Huawei Technologies Co., Limited

Li Xue

Wei Cao

Haibo Zhang

Haonan Ye

Liang Zhang

Jian Cheng

Guoyi Chen

Xin Xiao

Yuming Xie

Guofeng Qian

Guanfeng Li

Weiwang Xu

Jian Li

Jun Wu

Qiang Wang

Jian Mg Cheng

Shanshan Wang

Zongpeng Du

Jie Xiong

Youqing Yang

Ping Huang

Xinchun Guo

Yihong Huang

Yanfang Zhang

Xiaoyun Si

Zhiwang Zhao

Jinfeng Zhang

Jianzheng Zhang

Changbao Liu

Ruihong Wang

Chaowa Qiu

Jihong Mei

Ming Yang

Wei Lin

Ming Ju Chen

Chao Sun

Dewei Bao

Zhanbin Wang

Qian Xiao

Gang Li

Lv Ding

Luyou He

Zhiman Xiong

Teng Teng

Ming Chen

Tijun Shi

Xiao Wei

Qiaoling Wang

Wenyi Fang

A device identification method, apparatus, and system are provided. A management device or a collection device first determines a network traffic feature of a to-be-identified device based on a first dataset. The first dataset includes a plurality of pieces of first data, and each piece of first data includes a data amount of a data packet that is of the to-be-identified device and that is collected within one first periodicity. Then, the management device or the collection device determines a device type of the to-be-identified device based on a device identification model and the network traffic feature of the to-be-identified device.

Device identification method, apparatus, and system

A method, an apparatus, a system, a device, and a storage medium for implementing terminal verification are provided. In this method, a transmission feature of a terminal is reconstructed to verify the terminal. For example, if a difference between the transmission feature obtained through reconstruction and the transmission feature of the terminal is relatively large, it indicates that the transmission feature of the terminal is abnormal, and the terminal is an abnormal terminal. In this case, it is determined that the verification of the terminal fails.

Method, apparatus, system, device, and storage medium for implementing terminal verification

In certain embodiments, a forwarding device receives at least one service flow. The forwarding device obtains service information of the at least one service flow, where the service information of the service flow includes identification information of a network object to which the service flow belongs and M key performance indicators KPIs of the service flow. M is an integer greater than 0, and the network object includes one or more devices. The forwarding device sends detection information to a first device, where the detection information includes the service information of the at least one service flow or a feature set obtained based on the service information of the at least one service flow. The detection information is used to detect whether the network object is in a faulty state.

Fault detection method, apparatus, and system

This application provides a data collection method and a device. A data collection device determines, based on a threshold corresponding to a target indicator, a collection interval for to-be-collected data of the target indicator from a plurality of available collection intervals; and the data collection device collects the data of the target indicator based on the collection interval.

Data collection method and device

A forwarding device receives at least one service flow; the forwarding device obtains service information of the at least one service flow, where the service information of the service flow includes identification information of a network object to which the service flow belongs and M key performance indicators KPIs of the service flow, M is an integer greater than 0, and the network object includes one or more devices; and the forwarding device sends training information to a first device, where the training information includes the service information of the at least one service flow or a feature set obtained based on the service information of the at least one service flow, the training information is used to train a fault detection model, and the fault detection model is used to detect whether the network object is in a faulty state.

Fault detection model training method, apparatus, and system

A traffic anomaly detection method includes obtaining a target time series including N elements; obtaining a target parameter of the target time series, where the target parameter includes at least one of a periodic factor or a jitter density, the periodic factor represents a wave-shaped change that is presented in the target time series and that is about a long-term trend, and the jitter density represents a deviation between an actual value and a target value of the target time series within a target time; determining, from a plurality of types based on the target parameter, a first type to which the target time series belongs, where each of the types corresponds to one parameter set, and the target parameter belongs to a parameter set corresponding to the first type; and detecting an anomaly of the target time series based on a first-type decision model corresponding to the first type.

Traffic anomaly detection method, and model training method and apparatus

A method for implementing a generic routing encapsulation (GRE) tunnel, an access point (AP), and a network system. The method includes establishing a Control And Provisioning of Wireless Access Points (CAPWAP) tunnel with an access controller (AC), receiving addresses of at least two gateways, establishing at least two GRE tunnels coupled to the gateways respectively according to the addresses of the at least two gateways, and selecting one of the at least two GRE tunnels to transmit data.

Method for implementing GRE tunnel, access point and gateway

In an embodiment, a data collection method includes: collecting, by a data collection device, performance indicator data of a target device based on a collection periodicity; detecting change amplitude of the collected performance indicator data that is in a change detection window, where the change detection window includes multiple collection periodicities; and when it is detected that change amplitude of the performance indicator data that is in the change detection window is greater than or equal to a change detection threshold, sending the performance indicator data that is in the change detection window to a data analysis device.

Data collection method and related device

A network quality determining method includes, when experience quality represented by a customer experience index of a target user within a first time period is lower than experience quality represented by a customer experience index threshold, determining, using a first parameter value that is of a network key performance indicator (KPI) parameter of the target user within a second time period and that is collected by a collection device in a carrier network, a second parameter value of the network KPI parameter of the target user within the second time period in a home network, and when network quality represented by the second parameter value of the network KPI parameter is lower than network quality represented by a first threshold corresponding to the network KPI parameter in the home network, determining that quality of the home network is poor.

Network quality determining method and apparatus and storage medium

A fault localization method includes: obtaining user experience data, network topology data, and resource management data that are of a video service; where the network topology data is used to represent a connection relationship between network devices, and the resource management data is used to represent a connection relationship between user equipment and the network devices; determining a QoE experience indicator of a network device based on the user experience data, the network topology data, and the resource management data; and when QoE represented by the QoE experience indicator of the network device is lower than QoE represented by a device screening threshold, determining the network device as a possible questionable device.

Fault localization method and device

A fault locating method includes: receiving, by a first network device, a fault information packet flooded by another network device in a network, where each fault information packet includes statistical information about an interior gateway protocol packet of the network device sending the fault information packet, and the statistical information of each network device includes a statistical result on one or more key performance indicators KPIs of the network device; and determining, based on statistical information of the first network device and the statistical information of the another network device, a network device on which a fault occurs in the network. According to the method, the fault information packet flooded by the network device is received, so that information required for fault locating, that is, the KPI of the network device, can be quickly collected. This facilitates a fault locating process and reduces a fault locating time.

Fault locating method and network device

A packet processing method includes receiving, by an aggregation gateway, a first tunnel establishment request message sent by a home gateway, and sending a first tunnel establishment success message to the home gateway; receiving, by the aggregation gateway, a second tunnel establishment request message sent by the home gateway, and sending a second tunnel establishment success message to the home gateway; associating, by the aggregation gateway, a first tunnel with a second tunnel according to an identifier of the home gateway; and sending a downlink packet to the home gateway by using the first tunnel and/or the second tunnel. The embodiments of the present application may increase bandwidth.

Packet processing method and device

A sampling frequency recommendation method, apparatus, and device, and a storage medium relating to the field of communications technologies are disclosed. The sampling frequency recommendation method includes: obtaining a network key performance indicator of a to-be-analyzed data stream; sampling the network key performance indicator based on a plurality of different sampling frequencies to obtain an experience quality sequence corresponding to each sampling frequency, where the plurality of different sampling frequencies include one standard sampling frequency and at least two to-be-tested sampling frequencies, and the standard sampling frequency is greater than each to-be-tested sampling frequency; and determining a matching degree between an experience quality sequence corresponding to each to-be-tested sampling frequency and a standard experience quality sequence, and determining a recommended sampling frequency based on the matching degree between the experience quality sequence corresponding to each to-be-tested sampling frequency and the standard experience quality sequence.

Sampling frequency recommendation method, apparatus and device, and storage medium

A video quality assessment method includes obtaining a video quality assessment parameter of a to-be-assessed video, where the video quality assessment parameter of the to-be-assessed video includes an average packet loss gap of the to-be-assessed video, determining packet loss dispersion of the to-be-assessed video based on the video quality assessment parameter of the to-be-assessed video, and determining quality of the to-be-assessed video based on a packet loss rate of the to-be-assessed video, an average consecutive packet loss length of the to-be-assessed video, the packet loss dispersion of the to-be-assessed video, and attribute information of the to-be-assessed video.

Video quality assessment method and device

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Shenzhen, China

Li Xue