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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.
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2.发明申请公开(公告)号：US20120116743A1
公开(公告)日：2012-05-10
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： G06G7/62
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.
摘要翻译： 本发明的实施例提供了一种用于通过创建和管理从群集计算环境内的操作设备的性能统计生成的一组统计模型来适配用于群集计算环境（例如，云环境）的信息提取中间件的方法。 这种方法考虑了所需的建模精度，包括建模的计算成本，以便在给定时间点选择最佳建模解决方案。 当需要更高的精度（例如，接近工作负载饱和）时，该方法适应于使用适当的建模算法。 将统计模型适应数据特征确保最佳精度，最小的计算时间和建模资源。 该方法提供使用基于精度和基于概率的触发器来优化群集计算环境（即，最大化精度和最小化计算时间）的模型的智能选择性细化。
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公开(公告)号：US09323561B2
公开(公告)日：2016-04-26
申请号：US12855780
申请日：2010-08-13
CPC分类号： G06F9/45558 , G06F9/5072 , G06F11/3409 , G06F11/3433 , G06F11/3442 , G06F2009/4557 , G06F2209/501 , H04L41/14 , H04L41/145
摘要： In general, embodiments of present invention provide an approach for calibrating a cloud computing environment. Specifically, embodiments of the present invention provide an empirical approach for obtaining end-to-end performance characteristics for workloads in the cloud computing environment (hereinafter the “environment”). In a typical embodiment, different combinations of cloud server(s) and cloud storage unit(s) are determined. Then, a virtual machine is deployed to one or more of the servers within the cloud computing environment. The virtual machine is used to generate a desired workload on a set of servers within the environment. Thereafter, performance measurements for each of the different combinations under the desired workload will be taken. Among other things, the performance measurements indicate a connection quality between the set of servers and the set of storage units, and are used in calibrating the cloud computing environment to determine future workload placement. Along these lines, the performance measurements can be populated into a table or the like, and a dynamic map of a data center having the set of storage units can be generated.
摘要翻译： 通常，本发明的实施例提供了一种用于校准云计算环境的方法。 具体地，本发明的实施例提供了一种用于获得云计算环境（以下称为“环境”）中的工作负载的端到端性能特征的经验性方法。 在典型的实施例中，确定云服务器和云存储单元的不同组合。 然后，将虚拟机部署到云计算环境中的一个或多个服务器。 虚拟机用于在环境中的一组服务器上生成所需的工作负载。 此后，将采取在所需工作负荷下的每个不同组合的性能测量。 其中，性能测量表明服务器组和存储单元组之间的连接质量，并用于校准云计算环境以确定未来的工作负载布局。 沿着这些行，性能测量可以被填充到表等中，并且可以生成具有该组存储单元的数据中心的动态映射。
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公开(公告)号：US20120042061A1
公开(公告)日：2012-02-16
申请号：US12855780
申请日：2010-08-13
IPC分类号： G06F15/173 , G06F9/455
CPC分类号： G06F9/45558 , G06F9/5072 , G06F11/3409 , G06F11/3433 , G06F11/3442 , G06F2009/4557 , G06F2209/501 , H04L41/14 , H04L41/145
摘要： In general, embodiments of present invention provide an approach for calibrating a cloud computing environment. Specifically, embodiments of the present invention provide an empirical approach for obtaining end-to-end performance characteristics for workloads in the cloud computing environment (hereinafter the “environment”). In a typical embodiment, different combinations of cloud server(s) and cloud storage unit(s) are determined. Then, a virtual machine is deployed to one or more of the servers within the cloud computing environment. The virtual machine is used to generate a desired workload on a set of servers within the environment. Thereafter, performance measurements for each of the different combinations under the desired workload will be taken. Among other things, the performance measurements indicate a connection quality between the set of servers and the set of storage units, and are used in calibrating the cloud computing environment to determine future workload placement. Along these lines, the performance measurements can be populated into a table or the like, and a dynamic map of a data center having the set of storage units can be generated.
摘要翻译： 通常，本发明的实施例提供了一种用于校准云计算环境的方法。 具体地，本发明的实施例提供了一种用于获得云计算环境（以下称为“环境”）中的工作负载的端到端性能特征的经验性方法。 在典型的实施例中，确定云服务器和云存储单元的不同组合。 然后，将虚拟机部署到云计算环境中的一个或多个服务器。 虚拟机用于在环境中的一组服务器上生成所需的工作负载。 此后，将采取在所需工作负荷下的每个不同组合的性能测量。 其中，性能测量表明服务器组和存储单元组之间的连接质量，并用于校准云计算环境以确定未来的工作负载布局。 沿着这些行，性能测量可以被填充到表等中，并且可以生成具有该组存储单元的数据中心的动态映射。
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公开(公告)号：US20110314069A1
公开(公告)日：2011-12-22
申请号：US12817245
申请日：2010-06-17
IPC分类号： G06F17/30
CPC分类号： G06F17/30082
摘要： Embodiments of the present invention provide lifecycle storage management for data within a Cloud computing environment. Specifically, a set of policies can be defined that allow for automatic valuation of the data and migration of the data between a set of storage tiers. Before a policy set is deployed, it can be assessed to determine effects it will have on cost, performance, and data location. Based on data characteristics and access patterns, a set of policy recommendations can be provided that predict the value of the data over time, and offer an improved migration strategy for moving the data between the set of storage tiers as the value of the data changes.
摘要翻译： 本发明的实施例为云计算环境中的数据提供生命周期存储管理。 具体来说，可以定义一组策略，允许数据的自动估价和一组存储层之间的数据迁移。 在部署策略集之前，可以对其进行评估，以确定其对成本，性能和数据位置的影响。 基于数据特征和访问模式，可以提供一组预测数据随时间推移的策略建议，并提供改进的迁移策略，用于随着数据值的变化在一组存储层之间移动数据。
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公开(公告)号：US08918439B2
公开(公告)日：2014-12-23
申请号：US12817245
申请日：2010-06-17
CPC分类号： G06F17/30082
摘要： Embodiments of the present invention provide lifecycle storage management for data within a Cloud computing environment. Specifically, a set of policies can be defined that allow for automatic valuation of the data and migration of the data between a set of storage tiers. Before a policy set is deployed, it can be assessed to determine effects it will have on cost, performance, and data location. Based on data characteristics and access patterns, a set of policy recommendations can be provided that predict the value of the data over time, and offer an improved migration strategy for moving the data between the set of storage tiers as the value of the data changes.
摘要翻译： 本发明的实施例为云计算环境中的数据提供生命周期存储管理。 具体来说，可以定义一组策略，允许数据的自动估价和一组存储层之间的数据迁移。 在部署策略集之前，可以对其进行评估，以确定其对成本，性能和数据位置的影响。 基于数据特征和访问模式，可以提供一组预测数据随时间推移的策略建议，并提供改进的迁移策略，用于随着数据值的变化在一组存储层之间移动数据。
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