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    Detection of abnormal resource usage in a data center
    2.
    发明授权
    Detection of abnormal resource usage in a data center 有权

    公开(公告)号：US10402244B2

    公开(公告)日：2019-09-03

    申请号：US15385718

    申请日：2016-12-20

    Applicant: Microsoft Technology Licensing, LLC

    Inventor： Hani Neuvirth-Telem ,  Amit Hilbuch ,  Shay Baruch Nahum ,  Yehuda Finkelstein ,  Daniel Alon ,  Elad Yom-Tov

    IPC: G06F11/00 ,  G06F11/34 ,  G06F11/30 ,  G06N20/00 ,  G06N5/04

    Abstract: A system for identifying abnormal resource usage in a data center is provided. In some embodiments, the system employs a prediction model for each of a plurality of resources and an abnormal resource usage criterion. For each of a plurality of resources of the data center, the system retrieves current resource usage data for a current time and past resource usage data for that resource. The system then extracts features from the past resource usage data for that resource, predicts using the prediction model for that resource usage data for the current time based on the extracted features, and determines an error between the predicted resource usage data and the current resource usage data. After determining the error data for the resources, the system determines whether errors satisfy the abnormal resource usage criterion. If so, the system indicates that an abnormal resource usage has occurred.

    Multilingual content based recommendation system
    3.
    发明授权
    Multilingual content based recommendation system 有权

    公开(公告)号：US09898773B2

    公开(公告)日：2018-02-20

    申请号：US14546719

    申请日：2014-11-18

    Applicant: Microsoft Technology Licensing, LLC

    Inventor： Nir Nice ,  Noam Koenigstein ,  Shay Ben-Elazar ,  Shahar Keren ,  Ulrich Paquet ,  Yehuda Finkelstein

    IPC: G06F17/30 ,  G06F17/27 ,  G06Q30/06

    CPC classification number: G06Q30/0631 ,  G06F17/2785 ,  G06F17/30616 ,  G06F17/30684

    Abstract: Example apparatus and methods access multiple sources of information concerning features for applications, clean the data from the multiple sources, extract features from the cleaned data, selectively weight the sources, data or extracted features and produce a feature vector. The feature vector may then be used in a single language feature space or in a multi-language feature space. Feature spaces may then be used to find similarities between applications to facilitate recommending applications. In one embodiment, different feature spaces may be connected using a graph where nodes represent items and edges represent similarity relationships between items based on related feature spaces. Traversing the graph may allow similarities to be found that might not otherwise be possible. For example, while there may be no direct English to Hebrew similarity relationship, there may be English to French and French to Hebrew relationships that can be followed in the graph.

    DETECTION OF ABNORMAL RESOURCE USAGE IN A DATA CENTER
    5.
    发明申请
    DETECTION OF ABNORMAL RESOURCE USAGE IN A DATA CENTER 有权
    检测数据中心异常资源使用情况

    公开(公告)号：US20160350198A1

    公开(公告)日：2016-12-01

    申请号：US14721777

    申请日：2015-05-26

    Applicant: Microsoft Technology Licensing, LLC

    Inventor： Hani Neuvirth-Telem ,  Amit Hilbuch ,  Shay Baruch Nahum ,  Yehuda Finkelstein ,  Daniel Alon ,  Elad Yom-Tov

    IPC: G06F11/34 ,  G06F11/30

    CPC classification number: G06F11/006 ,  G06F11/00 ,  G06F11/3051 ,  G06F11/3447 ,  G06F11/3452 ,  G06F2201/86 ,  G06N5/04 ,  G06N99/005

    Abstract: A system for identifying abnormal resource usage in a data center is provided. In some embodiments, the system employs a prediction model for each of a plurality of resources and an abnormal resource usage criterion. For each of a plurality of resources of the data center, the system retrieves current resource usage data for a current time and past resource usage data for that resource. The system then extracts features from the past resource usage data for that resource, predicts using the prediction model for that resource usage data for the current time based on the extracted features, and determines an error between the predicted resource usage data and the current resource usage data. After determining the error data for the resources, the system determines whether errors satisfy the abnormal resource usage criterion. If so, the system indicates that an abnormal resource usage has occurred.

    Abstract translation: 提供了用于识别数据中心中的异常资源使用的系统。 在一些实施例中，系统针对多个资源中的每个资源和异常资源使用准则采用预测模型。 对于数据中心的多个资源中的每一个，系统检索当前时间的当前资源使用数据和该资源的过去资源使用数据。 系统然后从该资源的过去资源使用数据中提取特征，基于所提取的特征，预测当前时间使用该资源使用数据的预测模型，并且确定预测资源使用数据与当前资源使用之间的误差 数据。 在确定资源的错误数据后，系统确定错误是否满足异常资源使用准则。 如果是这样，系统表示资源使用异常。

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