CN109861869B - Configuration file generation method and device - Google Patents
Configuration file generation method and device Download PDFInfo
- Publication number
- CN109861869B CN109861869B CN201910184533.2A CN201910184533A CN109861869B CN 109861869 B CN109861869 B CN 109861869B CN 201910184533 A CN201910184533 A CN 201910184533A CN 109861869 B CN109861869 B CN 109861869B
- Authority
- CN
- China
- Prior art keywords
- network
- configuration file
- information
- topology
- member device
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Images
Landscapes
- Small-Scale Networks (AREA)
- Data Exchanges In Wide-Area Networks (AREA)
Abstract
The application provides a method and a device for generating a configuration file, wherein the method comprises the following steps: determining a topological structure of the network and equipment information of each member equipment in the network; generating a first network topology map of the network based on the topology structure of the network and the device information of each member device; inputting the first network topological graph into a trained neural network, so that the first network topological graph is identified by the neural network, and outputting a first configuration file of each member device in the network; and acquiring a first configuration file of each member device output by the neural network. By using the method provided by the application, the generation and issuing efficiency and accuracy of the configuration file can be improved.
Description
Technical Field
The present application relates to the field of computer communications, and in particular, to a method and an apparatus for generating a configuration file.
Background
A user network typically consists of tens or even hundreds of network devices. The existing configuration method for network equipment in a user network is as follows: each network device in the user network is manually configured. This manual configuration is inefficient and prone to errors.
Disclosure of Invention
In view of this, the present application provides a method and an apparatus for generating a configuration file, so as to improve efficiency and accuracy of generating and issuing the configuration file.
Specifically, the method is realized through the following technical scheme:
according to a first aspect of the present application, a method for generating a configuration file is provided, where the method is applied to a network management device in a network, and includes:
determining a topological structure of the network and equipment information of each member equipment in the network;
generating a first network topology map of the network based on the topology structure of the network and the device information of each member device;
inputting the first network topological graph into a trained neural network, so that the first network topological graph is identified by the neural network, and outputting a first configuration file of each member device in the network;
and acquiring a first configuration file of each member device output by the neural network.
Optionally, the determining the topology of the network and the device information of each member device in the network includes:
sending topology collection messages to each member device;
receiving topology information and equipment information returned by each member equipment;
calculating a topology of the network based on the topology information of each member device.
Optionally, after generating the configuration file of each member device in the network, the method further includes:
displaying the first network topological graph and a first configuration file generated for each member device to a user;
and if a confirmation message aiming at the first configuration file input by the user is received, issuing the first configuration file generated for each member device to each member device.
Optionally, after the network topology and the configuration file generated for each member device are presented to the user, the method further includes:
if a modification message aiming at the first configuration file and input by a user is received, acquiring a second network topological graph carried in the modification message; the second network topological graph is a network topological graph formed by adding key information to a specified position in the first network topological graph by a user;
inputting the second network topological graph into the neural network, so that the neural network identifies the second network topological graph and outputs a second configuration file of each member device;
and acquiring the second configuration file of each member device and issuing the second configuration file to each member device.
Optionally, the neural network is trained by sample label pairs corresponding to various types of networks; the sample in the sample label pair corresponding to each network is a network topology map of the network, and the label is a configuration file of each member device in the network.
According to a second aspect of the present application, there is provided a device for generating a configuration file, where the device is applied to a network management device in a network, and the device includes:
a determining unit, configured to determine a topology structure of the network and device information of each member device in the network;
a generating unit, configured to generate a first network topology map of the network based on a topology structure of the network and device information of each member device;
the input unit is used for inputting the first network topological graph into a trained neural network, so that the neural network can identify the first network topological graph and output a first configuration file of each member device in the network;
and the acquisition unit is used for acquiring the first configuration file of each member device output by the neural network.
Optionally, the determining unit is specifically configured to send a topology collection packet to each member device; receiving topology information and equipment information returned by each member equipment; calculating a topology of the network based on the topology information of each member device.
Optionally, the apparatus further comprises:
the issuing unit is used for displaying the first network topological graph and a first configuration file generated for each member device to a user; and if a confirmation message aiming at the first configuration file input by the user is received, issuing the first configuration file generated for each member device to each member device.
Optionally, the issuing unit is further configured to, if a modification message for the first configuration file and input by a user is received, obtain a second network topology map carried in the modification message; the second network topological graph is a network topological graph formed by adding key information to a specified position in the first network topological graph by a user; inputting the second network topological graph into the neural network, so that the neural network identifies the second network topological graph and outputs a second configuration file of each member device; and acquiring the second configuration file of each member device and issuing the second configuration file to each member device.
Optionally, the neural network is trained by sample label pairs corresponding to various types of networks; the sample in the sample label pair corresponding to each network is a network topology map of the network, and the label is a configuration file of each member device in the network.
According to the method and the device, the trained neural network is adopted to identify the network topological graph automatically generated by the network management equipment, so that the configuration file of each member equipment is obtained, and the configuration file does not need to be manually input for each member equipment, so that the generation efficiency of the configuration file can be greatly improved, and the workload of manual configuration is greatly reduced.
In addition, after the configuration files of the member devices are generated, the network management device does not immediately issue the configuration files to the member devices, but displays the configuration files for users to confirm, and the method can greatly improve the accuracy of issuing the configuration files.
Drawings
FIG. 1 is a schematic diagram of a network topology shown in an exemplary embodiment of the present application;
FIG. 2 is a flow chart illustrating a method for profile generation according to an exemplary embodiment of the present application;
FIG. 3 is a schematic diagram of another network topology shown in an exemplary embodiment of the present application;
FIG. 4 is a block diagram of a configuration file generation apparatus shown in an exemplary embodiment of the present application;
fig. 5 is a hardware structure diagram of a network management device according to an exemplary embodiment of the present application.
Detailed Description
Reference will now be made in detail to the exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, like numbers in different drawings represent the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present application. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the application, as detailed in the appended claims.
The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. As used in this application and the appended claims, the singular forms "a", "an", and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It should also be understood that the term "and/or" as used herein refers to and encompasses any and all possible combinations of one or more of the associated listed items.
It is to be understood that although the terms first, second, third, etc. may be used herein to describe various information, such information should not be limited to these terms. These terms are only used to distinguish one type of information from another. For example, first information may also be referred to as second information, and similarly, second information may also be referred to as first information, without departing from the scope of the present application. The word "if" as used herein may be interpreted as "at … …" or "when … …" or "in response to a determination", depending on the context.
In general, the network may include: network management equipment and member equipment.
1) The network management device may be configured to manage member devices, such as discovering member devices, collecting network topology information of each member device (e.g., which devices the member device is connected to, connection mode, connection port, etc.), device information of each member device (e.g., device model, etc.), and so on.
The Network management device may be a physical device configured in the Network, for example, when the Network is an SDN (Software Defined Network) Network, the Network management device may be an SDN controller.
Of course, the network management device may also be a network device configured with network management software in the network. Here, the network management device is only illustrated by way of example, and is not particularly limited.
2) The member devices (also referred to as network devices) may include forwarding devices, such as switches, routers, and the like, and are mainly used for forwarding messages and the like.
The network management equipment in the network generates a network topological graph of the network based on the topological structure of the network and the equipment information of each member equipment, inputs the network topological graph into a trained neural network, and identifies the network topological graph by the neural network to obtain the configuration file of each member equipment in the network.
According to the method and the device, the trained neural network is adopted to identify the network topological graph automatically generated by the network management equipment, so that the configuration file of each member equipment is obtained, and the configuration file does not need to be manually input for each member equipment, so that the generation efficiency of the configuration file can be greatly improved.
The training of the neural network is explained in detail below.
The neural network can be carried on an electronic device, and the electronic device can be the network management device, or a server cluster independent of the network management device. Here, the electronic device mounted on the neural network is merely described as an example, and is not particularly limited.
The neural network is formed by training sample label pairs corresponding to various types of networks; the sample in the sample label pair corresponding to each network is a network topology map of the network, and the label is a configuration file of each member device in the network.
Specifically, the training of the neural network can be realized by the following steps.
Step 1: the electronic device can acquire the network topology of various types of networks, the device information of each member device in the network, the key information for representing the network attribute, and the configuration file of each member device in each network, and then generate a network topology map in a preset format based on the network topology of various types of networks, the device information of each member device in the network, and the key information.
First, the concept involved in step 1 will be explained.
1) Various types of networks may include: the networks applied to different scenarios, such as a campus Network, a data center Network, etc., may also include special networks constructed by various Network technologies, such as a VXLAN (Virtual eXtensible local area Network) Network, an MPLS (Multi-protocol Label Switching) L2VPN (Layer 2 Virtual Private Network) Network, etc., and of course, the various types of networks may also include a common two-Layer forwarding Network and a common three-Layer forwarding Network. The network type of the network is only exemplified and not particularly limited.
The richer the network types are, the greater the number of networks is, and the higher the accuracy of generating the configuration file by the trained neural network is.
2) Network topology map
In practical applications, different types of networks may have the same network topology, so in the embodiment of the present application, the generated network topology may include not only the member devices in the network and the connection relationships of the member devices, but also the device information of the member devices and the key information of the network.
The device information may include information such as a device model. Due to the different models of devices typically employed by networks applied to different scenarios. For example, the device models of the network devices used in the campus network and the data center network are different. The neural network can thus be trained to distinguish networks applied to different scenarios based on the device information.
The key information is used to characterize the attributes of the network, for example, the key information may be used to identify that the network is a special network, such as a VXLAN network, or an MPLS L2VPN network. The neural network is trained through the key information to distinguish common networks (such as common two-layer networks and three-layer networks) from special networks.
In addition, it should be noted that the network topology map of the present application is a network topology map with a preset format, and further, icons and connection lines of each network device in the network topology map are preset. Different types of network devices correspond to different icons. For example, access switches, aggregation switches, core switches, APs, ACs, etc. all correspond to different icons. The designated location near the icon of the network device also configures device information for the network device. If the network is a special network, the key information is configured at the designated position of the whole network topological graph.
For example, a network topology diagram of the present application may be as shown in fig. 1.
And 2, step: the electronic equipment generates a sample label pair of each network by taking the configuration file of each member equipment in the network as a label and taking a network topological graph with a preset format generated for the network as a sample aiming at each network.
And step 3: the electronic device may train the neural network using sample label pairs corresponding to various types of networks.
In implementation, the electronic device may input sample tag pairs corresponding to various types of networks to the neural network. For each sample label pair, the neural network may identify the sample to obtain a configuration file of each network device in the sample. And then the neural network can calculate errors of the configuration files of the network devices and the configuration files of the network devices in the labels, which are obtained by identification, and reversely propagate the errors, and adjust parameters of each layer of the neural network until the errors of the configuration files of the network devices and the configuration files of the network devices in the labels, which are obtained by the neural network identification sample, are within a certain range, so that the neural network training is determined to be completed.
A method of generating a profile using the neural network will be described in detail below.
Referring to fig. 2, fig. 2 is a flowchart of a method for generating a configuration file according to an exemplary embodiment of the present application, where the method may be applied to a network management device in a network, and may include the following steps.
Step 201: the network management device can determine the topological structure of the network and the device information of each member device in the network.
When the method is implemented, the network management equipment can send the topology collection messages to each member equipment. After receiving the topology collection message, the member device can return the topology information and the device information of the member device to the network management device.
After receiving the topology information of each member device returned by each member device, the network management device may calculate the topology structure of the network based on the topology information of each member device.
Wherein the topology information includes: interface information, neighbor information, stack information, etc. for the member devices. Here, the topology information is merely exemplary and is not particularly limited.
It should be further noted that the topology collection packet may be a packet based on the LLDP protocol, may also be a packet of other types of protocols for collecting topology information and device information of the member device, and may also be a private protocol packet, where the topology collection packet is only exemplarily illustrated and is not specifically limited.
Step 202: and the network management equipment generates a first network topology map of the network based on the topology structure of the network and the equipment information of each member equipment.
During implementation, the network device generates a first network topology map in a preset format based on the calculated topology structure of the network and the device information of each member device.
Step 203: and the network management equipment inputs the first network topological graph into a trained neural network, so that the neural network identifies the first network topological graph and outputs a first configuration file of each member equipment in the network.
Step 204: the network management equipment can acquire the first configuration file of each member equipment output by the neural network.
When the method is implemented, the network management equipment can input the first network topological graph into the trained neural network. The neural network may identify the first network topology map, obtain a first configuration file of each member device in the first network topology map, and output the first configuration file of each member device.
The network management device can obtain a first configuration file of each member device output by the neural network.
In practical applications, a special network (such as a VXLAN network, an MPLS L2VPN network, etc.) and a common network (such as a common two-layer forwarding network and a common three-layer forwarding network, etc.) may have the same network topology, and configuration files of each member device in the special network and the common network are different. Key information of the network is required to distinguish a special network from a common network.
However, the network management device cannot determine the key information of the network based on the topology collection packet, so after the network management device obtains the first configuration file of each member device output by the neural network, the first configuration file of each member device and the first network topology map can be displayed to the user, so that the user can confirm the first configuration file.
After the user determines that the first profile for each member device is correct, the user may enter a confirmation message. After the network management equipment receives the confirmation message aiming at the first configuration file input by the user, the network management equipment can issue the first configuration file of each member equipment to each member equipment.
When the user determines that the first configuration file of the member device is incorrect, the user can input key information at the specified position of the first network topology graph displayed by the network management device to form a second network topology graph. Then, the user can input a modification message to the network management device, where the modification message carries the second network topology map.
When the network management equipment receives a modification message of a user for the first configuration file, a second network topology map carried in the modification message can be obtained, and then the second network topology map is input to the neural network. The neural network can identify the second network topology map and output a second configuration file of each member device.
The network management equipment can acquire the second configuration file of each member equipment output by the neural network and then issue the second configuration file of each member equipment to each member equipment.
For example, it is assumed that the network is a VXLAN network, but since the network management device in the network cannot determine the key information (i.e., VXLAN) of the network based on the network topology information and the device information of each member device in the network, a first network topology map generated by the network management device based on the network topology information and the device information of each member device is shown in fig. 3.
The network management device inputs the network topology map shown in fig. 3 into the neural network, so that the neural network identifies the network topology map shown in fig. 3, and outputs a first configuration file corresponding to each member device in the network topology map.
Then, the network management device may present the network topology shown in fig. 3 and the first configuration file to the user.
The user finds that the first profile is incorrect and adds critical information (i.e., VXLAN) at a specified location of the network topology shown in fig. 3, forming a second network topology (as shown in fig. 1). The user may then enter a modification message on the network management device, which carries the network topology shown in fig. 1.
The network management device may obtain the network topology map shown in fig. 1 carried in the modification message, and then input the network topology map shown in fig. 1 into the neural network, so that the neural network identifies the network topology map shown in fig. 1, and outputs the second configuration file of each member device. The network management equipment can acquire the second configuration file of each member equipment output by the neural network and issue the second configuration file to each member equipment.
As can be seen from the above description, the accuracy of the network management device issuing the configuration file for each member device can be improved by the user confirming the generated configuration file of each member device.
The application provides a method for generating a configuration file, wherein network management equipment in a network generates a network topological graph of the network based on a topological structure of the network and equipment information of each member equipment, the network topological graph is input into a trained neural network, and the neural network identifies the network topological graph to obtain the configuration file of each member equipment in the network.
According to the method and the device, the trained neural network is adopted to identify the network topological graph automatically generated by the network management equipment, so that the configuration file of each member equipment is obtained, and the configuration file does not need to be manually input for each member equipment, so that the generation efficiency of the configuration file can be greatly improved, and the workload of manual configuration is greatly reduced.
In addition, after the configuration files of the member devices are generated, the network management device does not immediately issue the configuration files to the member devices, but displays the configuration files for users to confirm, and the method can greatly improve the accuracy of issuing the configuration files.
In addition, the application also provides a configuration file generation device corresponding to the configuration file generation method.
Referring to fig. 4, fig. 4 is a block diagram of a configuration file generation apparatus according to an exemplary embodiment of the present application. The device can be applied to network management equipment and can comprise the following units.
A determining unit 401, configured to determine a topology structure of the network and device information of each member device in the network;
a generating unit 402, configured to generate a first network topology map of the network based on a topology structure of the network and device information of each member device;
an input unit 403, configured to input the first network topology map into a trained neural network, so that the neural network identifies the first network topology map, and outputs a first configuration file of each member device in the network;
an obtaining unit 404, configured to obtain a first configuration file of each member device output by the neural network.
Optionally, the determining unit 401 is specifically configured to send a topology collection packet to each member device; receiving topology information and equipment information returned by each member equipment; calculating a topology of the network based on the topology information of the member devices.
Optionally, the apparatus further comprises:
a sending unit 405, configured to show the first network topology map and a first configuration file generated for each member device to a user; and if a confirmation message aiming at the first configuration file input by the user is received, issuing the first configuration file generated for each member device to each member device.
Optionally, the issuing unit 405 is further configured to, if a modification message for the first configuration file and input by a user is received, obtain a second network topology map carried in the modification message; the second network topological graph is a network topological graph formed by adding key information to a specified position in the first network topological graph by a user; inputting the second network topological graph into the neural network, so that the neural network identifies the second network topological graph and outputs a second configuration file of each member device; and acquiring the second configuration file of each member device and issuing the second configuration file to each member device.
Optionally, the neural network is trained by sample label pairs corresponding to various types of networks; the sample in the sample label pair corresponding to each network is a network topology map of the network, and the label is a configuration file of each member device in the network.
Referring to fig. 5, fig. 5 is a hardware structure diagram of a network management device according to an exemplary embodiment of the present application.
The network management equipment comprises: a communication interface 501, a processor 502, a machine-readable storage medium 503, and a bus 504; wherein the communication interface 501, the processor 502 and the machine-readable storage medium 503 are in communication with each other via a bus 504. The processor 502 may perform the configuration file generation method described above by reading and executing machine-executable instructions in the machine-readable storage medium 503 corresponding to the configuration file generation control logic.
The machine-readable storage medium 503 referred to herein may be any electronic, magnetic, optical, or other physical storage device that can contain or store information such as executable instructions, data, and the like. For example, the machine-readable storage medium may be: volatile memory, non-volatile memory, or similar storage media. In particular, the machine-readable storage medium 503 may be a RAM (random Access Memory), a flash Memory, a storage drive (e.g., a hard disk drive), a solid state disk, any type of storage disk (e.g., a compact disk, a DVD, etc.), or similar storage medium, or a combination thereof.
The specific details of the implementation process of the functions and actions of each unit in the above device are the implementation processes of the corresponding steps in the above method, and are not described herein again.
For the device embodiments, since they substantially correspond to the method embodiments, reference may be made to the partial description of the method embodiments for relevant points. The above-described embodiments of the apparatus are merely illustrative, and the units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the scheme of the application. One of ordinary skill in the art can understand and implement it without inventive effort.
The above description is only exemplary of the present application and should not be taken as limiting the present application, as any modification, equivalent replacement, or improvement made within the spirit and principle of the present application should be included in the scope of protection of the present application.
Claims (6)
1. A method for generating configuration files is applied to network management equipment in any type of network, and comprises the following steps:
determining a topological structure of the network and equipment information of each member equipment in the network;
generating a first network topology map in a preset network format based on the topology structure of the network, the equipment information of each member equipment and key information for representing the attribute of the network; the device information at least comprises a device model;
inputting the first network topological graph into a trained neural network, so that the neural network identifies the first network topological graph, and outputting a first configuration file of each member device in the network; the trained neural network is formed by training sample label pairs corresponding to various types of networks; the sample in the sample label pair corresponding to each network is a network topological graph of the network, and the label is a configuration file of each member device in the network; the network topology map of the network is generated based on the topology structure of the network, the device information of each member device and key information for representing the attributes of different types of networks;
and acquiring a first configuration file of each member device output by the neural network.
2. The method of claim 1, wherein determining the topology of the network and the device information of each member device in the network comprises:
sending topology collection messages to each member device;
receiving topology information and equipment information returned by each member equipment;
calculating a topology of the network based on the topology information of each member device.
3. The method of claim 1, wherein after the generating the configuration file for each member device in the network, the method further comprises:
displaying the first network topological graph and a first configuration file generated for each member device to a user;
and if a confirmation message which is input by the user and aims at the first configuration file is received, issuing the first configuration file generated for each member device to each member device.
4. An apparatus for generating a configuration file, wherein the apparatus is applied to a network management device in any type of network, and comprises:
a determining unit, configured to determine a topology structure of the network and device information of each member device in the network;
the generating unit is used for generating a first network topological graph in a preset network format based on the topological structure of the network, the equipment information of each member equipment and the key information for representing the attribute of the network; the device information at least comprises a device model;
the input unit is used for inputting the first network topological graph into a trained neural network, so that the neural network identifies the first network topological graph and outputs a first configuration file of each member device in the network; the trained neural network is formed by training sample label pairs corresponding to various types of networks; the sample in the sample label pair corresponding to each network is a network topological graph of the network, and the label is a configuration file of each member device in the network; the network topological graph of the network is generated based on the topological structure of the network, the equipment information of each member equipment and key information for representing the attributes of the network;
and the acquisition unit is used for acquiring the first configuration file of each member device output by the neural network.
5. The apparatus according to claim 4, wherein the determining unit is specifically configured to send a topology discovery packet to each member device; receiving topology information and equipment information returned by each member equipment; calculating a topology of the network based on the topology information of each member device.
6. The apparatus of claim 5, further comprising:
the issuing unit is used for displaying the first network topological graph and a first configuration file generated for each member device to a user; and if a confirmation message aiming at the first configuration file input by the user is received, issuing the first configuration file generated for each member device to each member device.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910184533.2A CN109861869B (en) | 2019-03-12 | 2019-03-12 | Configuration file generation method and device |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910184533.2A CN109861869B (en) | 2019-03-12 | 2019-03-12 | Configuration file generation method and device |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN109861869A CN109861869A (en) | 2019-06-07 |
| CN109861869B true CN109861869B (en) | 2022-05-31 |
Family
ID=66900520
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910184533.2A Active CN109861869B (en) | 2019-03-12 | 2019-03-12 | Configuration file generation method and device |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN109861869B (en) |
Families Citing this family (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20210329436A1 (en) * | 2020-04-17 | 2021-10-21 | Bot3, Inc. | Bluetooth device networking system and method based on ble |
| CN113315655A (en) * | 2021-05-24 | 2021-08-27 | 恒隆通信技术有限公司 | Information configuration method of intelligent networking environment and intelligent networking system |
| CN115291963A (en) * | 2022-06-17 | 2022-11-04 | 芯华章科技股份有限公司 | Method for configuring hardware resources, electronic device and storage medium |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101183370A (en) * | 2007-12-13 | 2008-05-21 | 浪潮通信信息系统有限公司 | Topological modelling approach based on class definition and relationship definition |
| CN102624876A (en) * | 2012-02-23 | 2012-08-01 | 华为技术有限公司 | Data configuration method and data configuration device |
| CN106576054A (en) * | 2014-08-17 | 2017-04-19 | 微软技术许可有限责任公司 | Network device configuration framework |
| CN108880853A (en) * | 2017-11-27 | 2018-11-23 | 北京视联动力国际信息技术有限公司 | A kind of configuration information recovery method and configuration information server regarding networked server |
Family Cites Families (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US10389173B2 (en) * | 2017-03-31 | 2019-08-20 | Cisco Technology, Inc. | Programmable and application aware power utility automation networking |
| US10331973B2 (en) * | 2017-06-26 | 2019-06-25 | Nicira, Inc. | System and method for deploying graphical diagram topologies |
-
2019
- 2019-03-12 CN CN201910184533.2A patent/CN109861869B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101183370A (en) * | 2007-12-13 | 2008-05-21 | 浪潮通信信息系统有限公司 | Topological modelling approach based on class definition and relationship definition |
| CN102624876A (en) * | 2012-02-23 | 2012-08-01 | 华为技术有限公司 | Data configuration method and data configuration device |
| CN106576054A (en) * | 2014-08-17 | 2017-04-19 | 微软技术许可有限责任公司 | Network device configuration framework |
| CN108880853A (en) * | 2017-11-27 | 2018-11-23 | 北京视联动力国际信息技术有限公司 | A kind of configuration information recovery method and configuration information server regarding networked server |
Also Published As
| Publication number | Publication date |
|---|---|
| CN109861869A (en) | 2019-06-07 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US8848544B2 (en) | Event correlation using network data flow simulation over unmanaged network segments | |
| CN109861869B (en) | Configuration file generation method and device | |
| US7619988B2 (en) | Network configuration management apparatus, network configuration management program and network configuration management method | |
| CN103514245B (en) | Creation user it is visible processing tracking can search for and global data base | |
| CN109428749A (en) | Network management and relevant device | |
| CN109714239B (en) | Management message issuing method, VNFM (virtual network management frequency) equipment and server | |
| CN111835532B (en) | Network authentication method and device | |
| CN111083179B (en) | Internet of things cloud platform, and equipment interaction method and device based on same | |
| CN112311571B (en) | Network topology generation method and device, electronic equipment and non-transitory storage medium | |
| CN107547242B (en) | The acquisition methods and device of VM configuration information | |
| CN112087379A (en) | Service chain arranging method and device, storage medium and electronic device | |
| US20210044484A1 (en) | Network topology display method and network management device | |
| CN113938378B (en) | Method, equipment and medium for checking network equipment configuration in cloud network environment | |
| CN103634177A (en) | Method and system for implementing configuration on basis of network services | |
| CN107122324A (en) | A kind of method for message transmission and device | |
| CN110855457A (en) | Method and device for generating network model and configuring network | |
| CN109379268A (en) | Creation method, device and the server of Virtual Private Network | |
| CN113794581B (en) | Distributed CP unified deployment method, network equipment and storage medium | |
| CN108768849A (en) | Message processing method and device | |
| CN110213073A (en) | Data flow variation, electronic equipment, calculate node and storage medium | |
| CN110460456B (en) | Method and device for synchronously generating network topology by Management Information Base (MIB) | |
| CN110430140A (en) | Path processing method, device, equipment and storage medium | |
| CN108989311A (en) | Generate the method and apparatus of input parameter | |
| CN108259340B (en) | Topology information transmission method and device | |
| CN102325043B (en) | Topology generation method, device and system |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant | ||
| TR01 | Transfer of patent right |
Effective date of registration: 20230627 Address after: 310052 11th Floor, 466 Changhe Road, Binjiang District, Hangzhou City, Zhejiang Province Patentee after: H3C INFORMATION TECHNOLOGY Co.,Ltd. Address before: 310052 Changhe Road, Binjiang District, Hangzhou, Zhejiang Province, No. 466 Patentee before: NEW H3C TECHNOLOGIES Co.,Ltd. |
|
| TR01 | Transfer of patent right |