Abstract:
A model update system, which may be applied to the network control field, includes a site analysis device and a first analysis device. The site analysis device is configured to: receive a first model sent by the first analysis device; train the first model by using a first training sample to obtain a second model, where the first training sample includes first feature data of a network device in a site network corresponding to the site analysis device; obtain differential data between the first model and the second model; and send the differential data to the first analysis device. The first analysis device is configured to: send the first model to the site analysis device; receive the differential data sent by the site analysis device; and update the first model based on the differential data to obtain a third model.
Abstract:
This application discloses a model training method, apparatus, and system, and belongs to the AI field. The method includes: receiving a machine learning model sent by a first analysis device; and performing incremental training on the machine learning model based on a first training sample set, where feature data in the first training sample set is feature data from a local network corresponding to a local analysis device. In this application, a problem that the machine learning model obtained through offline training cannot be effectively adapted to a requirement of the local analysis device is resolved. Embodiments of this application are used to predict a classification result.
Abstract:
A method for implementing a GRE tunnel is provided. The access device obtains an address of an aggregation gateway group including at least one aggregation gateway. The access device sends a tunnel setup request in which an address of the access device is encapsulated by using the address of the aggregation gateway group as a destination address. The tunnel setup request is used to request for setting up a GRE tunnel. The access device receives a tunnel setup accept response sent back by an aggregation gateway and obtains an address of the aggregation gateway from the response. The aggregation gateway belongs to the aggregation gateway group. The access device configures the address of the aggregation gateway as a network side destination address of the GRE tunnel. A dynamic setup of a GRE tunnel on an access network that uses an aggregation technology is implemented.
Abstract:
A method for implementing a GRE tunnel, an access point (AP), and a gateway (GW). The method includes: the AP receives a first packet, where the first packet carries an address of the GW; configures a GRE tunnel to the GW, where a source destination of the GRE tunnel is an address of the AP, and a destination address of the GRE tunnel is the address of the GW; the AP receives a second packet sent by user equipment; performs GRE tunnel encapsulation for the second packet to form a third packet; and the AP sends the third packet to the GW by using the GRE tunnel, where the third packet carries the address of the AP. The embodiments of the present application enable efficient establishment of the GRE tunnel between the AP and the GW if there are a large quantity of APs.