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    DYNAMIC INSTALLATION AND MANAGEMENT OF VIRTUAL CACHING APPLICANCES
    22.
    发明申请
    DYNAMIC INSTALLATION AND MANAGEMENT OF VIRTUAL CACHING APPLICANCES 审中-公开
    虚拟缓存应用程序的动态安装和管理

    公开(公告)号：US20160170794A1

    公开(公告)日：2016-06-16

    申请号：US15048907

    申请日：2016-02-19

    Applicant: NetApp, Inc.

    Inventor： Lakshmi Narayanan Bairavasundaram ,  Gokul Soundararajan ,  Vipul Mathur ,  Kaladhar Voruganti

    IPC: G06F9/455 ,  G06F9/50 ,  H04L29/08

    CPC classification number: G06F9/45558 ,  G06F9/5016 ,  G06F9/5077 ,  G06F9/5083 ,  G06F11/073 ,  G06F11/3409 ,  G06F12/0806 ,  G06F2009/4557 ,  G06F2009/45583 ,  G06F2009/45595 ,  H04L41/0816 ,  H04L41/0896 ,  H04L67/2842

    Abstract: It is detected that a metric associated with a first workload has breached a first threshold. It is determined that the first workload and a second workload access the same storage resources, wherein the storage resources are associated with a storage server. It is determined that the metric is impacted by the first workload and the second workload accessing the same storage resources. A candidate solution is identifier. An estimated impact of a residual workload is determined based, at least in part, on the candidate solution. A level of caching of at least one of the first workload or the second workload is adjusted based, at least in part, on the estimated impact of the residual workload.

    Abstract translation: 检测到与第一工作负荷相关联的度量已经违反了第一阈值。 确定第一工作负载和第二工作负载访问相同的存储资源，其中存储资源与存储服务器相关联。 确定该度量受到第一工作负载的影响，并且第二工作负载访问相同的存储资源。 候选解决方案是标识符。 至少部分地基于候选解决方案确定剩余工作量的估计影响。 至少部分地基于剩余工作量的估计影响来调整至少一个第一工作负载或第二工作负载的高速缓存。

    Caching data using multiple cache devices
    23.
    发明授权
    Caching data using multiple cache devices 有权
    使用多个缓存设备缓存数据

    公开(公告)号：US09274954B1

    公开(公告)日：2016-03-01

    申请号：US13718700

    申请日：2012-12-18

    Applicant: NetApp, Inc.

    Inventor： Lakshmi Narayanan Bairavasundaram ,  Gokul Soundararajan ,  Mark Walter Storer

    IPC: G06F12/08

    CPC classification number: G06F12/0806 ,  G06F3/061 ,  G06F3/0656 ,  G06F3/067 ,  G06F3/0689 ,  G06F11/073 ,  G06F11/076 ,  G06F11/1076 ,  G06F12/0815 ,  G06F12/0871 ,  G06F12/0888 ,  G06F2212/1016 ,  G06F2212/284 ,  G06F2212/461 ,  G06F2212/466 ,  G06F2212/601

    Abstract: System and method for processing cache metadata from a plurality of cache devices. One or more storage systems may store data blocks to a set of storage devices, each storage system hosting and operating one or more cache devices for caching data blocks of the storage devices. Each cache device may host and implement an agent engine that collects and stores cache metadata for the cache device, the cache metadata describing caching operations of the cache device. A monitor engine receives and aggregates “raw cache metadata” from each of a plurality of cache devices and processes the raw cache metadata to produce processed cache metadata comprising normalized cache metadata (based on activity rate of a cache device), weighted cache metadata, or normalized weighted cache metadata. The processed cache metadata is used by a cache prediction engine to select data blocks for caching to one or more of the cache devices.

    Abstract translation: 用于处理来自多个高速缓存设备的缓存元数据的系统和方法。 一个或多个存储系统可以将数据块存储到一组存储设备，每个存储系统托管和操作用于高速缓存存储设备的数据块的一个或多个高速缓存设备。 每个缓存设备可以托管和实现代理引擎，该引擎收集并存储高速缓存设备的高速缓存元数据，高速缓存元数据描述高速缓存设备的高速缓存操作。 监视器引擎从多个高速缓存设备中的每一个接收并聚合“原始高速缓存元数据”，并且处理原始高速缓存元数据以产生经处理的高速缓存元数据，其包括归一化高速缓存元数据（基于高速缓存设备的活动速率），加权高速缓存元数据 归一化加权缓存元数据。 处理的高速缓存元数据被高速缓存预测引擎用于选择用于高速缓存到一个或多个高速缓存设备的数据块。

    Managing Service Level Objectives for Storage Workloads
    24.
    发明申请
    Managing Service Level Objectives for Storage Workloads 有权
    管理存储工作负载的服务级别目标

    公开(公告)号：US20140380332A1

    公开(公告)日：2014-12-25

    申请号：US14484780

    申请日：2014-09-12

    Applicant: NETAPP, INC.

    Inventor： Vipul Mathur ,  Neeraja Yadwadkar ,  Lakshmi Narayanan Bairavasundaram ,  Rukma Ameet Talwadker ,  Kaladhar Voruganti ,  Sai Rama Krishna Susaria

    IPC: G06F9/46

    CPC classification number: H04L67/1008 ,  G06F9/465 ,  G06F11/3006 ,  G06F11/3034 ,  G06F11/3409 ,  G06F11/3442 ,  G06F11/3447 ,  G06F11/3485 ,  G06F2201/81 ,  G06F2201/86 ,  H04L12/54 ,  H04L29/06 ,  H04L41/5009 ,  H04L47/70 ,  H04L67/1097 ,  Y02D10/34

    Abstract: 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.

    Abstract translation: 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。

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