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    Modeler for predicting storage metrics
    2.
    发明授权
    Modeler for predicting storage metrics 有权
    用于预测存储指标的建模器

    公开(公告)号：US08620921B1

    公开(公告)日：2013-12-31

    申请号：US13016892

    申请日：2011-01-28

    Applicant: Sai Rama Krishna Susarla ,  Kaladhar Voruganti ,  Vipul Mathur

    Inventor： Sai Rama Krishna Susarla ,  Kaladhar Voruganti ,  Vipul Mathur

    IPC: G06F17/30

    CPC classification number: G06N99/005 ,  G06F11/3409 ,  G06F17/30294 ,  G06F17/30587

    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。

    Performance impact analysis of network change
    4.
    发明授权
    Performance impact analysis of network change 有权
    网络变化的绩效影响分析

    公开(公告)号：US08903995B1

    公开(公告)日：2014-12-02

    申请号：US13553741

    申请日：2012-07-19

    Applicant: Jayanta Basak ,  Vipul Mathur ,  Siddhartha Nandi ,  Srinivasan Narayanamurthy ,  Kaladhar Voruganti

    Inventor： Jayanta Basak ,  Vipul Mathur ,  Siddhartha Nandi ,  Srinivasan Narayanamurthy ,  Kaladhar Voruganti

    IPC: G06F15/16

    CPC classification number: G06Q10/06 ,  H04L41/0636 ,  H04L43/08

    Abstract: A network server analyzes a change in the network, including performing a machine-learning analysis of an extrapolation space. The server accesses observed data from multiple counters that each record samples for a metric in the network. The server performs a CART (classification and regression tree) analysis of the observed data to select the counters whose metrics affect a target network performance, such as latency. The server estimates an extrapolation space based on the observed data for the selected counters. The server then performs a machine-learning analysis of the extrapolation space based on a kriging model of the selected counters.

    Abstract translation: 网络服务器分析网络中的变化，包括执行外推空间的机器学习分析。 服务器访问来自多个计数器的观测数据，每个记录对网络中的度量进行记录采样。 服务器对观察到的数据执行CART（分类和回归树）分析，以选择其度量影响目标网络性能的计数器，例如延迟。 服务器根据所选计数器的观察数据估计外推空间。 然后，服务器基于所选计数器的克里金模型对外推空间进行机器学习分析。

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