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    • 7. 发明授权
    • Method and system for detecting changes in network performance
    • 检测网络性能变化的方法和系统
    • US08774023B2
    • 2014-07-08
    • US12887903
    • 2010-09-22
    • Jia WangZihui GeAjay MahimkarAman ShaikhJennifer YatesYin ZhangJoanne Emmons
    • Jia WangZihui GeAjay MahimkarAman ShaikhJennifer YatesYin ZhangJoanne Emmons
    • H04L12/26
    • H04L43/16H04L41/082H04L43/0829H04L43/0852
    • A system and method are provided for identifying a change point in a set of data. The system performs the method by receiving a set of data. The data indicates a plurality of performance measurements from a measurement point in a network. Each of the plurality of measurements represents a single type of performance measurement made at the measurement point at each of a corresponding plurality of points in time. The method also includes dividing the set of data into a plurality of data points in a chronological order. Each data point has a value corresponding to the performance measurements. The method also includes ranking the data points in an ascending order, calculating a cumulative sum for each of the data points, calculating a change score for the set of data points. A change point is identified in the data set if the change score exceeds a predetermined confidence level.
    • 提供了用于识别一组数据中的变化点的系统和方法。 系统通过接收一组数据来执行该方法。 数据表示来自网络中的测量点的多个性能测量。 多个测量中的每一个表示在相应的多个时间点的每一个处的测量点进行的单一类型的性能测量。 该方法还包括按照时间顺序将数据集合划分成多个数据点。 每个数据点都具有对应于性能测量值。 该方法还包括以升序对数据点进行排序,计算每个数据点的累积和,计算该组数据点的改变得分。 如果改变得分超过预定的置信水平,则在数据集中识别变化点。
    • 10. 发明申请
    • Measurement-Based Validation of a Simple Model for Panoramic Profiling of Subnet-Level Network Data Traffic
    • 基于测量的验证子网级数据流量全景分析的简单模型
    • US20100034102A1
    • 2010-02-11
    • US12186113
    • 2008-08-05
    • Jia WangZihui GeHongbo JiangShudong Jin
    • Jia WangZihui GeHongbo JiangShudong Jin
    • H04L12/26
    • H04L43/0876H04L41/0213H04L41/0893
    • A system and method for profiling subnet-level aggregate network data traffic is disclosed. The system allows a user to define a collection of features that combined characterize the subnet-level aggregate traffic behavior. Preferably, the features include daily traffic volume, time-of-day behavior, spatial traffic distribution, traffic balance in flow direction, and traffic distribution in type of application. The system then applies machine learning techniques to classify the subnets into a number of clusters on each of the features, by assigning a membership probability vector to each network thus allowing panoramic traffic profiles to be created for each network on all features combined. These membership probability vectors may optionally be used to detect network anomalies, or to predict future network traffic.
    • 公开了一种用于分析子网级聚合网络数据流量的系统和方法。 该系统允许用户定义组合的特征集合,以表征子网级聚合流量行为。 优选地,特征包括日常交通量,时间行为,空间流量分配,流向方向上的流量平衡以及应用类型中的流量分布。 然后,该系统应用机器学习技术,通过向每个网络分配成员概率向量,从而允许在组合的所有特征上为每个网络创建全景业务简档,从而将子网分类为每个特征上的多个集群。 这些成员概率向量可以可选地用于检测网络异常,或者预测未来的网络流量。