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    • 21. 发明申请
    • System and method for proactive impact analysis of policy-based storage systems
    • 基于策略的存储系统的主动影响分析系统和方法
    • US20070112870A1
    • 2007-05-17
    • US11281739
    • 2005-11-16
    • Madhukar KorupoluAameek SinghKaladhar Voruganti
    • Madhukar KorupoluAameek SinghKaladhar Voruganti
    • G06F17/30
    • G06F3/0637G06F3/0605G06F3/067
    • A system efficiently and proactively assesses the impact of user's actions on a network storage system. The system generally operates on a storage area network that includes a database represented by states and policies, before the user action is executed. The system comprises a storage monitor that captures a snapshot of the database states. An impact analysis module of the system then applies a user action to the snapshot; and further selectively applies at least some of the policies to the snapshot. The impact analysis module simulates the user action on the snapshot without applying actually changes to the database, and further analyzes whether the simulated user action violates at least one applied policy. The system takes the appropriate action based on the result of the analysis.
    • 系统有效地主动评估用户对网络存储系统的影响。 在执行用户操作之前,系统通常在包括由状态和策略表示的数据库的存储区域网络上操作。 该系统包括捕获数据库状态的快照的存储监视器。 然后系统的影响分析模块将用户操作应用于快照; 并进一步选择性地将快照中的至少一些策略应用于快照。 影响分析模块模拟快照上的用户操作,而不对数据库进行实际更改,并进一步分析模拟用户操作是否违反至少一个应用策略。 系统根据分析结果采取适当的措施。
    • 22. 发明授权
    • Methods, systems, and computer program products for disaster recovery planning
    • 用于灾难恢复规划的方法,系统和计算机程序产品
    • US07945537B2
    • 2011-05-17
    • US12126487
    • 2008-05-23
    • Srinivasan BalasubramanianTushar MohanRoberto C. PineiroRohit JainRamani R. RoutrayGauri ShahAkshat VermaKaladhar Voruganti
    • Srinivasan BalasubramanianTushar MohanRoberto C. PineiroRohit JainRamani R. RoutrayGauri ShahAkshat VermaKaladhar Voruganti
    • G06F17/30
    • G06F11/20
    • Formulating an integrated disaster recovery (DR) plan based upon a plurality of DR requirements for an application by receiving a first set of inputs identifying one or more entity types for which the plan is to be formulated, such as an enterprise, one or more sites of the enterprise, the application, or a particular data type for the application. At least one data container representing a subset of data for an application is identified. A second set of inputs is received identifying at least one disaster type for which the plan is to be formulated. A third set of inputs is received identifying a DR requirement for the application as a category of DR Quality of Service (QoS) class to be applied to the disaster type. A composition model is generated specifying one or more respective DR QoS parameters as a function of a corresponding set of one or more QoS parameters representative of a replication technology solution. The replication technology solution encompasses a plurality of storage stack levels. A solution template library is generated for mapping the application to each of a plurality of candidate replication technology solutions. The template library is used to select a DR plan in the form of a replication technology solution for the application.
    • 基于针对应用的多个DR需求来制定综合灾难恢复(DR)计划,所述DR要求通过接收标识要为其制定所述计划的一个或多个实体类型的第一组输入,诸如企业,一个或多个站点 的应用程序,或应用程序的特定数据类型。 识别表示应用程序的数据子集的至少一个数据容器。 接收第二组输入,确定要制定该计划的至少一种灾害类型。 接收到第三组输入,将应用程序的DR要求标识为要应用于灾难类型的DR服务质量(QoS)类别。 生成指定作为代表复制技术解决方案的一个或多个QoS参数的相应组的函数的一个或多个相应DR QoS参数的组合模型。 复制技术解决方案包含多个存储堆栈级别。 生成解决方案模板库,用于将应用程序映射到多个候选复制技术解决方案中的每一个。 模板库用于以应用程序的复制技术解决方案的形式选择DR计划。
    • 27. 发明授权
    • Modeling storage system performance
    • 建模存储系统性能
    • US09514022B1
    • 2016-12-06
    • US13275607
    • 2011-10-18
    • Jayanta BasakKaladhar VorugantiSiddhartha Nandi
    • Jayanta BasakKaladhar VorugantiSiddhartha Nandi
    • G06F11/34
    • G06F11/3447G06F11/00
    • A system and method for creating an accurate black-box model of a live storage system and for predicting performance of the storage system under a given workload is disclosed. An analytics engine determines a subset of counters that are relevant to performance of the storage system with respect to a particular output (e.g., throughput or latency) from performance data in counters of the storage system. Using the subset of counters, the analytics engine creates a workload signature for the storage system by using a recursive partitioning technique, such as a classification and regression tree. The analytics engine then creates the black-box model of the storage system performance by applying uncertainty measurement techniques, such as a Gaussian process, to the workload signature.
    • 公开了一种用于创建实时存储系统的精确黑箱模型并用于在给定工作负载下预测存储系统的性能的系统和方法。 分析引擎确定与存储系统的计数器中的性能数据相关的特定输出(例如,吞吐量或延迟)与存储系统的性能相关的计数器的子集。 使用计数器子集,分析引擎通过使用递归分区技术(如分类和回归树)为存储系统创建工作负载签名。 然后,分析引擎通过对工作负载签名应用不确定性测量技术(例如高斯过程)来创建存储系统性能的黑盒模型。
    • 28. 发明授权
    • Modeler for predicting storage metrics
    • 用于预测存储指标的建模器
    • US08620921B1
    • 2013-12-31
    • US13016892
    • 2011-01-28
    • Sai Rama Krishna SusarlaKaladhar VorugantiVipul Mathur
    • Sai Rama Krishna SusarlaKaladhar VorugantiVipul Mathur
    • G06F17/30
    • G06N99/005G06F11/3409G06F17/30294G06F17/30587
    • Described herein is a system and method for dynamically managing service-level objectives (SLOs) for workloads of a cluster storage system. Proposed states/solutions of the cluster may be produced and evaluated to select one that achieves the SLOs for each workload. A planner engine may produce a state tree comprising nodes, each node representing a proposed state/solution. New nodes may be added to the state tree based on new solution types that are permitted, or nodes may be removed based on a received time constraint for executing a proposed solution or a client certification of a solution. The planner engine may call an evaluation engine to evaluate proposed states, the evaluation engine using an evaluation function that considers SLO, cost, and optimization goal characteristics to produce a single evaluation value for each proposed state. The planner engine may call a modeler engine that is trained using machine learning techniques.
    • 这里描述了用于动态管理用于集群存储系统的工作负载的服务级目标(SLO)的系统和方法。 可以生成和评估集群的建议状态/解决方案,以选择为每个工作负载实现SLO的状态/解决方案。 计划器引擎可以产生包括节点的状态树,每个节点表示提出的状态/解。 可以基于允许的新解决方案类型将新节点添加到状态树,或者可以基于接收到的时间约束来移除节点,以执行解决方案或解决方案的客户端认证。 计划器引擎可以调用评估引擎来评估提出的状态,评估引擎使用考虑SLO,成本和优化目标特征的评估函数,以产生每个建议状态的单个评估值。 计划器引擎可以调用使用机器学习技术训练的建模者引擎。
    • 29. 发明授权
    • Software module for using flash memory as a secondary permanent storage device
    • 使用闪存作为辅助永久存储设备的软件模块
    • US08499132B1
    • 2013-07-30
    • US12030168
    • 2008-02-12
    • Shankar PasupathyGarth GoodsonKaladhar VorugantiKiran Srinivasan
    • Shankar PasupathyGarth GoodsonKaladhar VorugantiKiran Srinivasan
    • G06F12/02
    • G06F3/061G06F3/0631G06F3/0685G06F12/0246G06F2212/7201Y02D10/13
    • Described herein is a flash remapping (FR) layer in a storage operating system for utilizing flash memory as a secondary permanent storage device in a storage system. The FR layer collects particular information (specified by collection parameters) of received access requests for data stored on primary storage devices of the storage system. Based on the collected information and a predetermined access pattern (specified by pattern parameters), the FR layer selects data sets on the primary storage devices to be transferred permanently to flash memory, whereby subsequent access requests to the selected data sets are redirected to flash memory. New parameters may be received by the FR layer (from a user or program) to dynamically reconfigure the functions of the FR layer. The FR layer may be implemented in the operating system without requiring other code of the storage operating system to be modified.
    • 这里描述的是在存储操作系统中的闪存重映射(FR)层,用于在存储系统中利用闪存作为辅助永久存储设备。 FR层收集存储在存储系统的主存储设备上的数据的接收到的访问请求的特定信息(由收集参数指定)。 基于所收集的信息和预定的访问模式(由模式参数指定),FR层选择将主存储设备上的数据集永久地传送到闪存,由此对所选数据集的后续访问请求被重定向到闪存 。 可以由FR层(来自用户或程序)接收新参数以动态地重新配置FR层的功能。 可以在操作系统中实现FR层,而不需要修改存储操作系统的其他代码。