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    Coflow identification method and system, and server using method
    1.
    发明授权
    Coflow identification method and system, and server using method 有权

    公开(公告)号：US10567299B2

    公开(公告)日：2020-02-18

    申请号：US16127649

    申请日：2018-09-11

    Applicant: HUAWEI TECHNOLOGIES CO.,LTD.

    Inventor： Zhitang Chen ,  Yanhui Geng ,  Hong Zhang ,  Kai Chen

    IPC: H04L29/06 ,  H04L12/891 ,  G06F16/00 ,  G06N7/00 ,  G06N20/00 ,  H04L12/851 ,  H04L12/927

    Abstract: A coflow identification method includes: obtaining a weighted matrix by means of learning according to historical data in the network, where the weighted matrix is used to minimize a feature distance between data streams belonging to a same coflow and maximize a feature distance between data streams belonging to different coflows; computing a feature distance between any two data streams in the network according to metrics in the data stream layer data feature, the application layer data stream feature distance, the terminal aspect data feature distance, and the weighted matrix; and dividing the data streams in the network into several cluster sets by using a clustering algorithm and according to the feature distance between the any two data streams in the network, where each of the several cluster sets is a coflow.

    Data transmission method, and switch and network control system using the method
    2.
    发明授权
    Data transmission method, and switch and network control system using the method 有权

    公开(公告)号：US11159432B2

    公开(公告)日：2021-10-26

    申请号：US15879452

    申请日：2018-01-25

    Applicant: HUAWEI TECHNOLOGIES CO., LTD.

    Inventor： Zhitang Chen ,  Fred Chi Hang Fung ,  Yanhui Geng

    IPC: H04L12/851 ,  H04L12/753 ,  H04L12/931 ,  H04L12/751 ,  H04L12/715 ,  H04L12/803 ,  H04L12/24

    Abstract: A network data transmission method is provided. A switch device receives one or more data flows, classifies each of the received data flows into one of two classes according to data features of the data flow by using a decision tree model established by a flow table pipeline of the switch device. If a data flow belongs to a first class, the switch device reports the data flow to a controller, so that the controller computes a transmission path for the data flow. If a data flow belongs to a second class, the switch device obtains a transmission path for the data flow according to local flow table information, and transmits the data flow according to the obtained transmission path. Data flows are classified and filtered by using a switch, so as to improve network transmission efficiency while ensuring bearing capability of a network control system.

    Service survivability analysis method and apparatus
    3.
    发明授权
    Service survivability analysis method and apparatus 有权

    公开(公告)号：US11108619B2

    公开(公告)日：2021-08-31

    申请号：US16514261

    申请日：2019-07-17

    Applicant: HUAWEI TECHNOLOGIES CO., LTD.

    Inventor： Zhitang Chen ,  Qibin Wu ,  Yanhui Geng

    IPC: H04L12/24

    Abstract: Embodiments of this application provide a service survivability analysis method and apparatus, and relate to the field of communications technologies, so as to shorten duration of service survivability analysis and improve efficiency of the service survivability analysis. The method includes: obtaining a link fault record and network topology information that are in a preset time period; determining a similarity between any two links in all faulty links based on fault occurrence time and fault removal time of the any two links in the link fault record and connection information of network devices on the any two links, to obtain a link similarity matrix; performing clustering on all the faulty links based on the link similarity matrix, to obtain at least one link cluster; and performing survivability analysis on services on at least two preset links based on each of the at least one link cluster.

    Network data flow classification method and system
    5.
    发明授权
    Network data flow classification method and system 有权

    公开(公告)号：US10999175B2

    公开(公告)日：2021-05-04

    申请号：US16362135

    申请日：2019-03-22

    Applicant: Huawei Technologies Co., Ltd.

    Inventor： Zhitang Chen ,  Yanhui Geng ,  Georgios Trimponias

    IPC: H04L12/26 ,  H04L12/24 ,  H04L12/851 ,  H04L29/08 ,  G06K9/62

    Abstract: A network data flow classification method related to artificial intelligence includes collecting an information set, including a plurality of pieces of dimension information, of a to-be-processed data flow, establishing a static behavior model and a dynamic behavior model of each piece of dimension information in the information set, where the static behavior model represents a value selection rule of the dimension information, and the dynamic behavior model represents a correlation relationship of the dimension information between two adjacent time moments, obtaining, using the static behavior model and the dynamic behavior model respectively, a static model distance and a dynamic model distance between the to-be-processed data flow and a data flow of each target application type, determining an application type of the to-be-processed data flow based on the static model distance and the dynamic model distance.

    Network Data Flow Classification Method and System
    6.
    发明申请
    Network Data Flow Classification Method and System 审中-公开

    公开(公告)号：US20190222499A1

    公开(公告)日：2019-07-18

    申请号：US16362135

    申请日：2019-03-22

    Applicant: Huawei Technologies Co., Ltd.

    Inventor： Zhitang Chen ,  Yanhui Geng ,  Trimponias Georgios

    IPC: H04L12/26

    CPC classification number: H04L43/0888 ,  G06K9/62 ,  H04L29/08

    Abstract: A network data flow classification method related to artificial intelligence includes collecting an information set, including a plurality of pieces of dimension information, of a to-be-processed data flow, establishing a static behavior model and a dynamic behavior model of each piece of dimension information in the information set, where the static behavior model represents a value selection rule of the dimension information, and the dynamic behavior model represents a correlation relationship of the dimension information between two adjacent time moments, obtaining, using the static behavior model and the dynamic behavior model respectively, a static model distance and a dynamic model distance between the to-be-processed data flow and a data flow of each target application type, determining an application type of the to-be-processed data flow based on the static model distance and the dynamic model distance.

    Network service transmission method and system
    7.
    发明授权
    Network service transmission method and system 有权

    公开(公告)号：US11252077B2

    公开(公告)日：2022-02-15

    申请号：US16569239

    申请日：2019-09-12

    Applicant: Huawei Technologies Co., Ltd.

    Inventor： Georgios Trimponias ,  Hong Xu ,  Zhitang Chen

    IPC: H04L12/721 ,  H04L12/24 ,  H04L12/707 ,  H04L12/803

    Abstract: A network service transmission method includes obtaining network topology information and network service information, and determining a node centrality of each node in a set of other nodes; determining at least one segment node in the set of other nodes, determining at least one transmission path used to transmit each network service; and determining traffic of a network service that is to be transmitted on the at least one transmission path used to transmit the network service. After the segment node is determined, traffic of a network service transmitted on each transmission path is determined, and the transmission paths of the network services share the same segment node.

    Method and apparatus for detecting type of network data flow
    9.
    发明授权
    Method and apparatus for detecting type of network data flow 有权

    公开(公告)号：US10333854B2

    公开(公告)日：2019-06-25

    申请号：US15465757

    申请日：2017-03-22

    Applicant: Huawei Technologies Co., Ltd.

    Inventor： Zhitang Chen ,  Yanhui Geng ,  Pascal Poupart

    IPC: G06F7/556 ,  H04L29/06 ,  H04L12/721 ,  H04L12/26 ,  H04L12/851

    Abstract: A method for detecting a data flow type includes obtaining a header of a first data packet of a current data flow and a pattern vector of the current data flow from the header; comparing the at least one feature dimension in the pattern vector of the current data flow with a corresponding feature dimension in a pattern vector of at least one historical data flow, so as to obtain at least one pattern similarity of the current data flow; predicting a length of the current data flow according to the at least one pattern similarity of the current data flow and a length of the corresponding at least one historical data flow; and comparing the predicted length of the current data flow with a preset threshold, and determining whether the current data flow is a large data flow or a small data flow according to a comparison result.

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