Abstract: A communication method and apparatus. A terminal device determines channel state information based on S indexes of S vectors. Each of the S vectors is included in a first vector quantization dictionary. The first vector quantization dictionary includes N1 vectors, where N1 and S are both positive integers. The terminal device sends the channel state information to a network device. Because a vector included in the vector quantization dictionary usually has a relatively large dimension, quantizing channel information by using the first vector quantization dictionary is equivalent to performing dimension expansion on the channel information or maintaining a relatively high dimension. High-precision feedback is implemented by using relatively low signaling overheads.

Abstract: This application provides a data stream identification method and apparatus and belongs to the field of Internet technologies. The method includes: obtaining packet transmission attribute information of N consecutive packets in a target data stream; generating feature images of the packet transmission attribute information of the N consecutive packets based on the packet transmission attribute information of the N consecutive packets; and inputting the feature images into a pre-trained image classification model, to obtain a target application identifier corresponding to the target data stream. According to this application, accuracy of identifying an application identifier corresponding to a data stream can be improved.

Abstract: A reinforcement learning method and recognition apparatus includes: obtaining a structure graph, where the structure graph includes structure information that is of an environment or the intelligent agent and that is obtained through learning; inputing a current state of the environment and the structure graph to a policy function of the intelligent agent, where the policy function is used to generate an action in response to the current state and the structure graph, and the policy function of the intelligent agent is a graph neural network; outputing the action to the environment by using the intelligent agent; obtaining, from the environment by using the intelligent agent, a next state and reward data in response to the action; training the intelligent agent through reinforcement learning based on the reward data.

Abstract: A network parameter configuration method includes obtaining operational state information of a network device. The operational state information includes a state information set of each of N areas covered by the network device, and the state information set includes state information subsets in D dimensions. Negotiations are performed based on the state information subsets in the D dimensions of each of the N areas to obtain target parameter subsets in the D dimensions of each of the N areas. A configuration instruction is sent to the network device covering the N areas to instruct the network device to perform parameter configuration based on a target parameter set of each of the N areas that is carried in the configuration instruction. The method can improve network parameter configuration efficiency.

Abstract: A system control method includes receiving a control task and randomly selecting a chromosome from an evolution pool according to the control task. The selected chromosome is decoded to obtain (N+1) ensemble policies, where the chromosome includes (N+1) gene fragments, and where N is a positive integer greater than or equal to 1. Each gene fragment can uniquely correspond to an ensemble policy, and each ensemble policy can uniquely correspond to a preset function. One ensemble policy is used for assigning a weight to a preset function that uniquely corresponds to the ensemble policy. The evolution pool can maintain two or more chromosomes. An ensemble calculation is performed according to weights assigned by the (N+1) ensemble policies to obtain an ensemble control output. A control signal is generated according to the ensemble control output, where the control signal is used for performing system control.

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.

Abstract: A data packet forwarding method and apparatus, where the method includes collecting, by a source switch according to a preset sampling period, congestion extents of d sampling paths in n paths, storing congestion extent indication information of each sampling path, where d is less than n, selecting, according to the congestion extent indication information, a first target sampling path with a smallest congestion extent at a first time point, forwarding a first data packet using the first target sampling path, storing an identifier of a first data flowcell to which the first data packet belongs forwarding a second data packet using the first target sampling path when an identifier of a second data flowcell to which the second data packet belongs is the same as the identifier of the first data flowcell.

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.

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.

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.

Abstract: This application provides a data stream identification method and apparatus and belongs to the field of Internet technologies. The method includes: obtaining packet transmission attribute information of N consecutive packets in a target data stream; generating feature images of the packet transmission attribute information of the N consecutive packets based on the packet transmission attribute information of the N consecutive packets; and inputting the feature images into a pre-trained image classification model, to obtain a target application identifier corresponding to the target data stream. According to this application, accuracy of identifying an application identifier corresponding to a data stream can be improved.

Abstract: A method for use by a network controller is provided. The controller collects network status data of a current moment, and estimates network status information of the current moment according to the network status data of the current moment. The controller predicts network status information of a next moment according to the network status information of the current moment. The controller receives and preprocessing a dataflow entering the network at the current moment, to obtain a preprocessing result of the dataflow. The controller generates a control action by using a network control policy of the current moment and according to the network status information of the current moment, the predicted network status information of the next moment, and the preprocessing result of the dataflow. The controller obtains feedback information of the network resulted from the control action, and generates a network control policy of the next moment.

Abstract: Embodiments of this application provide a method for generating a routing control action in a software-defined network and a related device, to provide optimum control actions for the SDN. The method includes: obtaining a current network state parameter of the SDN; determining a Q function of the SDN based on the current network state parameter of the SDN and a deep neural network model, where the deep neural network model is determined based on a current topology structure of the SDN; and determining a routing control action for the SDN based on the Q function and a link state parameter of each link in the SDN. In the technical solution, the deep neural network model is combined with a Q-learning algorithm of reinforcement learning, and optimum control actions can be determined.

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.

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.

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.

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.

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.

Abstract: A data packet forwarding method and apparatus, where the method includes collecting, by a source switch according to a preset sampling period, congestion extents of d sampling paths in n paths, storing congestion extent indication information of each sampling path, where d is less than n, selecting, according to the congestion extent indication information, a first target sampling path with a smallest congestion extent at a first time point, forwarding a first data packet using the first target sampling path, storing an identifier of a first data flowcell to which the first data packet belongs forwarding a second data packet using the first target sampling path when an identifier of a second data flowcell to which the second data packet belongs is the same as the identifier of the first data flowcell.

Abstract: Embodiments of this application provide a method for generating a routing control action in a software-defined network and a related device, to provide optimum control actions for the SDN. The method includes: obtaining a current network state parameter of the SDN; determining a Q function of the SDN based on the current network state parameter of the SDN and a deep neural network model, where the deep neural network model is determined based on a current topology structure of the SDN; and determining a routing control action for the SDN based on the Q function and a link state parameter of each link in the SDN. In the technical solution, the deep neural network model is combined with a Q-learning algorithm of reinforcement learning, and optimum control actions can be determined.