公开(公告)号：US08489809B2

公开(公告)日：2013-07-16

申请号：US12831455

申请日：2010-07-07

申请人： Kavita Chavda ,  David P. Goodman ,  Sandeep Gopisetty ,  Seshashayee S. Murthy ,  Aameek Singh

发明人： Kavita Chavda ,  David P. Goodman ,  Sandeep Gopisetty ,  Seshashayee S. Murthy ,  Aameek Singh

IPC分类号： G06F21/00

CPC分类号： H04L67/1097 ,  G06F3/0607 ,  G06F3/0631 ,  G06F3/0664 ,  G06F3/067

摘要： Embodiments of the present invention provide an approach for intelligent storage planning and planning within a clustered computing environment (e.g., a cloud computing environment). Specifically, embodiments of the present invention will first determine/identify a set of storage area network volume controllers (SVCs) that is accessible from a host that has submitted a request for access to storage. Thereafter, a set of managed disk (mdisk) groups (i.e., corresponding to the set of SVCs) that are candidates for satisfying the request will be determined. This set of mdisk groups will then be filtered based on available space therein, a set of user/requester preferences, and optionally, a set of performance characteristics. Then, a particular mdisk group will be selected from the set of mdisk groups based on the filtering.

摘要翻译： 本发明的实施例提供了一种用于集群计算环境（例如，云计算环境）内的智能存储规划和规划的方法。 具体地，本发明的实施例将首先确定/识别可从已经提交了访问存储请求的主机可访问的一组存储区域网络卷控制器（SVC）。 此后，将确定作为满足该请求的候选的一组被管理盘（mdisk）组（即，对应于一组SVC）。 然后，这组mdisk组将根据其中的可用空间，一组用户/请求者首选项以及可选的一组性能特征进行过滤。 然后，将根据过滤从一组mdisk组中选择一个特定的mdisk组。

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：US09106675B2

公开(公告)日：2015-08-11

申请号：US12817238

申请日：2010-06-17

申请人： Gabriel Alatorre ,  Eric K. Butler ,  Kavita Chavda ,  Sandeep Gopisetty ,  Seshashayee S. Murthy ,  Aameek Singh

发明人： Gabriel Alatorre ,  Eric K. Butler ,  Kavita Chavda ,  Sandeep Gopisetty ,  Seshashayee S. Murthy ,  Aameek Singh

IPC分类号： G06F15/16 ,  H04L29/08 ,  H04L12/24 ,  H04L12/26

CPC分类号： H04L67/1097 ,  H04L41/0816 ,  H04L41/145 ,  H04L43/0876

摘要： Embodiments of the present invention provide an integrated host and subsystem port selection methodology that uses performance measurements combined with information about active data paths. This technique also helps in resilient fabric planning by selecting ports from redundant fabrics. In a typical embodiment, host port to storage port pairs that create a path between a host and a storage device will be identified. From these pairs, a set of host port to storage port candidates for communicate data from the host to the storage device will be identified based on a set of resiliency constraints. Then, a specific host port to storage port pair will be selected from the set based on a lowest joint workload measurement. A path will then be created between the specific host port and storage port, and data will be communicated from the host to the storage device via the path.

摘要翻译： 本发明的实施例提供了一种集成的主机和子系统端口选择方法，其使用结合有关活动数据路径的信息的性能测量。 这种技术还通过从冗余结构中选择端口来帮助弹性布局规划。 在典型的实施例中，将识别在主机和存储设备之间创建路径的主机端口到存储端口对。 从这些对中，将基于一组弹性约束来识别从主机到存储设备的用于传送数据的存储端口候选的一组主机端口。 然后，将根据最低联合工作负载测量从集合中选择特定的主机端口到存储端口对。 然后将在特定主机端口和存储端口之间创建路径，并且数据将通过路径从主机传送到存储设备。

公开(公告)号：US20110314164A1

公开(公告)日：2011-12-22

申请号：US12817238

申请日：2010-06-17

申请人： Gabriel Alatorre ,  Eric K. Butler ,  Kavita Chavda ,  Sandeep Gopisetty ,  Seshashayee S. Murthy ,  Aameek Singh

发明人： Gabriel Alatorre ,  Eric K. Butler ,  Kavita Chavda ,  Sandeep Gopisetty ,  Seshashayee S. Murthy ,  Aameek Singh

IPC分类号： G06F15/16

CPC分类号： H04L67/1097 ,  H04L41/0816 ,  H04L41/145 ,  H04L43/0876

摘要： Embodiments of the present invention provide an integrated host and subsystem port selection methodology that uses performance measurements combined with information about active data paths. This technique also helps in resilient fabric planning by selecting ports from redundant fabrics. In a typical embodiment, host port to storage port pairs that create a path between a host and a storage device will be identified. From these pairs, a set of host port to storage port candidates for communicate data from the host to the storage device will be identified based on a set of resiliency constraints. Then, a specific host port to storage port pair will be selected from the set based on a lowest joint workload measurement. A path will then be created between the specific host port and storage port, and data will be communicated from the host to the storage device via the path.

摘要翻译： 本发明的实施例提供了一种集成的主机和子系统端口选择方法，其使用结合有关活动数据路径的信息的性能测量。 这种技术还通过从冗余结构中选择端口来帮助弹性布局规划。 在典型的实施例中，将识别在主机和存储设备之间创建路径的主机端口到存储端口对。 从这些对中，将基于一组弹性约束来识别从主机到存储设备的用于传送数据的存储端口候选的一组主机端口。 然后，将根据最低联合工作负载测量从集合中选择特定的主机端口到存储端口对。 然后将在特定主机端口和存储端口之间创建路径，并且数据将通过路径从主机传送到存储设备。

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。

公开(公告)号：US20120042061A1

公开(公告)日：2012-02-16

申请号：US12855780

申请日：2010-08-13

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

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

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。

公开(公告)号：US09323561B2

公开(公告)日：2016-04-26

申请号：US12855780

申请日：2010-08-13

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

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

IPC分类号： G06F9/455 ,  G06F9/50 ,  G06F11/34 ,  H04L12/24

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。

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：US20120079097A1

公开(公告)日：2012-03-29

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： G06F15/16 ,  G06F15/173

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。 热点可能会发生，并且可以在云计算环境的任何一层检测，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：US08527998B2

公开(公告)日：2013-09-03

申请号：US13569620

申请日：2012-08-08

申请人： Brian Cho ,  Kavita Chavda ,  Mickey Iqbal ,  Seshashayee S. Murthy ,  Sandeep M. Uttamchandani ,  Pin Zhou

发明人： Brian Cho ,  Kavita Chavda ,  Mickey Iqbal ,  Seshashayee S. Murthy ,  Sandeep M. Uttamchandani ,  Pin Zhou

IPC分类号： G06F9/455 ,  G06F9/46 ,  G06F7/00

CPC分类号： G06F9/4881 ,  G06F11/1402

摘要： A system and associated method for continuously optimizing data archive management scheduling. A job scheduler receives, from an archive management system, inputs of task information, replica placement data, infrastructure topology data, and resource performance data. The job scheduler models a flow network that represents data content, software programs, physical devices, and communication capacity of the archive management system in various levels of vertices according to the received inputs. An optimal path in the modeled flow network is computed as an initial schedule, and the archive management system performs tasks according to the initial schedule. The operations of scheduled tasks are monitored and the job scheduler produces a new schedule based on feedbacks of the monitored operations and predefined heuristics.

摘要翻译： 一种用于连续优化数据归档管理调度的系统和相关方法。 作业调度器从归档管理系统接收任务信息，副本放置数据，基础架构拓扑数据和资源性能数据的输入。 作业调度器根据接收到的输入来建立表示数据内容，软件程序，物理设备和档案管理系统在各种顶点级别中的通信能力的流网络。 模拟流网络中的最优路径计算为初始调度，归档管理系统根据初始调度执行任务。 监视计划任务的操作，作业调度程序根据监视的操作和预定义的启发式反馈来生成新的调度。