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1.发明授权公开(公告)号：US08756310B2
公开(公告)日：2014-06-17
申请号：US13044048
申请日：2011-03-09
IPC分类号： G06F15/173
CPC分类号： H04L47/11 , G06F9/5072 , G06F11/3442 , G06F11/3495 , H04L41/147 , H04L43/0882
摘要： Embodiments of the present invention provide approaches (e.g., online methods) to analyze end-to-end performance issues in a multi-tier enterprise storage system (ESS), such as a storage cloud, where data may be distributed across multiple storage components. Specifically, performance and configuration data from different storage components (e.g., nodes) is collected and analyzed to identify nodes that are becoming (or may become) performance bottlenecks. In a typical embodiment, a set of components distributed among a set of tiers of an ESS is identified. For each component, a total capacity and a current load are determined. Based on these values, a utilization of each component is determined. Comparison of the utilization with a predetermined threshold and/or analysis of historical data allows one or more components causing a bottleneck to be identified.
摘要翻译： 本发明的实施例提供了分析多层企业存储系统（ESS）（诸如存储云）中的端到端性能问题的方法（例如在线方法），其中数据可以分布在多个存储组件上。 具体地，收集和分析来自不同存储组件（例如，节点）的性能和配置数据以识别正在（或可能成为）性能瓶颈的节点。 在典型的实施例中，识别分布在一组ESS中的一组分量。 对于每个组件，确定总容量和当前负载。 基于这些值，确定每个组件的利用率。 将利用率与预定阈值进行比较和/或历史数据分析允许识别出瓶颈的一个或多个组件。
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2.发明申请公开(公告)号：US20120233310A1
公开(公告)日：2012-09-13
申请号：US13044048
申请日：2011-03-09
IPC分类号： G06F15/173
CPC分类号： H04L47/11 , G06F9/5072 , G06F11/3442 , G06F11/3495 , H04L41/147 , H04L43/0882
摘要： Embodiments of the present invention provide approaches (e.g., online methods) to analyze end-to-end performance issues in a multi-tier enterprise storage system (ESS), such as a storage cloud, where data may be distributed across multiple storage components. Specifically, performance and configuration data from different storage components (e.g., nodes) is collected and analyzed to identify nodes that are becoming (or may become) performance bottlenecks. In a typical embodiment, a set of components distributed among a set of tiers of an ESS is identified. For each component, a total capacity and a current load are determined. Based on these values, a utilization of each component is determined. Comparison of the utilization with a predetermined threshold and/or analysis of historical data allows one or more components causing a bottleneck to be identified.
摘要翻译： 本发明的实施例提供了分析多层企业存储系统（ESS）（诸如存储云）中的端到端性能问题的方法（例如在线方法），其中数据可以分布在多个存储组件上。 具体地，收集和分析来自不同存储组件（例如，节点）的性能和配置数据以识别正在（或可能成为）性能瓶颈的节点。 在典型的实施例中，识别分布在一组ESS中的一组分量。 对于每个组件，确定总容量和当前负载。 基于这些值，确定每个组件的利用率。 将利用率与预定阈值进行比较和/或历史数据分析允许识别出瓶颈的一个或多个组件。
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公开(公告)号：US08554917B2
公开(公告)日：2013-10-08
申请号：US12859788
申请日：2010-08-20
IPC分类号： G06F15/173
CPC分类号： G06F3/0689 , G06F3/0619 , G06F3/0665 , G06F9/5072
摘要： Embodiments of the present invention provide performance isolation for storage clouds. Under one embodiment, workloads across a storage cloud architecture are grouped into clusters based on administrator or system input. A performance isolation domain is then created for each of the clusters, with each of the performance isolation domains comprising a set of data stores associated with a set of storage subsystems and a set of data paths that connect the set of data stores to a set of clients. Thereafter, performance isolation is provided among a set of layers of the performance isolation domains. Such performance isolation is provided by (among other things): pooling data stores from separate performance isolation domains into separate pools; assigning the pools to device adapters, RAID controller, and the set of storage subsystems; preventing workloads on the device adapters from exceeding capacities of the device adapters; mapping the set of data stores to a set of Input/Output (I/O) servers based on an I/O capacity and I/O load of the set of I/O servers; and/or pairing ports of the set of I/O servers with ports of the set of storage subsystems, the pairing being based upon availability, connectivity, I/O load, and I/O capacity.
摘要翻译： 本发明的实施例提供了用于存储云的性能隔离。 在一个实施例中，跨存储云架构的工作负载基于管理员或系统输入被分组成群集。 然后为每个集群创建性能隔离域，其中每个性能隔离域包括与一组存储子系统相关联的一组数据存储以及将该组数据存储连接到一组数据路径的一组数据路径 客户。 此后，在性能隔离域的一组层中提供性能隔离。 这种性能隔离由（除其他外）提供：将数据存储从单独的性能隔离域集中到单独的池中; 将池分配给设备适配器，RAID控制器和一组存储子系统; 防止设备适配器上的工作负载超过设备适配器的容量; 基于一组I / O服务器的I / O容量和I / O负载，将数据存储集映射到一组输入/输出（I / O）服务器; 和/或将该组I / O服务器的端口与该组存储子系统的端口配对，该配对基于可用性，连接性，I / O负载和I / O容量。
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4.发明申请公开(公告)号：US20120054181A1
公开(公告)日：2012-03-01
申请号：US12872964
申请日：2010-08-31
IPC分类号： G06F17/30
CPC分类号： G06F17/30536 , G06F17/30516
摘要： Embodiments for efficiently computing complex statistics from historical time series data are provided. A hierarchical summarization method includes receiving at least one stream of data and creating data blocks from the at least one stream of data. In another embodiment, a method for computing statistics for historical data includes accessing at least one online stream of historical data, the online stream of historical data including metadata, and creating data blocks from the at least one online stream of historical data. Each data block includes a pair of timestamps indicating a sampling start time and a sampling end time, a number of data samples spanned by the data block, a SUM(X) statistic, a SUM(XX) statistic, and a SUM(XY) statistic computed for the data samples spanned by the data block. Other methods are also presented, such as methods for efficiently and accurately calculating statistical queries regarding historical data for arbitrary time ranges, among others.
摘要翻译： 提供了从历史时间序列数据有效地计算复杂统计数据的实施例。 层次聚合方法包括从所述至少一个数据流接收至少一个数据流并创建数据块。 在另一个实施例中，一种用于计算历史数据的统计量的方法包括访问历史数据的至少一个在线流，包括元数据的历史数据的在线流，以及从至少一个历史数据的在线流创建数据块。 每个数据块包括指示采样开始时间和采样结束时间的一对时间戳，由数据块跨越的数据样本的数量，SUM（X）统计量，SUM（XX）统计量和SUM（XY） 对由数据块跨越的数据样本计算的统计量。 还提出了其他方法，例如用于有效和准确地计算关于任意时间范围的历史数据的统计查询的方法等。
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公开(公告)号：US08478845B2
公开(公告)日：2013-07-02
申请号：US12857005
申请日：2010-08-16
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。
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公开(公告)号：US20120047265A1
公开(公告)日：2012-02-23
申请号：US12859788
申请日：2010-08-20
IPC分类号： G06F15/173
CPC分类号： G06F3/0689 , G06F3/0619 , G06F3/0665 , G06F9/5072
摘要： Embodiments of the present invention provide performance isolation for storage clouds. Under one embodiment, workloads across a storage cloud architecture are grouped into clusters based on administrator or system input. A performance isolation domain is then created for each of the clusters, with each of the performance isolation domains comprising a set of data stores associated with a set of storage subsystems and a set of data paths that connect the set of data stores to a set of clients. Thereafter, performance isolation is provided among a set of layers of the performance isolation domains. Such performance isolation is provided by (among other things): pooling data stores from separate performance isolation domains into separate pools; assigning the pools to device adapters, RAID controller, and the set of storage subsystems; preventing workloads on the device adapters from exceeding capacities of the device adapters; mapping the set of data stores to a set of Input/Output (I/O) servers based on an I/O capacity and I/O load of the set of I/O servers; and/or pairing ports of the set of I/O servers with ports of the set of storage subsystems, the pairing being based upon availability, connectivity, I/O load, and I/O capacity.
摘要翻译： 本发明的实施例提供了用于存储云的性能隔离。 在一个实施例中，跨存储云架构的工作负载基于管理员或系统输入被分组成群集。 然后为每个集群创建性能隔离域，其中每个性能隔离域包括与一组存储子系统相关联的一组数据存储以及将该组数据存储连接到一组数据路径的一组数据路径 客户。 此后，在性能隔离域的一组层中提供性能隔离。 这种性能隔离由（除其他外）提供：将数据存储从单独的性能隔离域集中到单独的池中; 将池分配给设备适配器，RAID控制器和一组存储子系统; 防止设备适配器上的工作负载超过设备适配器的容量; 基于一组I / O服务器的I / O容量和I / O负载，将数据存储集映射到一组输入/输出（I / O）服务器; 和/或将该组I / O服务器的端口与该组存储子系统的端口配对，该配对基于可用性，连接性，I / O负载和I / O容量。
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7.发明授权Allocation of storage resources in a networked computing environment based on energy utilization 有权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
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。
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8.发明授权公开(公告)号：US08306953B2
公开(公告)日：2012-11-06
申请号：US12872964
申请日：2010-08-31
CPC分类号： G06F17/30536 , G06F17/30516
摘要： Embodiments for efficiently computing complex statistics from historical time series data are provided. A hierarchical summarization method includes receiving at least one stream of data and creating data blocks from the at least one stream of data. In another embodiment, a method for computing statistics for historical data includes accessing at least one online stream of historical data, the online stream of historical data including metadata, and creating data blocks from the at least one online stream of historical data. Each data block includes a pair of timestamps indicating a sampling start time and a sampling end time, a number of data samples spanned by the data block, a SUM(X) statistic, a SUM(XX) statistic, and a SUM(XY) statistic computed for the data samples spanned by the data block. Other methods are also presented, such as methods for efficiently and accurately calculating statistical queries regarding historical data for arbitrary time ranges, among others.
摘要翻译： 提供了从历史时间序列数据有效地计算复杂统计数据的实施例。 层次聚合方法包括从所述至少一个数据流接收至少一个数据流并创建数据块。 在另一个实施例中，用于计算历史数据的统计的方法包括访问历史数据的至少一个在线流，历史数据的在线流，包括元数据，以及从历史数据的至少一个在线流创建数据块。 每个数据块包括指示采样开始时间和采样结束时间的一对时间戳，由数据块跨越的数据样本的数量，SUM（X）统计量，SUM（XX）统计量和SUM（XY） 对由数据块跨越的数据样本计算的统计量。 还提出了其他方法，例如用于有效和准确地计算关于任意时间范围的历史数据的统计查询的方法等。
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9.发明申请ALLOCATION OF STORAGE RESOURCES IN A NETWORKED COMPUTING ENVIRONMENT BASED ON ENERGY UTILIZATION 有权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
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。
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公开(公告)号：US20120271678A1
公开(公告)日：2012-10-25
申请号：US13541440
申请日：2012-07-03
IPC分类号： G06Q10/06
CPC分类号： G06Q30/04 , G06Q10/06 , G06Q30/0283
摘要： Minimizing cost chargeback in an information technology (IT) computing environment including multiple resources. One implementation involves determining time-based usage patterns and allocation statistics for a plurality of resources and associated resource workloads. Using a regression function for determining a correlation of response time with resource usages and outstanding input/output instructions for the plurality of resources. Based on the time-based usage patterns, allocation statistics and the correlation, deriving an interpolation using positive and negative integrals to minimize a difference between allocated resource values and average allocation values. Determining service level objectives (SLOs) and resource allocation for minimizing cost chargeback for the resource workloads based on the derived interpolation.
摘要翻译： 在包括多种资源在内的信息技术（IT）计算环境中最大限度地降低成本。 一个实现涉及为多个资源和相关联的资源工作负载确定基于时间的使用模式和分配统计。 使用回归函数来确定响应时间与资源使用的相关性以及用于多个资源的未完成的输入/输出指令。 基于时间使用模式，分配统计和相关性，使用正负积分导出内插，以最小化分配的资源值与平均分配值之间的差异。 确定服务水平目标（SLO）和资源分配，以根据导出的插值来最大限度地降低资源负担的成本。
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