects a migration speed below a threshold speed, wherein the threshold speed defines a maximum system utility loss permitted.
摘要翻译：本发明的实施例提供了一种用于风险调节的主动数据迁移的方法，计算机程序产品等，以最大化效用。更具体地说，一种规划数据迁移以最大化正在运行并主动地为至少一个应用服务的存储基础设施的效用的方法包括：选择多个潜在数据项以进行迁移并选择多个潜在迁移目的地，潜在数据项可以移动此外，该方法选择可移动潜在数据项的多个潜在迁移速度，并选择潜在数据项能够移动到潜在数据迁移目的地的多个潜在迁移时间。选择多个潜在迁移速度选择低于阈值速度的迁移速度，其中阈值速度定义允许的最大系统效用损失。

9. 发明授权

US09679029B2 Optimizing storage cloud environments through adaptive statistical modeling 有权
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.

10. 发明授权

US09329908B2 Proactive identification of hotspots in a cloud computing environment 有权
标题翻译：在云计算环境中主动识别热点
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。

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