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1. 发明授权

US08478845B2 End-to-end provisioning of storage clouds 失效
标题翻译：存储云的端到端配置
公开(公告)号：US08478845B2
公开(公告)日：2013-07-02
申请号：US12857005
申请日：2010-08-16
申请人： Sandip Agarwala , Richard J. Ayala , Kavita Chavda , Sandeep Gopisetty
发明人： Sandip Agarwala , Richard J. Ayala , Kavita Chavda , Sandeep Gopisetty
IPC分类号： G06F15/177 , G06F15/167 , G06F15/173
CPC分类号： G06F15/177 , G06F3/0605 , G06F3/0629 , G06F3/067 , G06F9/5061 , H04L41/5054 , H04L67/1097
摘要： Embodiments discussed in this disclosure provide an integrated provisioning framework that automates the process of provisioning storage resources, end-to-end, for an enterprise storage cloud environment. Such embodiments configure and orchestrate the deployment of a user's workload and, at the same time, provide optimization across a multitude of storage cloud resources. Along these lines, input is received in the form of workload requirements and configuration information for available system resources. Based on the input, a set (at least one) of storage cloud configuration plans is developed that satisfy the workload requirements. A set of scripts is then generated that orchestrate the deployment and configuration of different software and hardware components based on the plans.
摘要翻译：在本公开中讨论的实施例提供了一种集成供应框架，其自动化为企业存储云环境提供端到端的存储资源的过程。这样的实施例配置和协调用户工作负载的部署，并且同时在多个存储云资源上提供优化。沿着这些方式，以可用系统资源的工作负载需求和配置信息的形式接收输入。基于输入，开发满足工作负载要求的一组（至少一个）存储云配置计划。然后生成一组脚本，根据计划编排不同软件和硬件组件的部署和配置。

2. 发明授权

US08407501B2 Allocation of storage resources in a networked computing environment based on energy utilization 有权
标题翻译：基于能源利用的网络计算环境中的存储资源分配
公开(公告)号：US08407501B2
公开(公告)日：2013-03-26
申请号：US13073081
申请日：2011-03-28
申请人： Sandip Agarwala , Eric K. Butler , Sandeep Gopisetty , Kavita Chavda
发明人： Sandip Agarwala , Eric K. Butler , Sandeep Gopisetty , Kavita Chavda
IPC分类号： G06F1/26
CPC分类号： H04L47/70 , G06F3/0625 , G06F3/0635 , G06F3/067 , Y02D10/154
摘要： Embodiments of the present invention provide an approach to provision storage resources (e.g., across an enterprise storage system (ESS) such as a general parallel file system (GPFS) or the like) for different workloads in an energy efficient manner. The system evaluates different energy profiles/workloads' energy consumption characteristics of storage devices to determine an allocation plan that reduces the energy cost (e.g., results in the lowest cost/energy consumption for handling a storage workload). In a typical embodiment, energy consumption characteristics for handling a particular storage workload will be determined. Thereafter, a type of storage device capable of handling the workload will be determined. Then, an allocation plan that results in the most efficient energy consumption for handling the workload will be developed. In general, the allocation plan is based upon the energy consumption characteristics and an energy efficiency algorithm. The energy efficiency algorithm serves to identify storage device(s) that can handle the workload in such a way as to reduce total energy consumption and, accordingly, costs. Along these lines, the energy efficiency algorithm may also consider other factors such as capacity and load of storage devices and service level agreement (SLA) terms in addition to energy costs (e.g., over times of day and/or days of week). In any event, at least one storage device can then be selected for handling the storage workload according to the allocation plan.
摘要翻译：本发明的实施例提供了一种以能量效率方式为不同工作负载提供存储资源（例如，跨企业存储系统（ESS），诸如通用并行文件系统（GPFS）等）的方法。该系统评估存储设备的不同能量简档/工作负载的能量消耗特征，以确定降低能量成本的分配计划（例如，导致用于处理存储工作负载的最低成本/能量消耗）。在典型的实施例中，将确定用于处理特定存储工作负载的能量消耗特性。此后，可以确定能够处理工作量的一种存储装置。然后，将开发出一种能够最有效地处理工作负载能耗的分配计划。一般来说，分配方案是基于能量消耗特性和能量效率算法。能源效率算法用于识别能够处理工作负载的存储设备，以减少总能量消耗，并因此降低成本。除此之外，能源效率算法还可以考虑其他因素，例如存储设备的容量和负载以及服务水平协议（SLA）术语以及能量成本（例如，超过一天和/或几周的时间）。在任何情况下，可以根据分配计划选择至少一个存储设备来处理存储工作负载。

3. 发明申请

US20120254640A1 ALLOCATION OF STORAGE RESOURCES IN A NETWORKED COMPUTING ENVIRONMENT BASED ON ENERGY UTILIZATION 有权
标题翻译：基于能源利用的网络计算环境中的存储资源分配
公开(公告)号：US20120254640A1
公开(公告)日：2012-10-04
申请号：US13073081
申请日：2011-03-28
申请人： Sandip Agarwala , Eric K. Butler , Sandeep Gopisetty , Kavita Chavda
发明人： Sandip Agarwala , Eric K. Butler , Sandeep Gopisetty , Kavita Chavda
IPC分类号： G06F12/02 , G06F1/32
CPC分类号： H04L47/70 , G06F3/0625 , G06F3/0635 , G06F3/067 , Y02D10/154
摘要： Embodiments of the present invention provide an approach to provision storage resources (e.g., across an enterprise storage system (ESS) such as a general parallel file system (GPFS) or the like) for different workloads in an energy efficient manner. The system evaluates different energy profiles/workloads' energy consumption characteristics of storage devices to determine an allocation plan that reduces the energy cost (e.g., results in the lowest cost/energy consumption for handling a storage workload). In a typical embodiment, energy consumption characteristics for handling a particular storage workload will be determined. Thereafter, a type of storage device capable of handling the workload will be determined. Then, an allocation plan that results in the most efficient energy consumption for handling the workload will be developed. In general, the allocation plan is based upon the energy consumption characteristics and an energy efficiency algorithm. The energy efficiency algorithm serves to identify storage device(s) that can handle the workload in such a way as to reduce total energy consumption and, accordingly, costs. Along these lines, the energy efficiency algorithm may also consider other factors such as capacity and load of storage devices and service level agreement (SLA) terms in addition to energy costs (e.g., over times of day and/or days of week). In any event, at least one storage device can then be selected for handling the storage workload according to the allocation plan.
摘要翻译：本发明的实施例提供了一种以能量效率方式为不同工作负载提供存储资源（例如，跨企业存储系统（ESS），诸如通用并行文件系统（GPFS）等）的方法。该系统评估存储设备的不同能量简档/工作负载的能量消耗特征，以确定降低能量成本的分配计划（例如，导致用于处理存储工作负载的最低成本/能量消耗）。在典型的实施例中，将确定用于处理特定存储工作负载的能量消耗特性。此后，可以确定能够处理工作量的一种存储装置。然后，将开发出一种能够最有效地处理工作负载能耗的分配计划。一般来说，分配方案是基于能量消耗特性和能量效率算法。能源效率算法用于识别能够处理工作负载的存储设备，以减少总能量消耗，并因此降低成本。除此之外，能源效率算法还可以考虑其他因素，例如存储设备的容量和负载以及服务水平协议（SLA）术语以及能量成本（例如，超过一天和/或几周的时间）。在任何情况下，可以根据分配计划选择至少一个存储设备来处理存储工作负载。

4. 发明申请

US20120042055A1 END-TO-END PROVISIONING OF STORAGE CLOUDS 失效
标题翻译：存储云的端到端提供
公开(公告)号：US20120042055A1
公开(公告)日：2012-02-16
申请号：US12857005
申请日：2010-08-16
申请人： Sandip Agarwala , Richard J. Ayala, JR. , Kavita Chavda , Sandeep Goplsetty
发明人： Sandip Agarwala , Richard J. Ayala, JR. , Kavita Chavda , Sandeep Goplsetty
IPC分类号： G06F15/177
CPC分类号： G06F15/177 , G06F3/0605 , G06F3/0629 , G06F3/067 , G06F9/5061 , H04L41/5054 , H04L67/1097
摘要： Embodiments discussed in this disclosure provide an integrated provisioning framework that automates the process of provisioning storage resources, end-to-end, for an enterprise storage cloud environment. Such embodiments configure and orchestrate the deployment of a user's workload and, at the same time, provide optimization across a multitude of storage cloud resources. Along these lines, input is received in the form of workload requirements and configuration information for available system resources. Based on the input, a set (at least one) of storage cloud configuration plans is developed that satisfy the workload requirements. A set of scripts is then generated that orchestrate the deployment and configuration of different software and hardware components based on the plans.
摘要翻译：在本公开中讨论的实施例提供了一种集成供应框架，其自动化为企业存储云环境提供端到端的存储资源的过程。这样的实施例配置和协调用户工作负载的部署，并且同时在多个存储云资源上提供优化。沿着这些方式，以可用系统资源的工作负载需求和配置信息的形式接收输入。基于输入，开发满足工作负载要求的一组（至少一个）存储云配置计划。然后生成一组脚本，根据计划编排不同软件和硬件组件的部署和配置。

5. 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.

6. 发明授权

US08918439B2 Data lifecycle management within a cloud computing environment 有权
标题翻译：云计算环境中的数据生命周期管理
公开(公告)号：US08918439B2
公开(公告)日：2014-12-23
申请号：US12817245
申请日：2010-06-17
申请人： Gabriel Alatorre , Richard Ayala , Kavita Chavda , Sandeep Gopisetty , Aameek Singh
发明人： Gabriel Alatorre , Richard Ayala , Kavita Chavda , Sandeep Gopisetty , Aameek Singh
IPC分类号： G06F12/00 , G06F7/00 , G06F17/00 , G06F17/30 , G06F15/173 , G06F9/455
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。

7. 发明申请

US20120011316A1 INTELLIGENT STORAGE PROVISIONING WITHIN A CLUSTERED COMPUTING ENVIRONMENT 有权
标题翻译：在集群计算环境中的智能存储提供
公开(公告)号：US20120011316A1
公开(公告)日：2012-01-12
申请号：US12831455
申请日：2010-07-07
申请人： Kavita Chavda , David P. Goodman , Sandeep Gopisetty , Sechashayee S. Murthy , Aameek Singh
发明人： Kavita Chavda , David P. Goodman , Sandeep Gopisetty , Sechashayee S. Murthy , Aameek Singh
IPC分类号： G06F12/02
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。

8. 发明申请

US20120110260A1 AUTOMATED STORAGE PROVISIONING WITHIN A CLUSTERED COMPUTING ENVIRONMENT 有权
标题翻译：在集群计算环境中自动存储提供
公开(公告)号：US20120110260A1
公开(公告)日：2012-05-03
申请号：US12915153
申请日：2010-10-29
申请人： Kavita Chavda , David P. Goodman , Sandeep Gopisetty , Larry S. McGimsey , James E. Olson , Aameek Singh
发明人： Kavita Chavda , David P. Goodman , Sandeep Gopisetty , Larry S. McGimsey , James E. Olson , Aameek Singh
IPC分类号： G06F12/00
CPC分类号： G06F3/0689 , G06F3/0605 , G06F3/0619 , G06F3/0665 , G06F3/067
摘要： Embodiments of the present invention provide an approach for automatic storage planning and provisioning within a clustered computing environment (e.g., a cloud computing environment). Specifically, embodiments of the present invention will receive planning input for a set of storage area network volume controllers (SVCs) within the clustered computing environment, the planning input indicating a potential load on the SVCs and its associated components. Along these lines, analytical models (e.g., from vendors) can be also used that allow for a load to be accurately estimated on the storage components. Regardless, configuration data for a set of storage components (i.e., the set of SVCs, a set of managed disk (Mdisk) groups associated with the set of SVCs, and a set of backend storage systems) will also be collected. Based on this configuration data, the set of storage components will be filtered to identify candidate storage components capable of addressing the potential load. Then, performance data for the candidate storage components will be analyzed to identify an SVC and an Mdisk group to address the potential load. This allows for storage provisioning planning to be automated in a highly accurate fashion.
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）内的自动存储规划和供应的方法。具体地，本发明的实施例将接收针对集群计算环境内的一组存储区域网络卷控制器（SVC）的规划输入，规划输入指示SVC及其相关组件上的潜在负载。沿着这些线路，还可以使用分析模型（例如，来自供应商），允许在存储组件上准确地估计负载。无论如何，还将收集一组存储组件（即，一组SVC，与该组SVC相关联的一组受管理磁盘（Mdisk）组）和一组后端存储系统的配置数据。基于该配置数据，将对该组存储组件进行滤波以识别能够寻址潜在负载的候选存储组件。然后，将分析候选存储组件的性能数据，以识别SVC和Mdisk组以解决潜在负载。这使得存储配置计划能够以高度精确的方式自动化。

9. 发明申请

US20110314069A1 DATA LIFECYCLE MANAGEMENT WITHIN A CLOUD COMPUTING ENVIRONMENT 有权
标题翻译：数据生命周期管理在云计算环境中
公开(公告)号：US20110314069A1
公开(公告)日：2011-12-22
申请号：US12817245
申请日：2010-06-17
申请人： Gabriel Alatorre , Richard Ayala , Kavita Chavda , Sandeep Gopisetty , Aameek Singh
发明人： Gabriel Alatorre , Richard Ayala , Kavita Chavda , Sandeep Gopisetty , Aameek Singh
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。

10. 发明授权

US09106675B2 Intelligent network storage planning within a clustered computing environment 有权
标题翻译：集群计算环境中的智能网络存储规划
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。

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