A method and system for automatically generating network topology diagrams

Abstract

This invention relates to the field of network topology map generation technology, specifically to an automated method and system for generating network topology maps. The invention acquires device information for each of a plurality of devices; determines an initial network topology map based on the device information of each device; acquires the status information of each device within a target period; and generates a target network topology map based on the status information of each device within the target period and the initial network topology map. This invention can quickly and accurately determine whether the initial network topology map has changed from the actual network topology within the target period based on the acquired status information, and thus can quickly and accurately determine whether to update the initial network topology map. Based on the status information of the multiple devices and the initial network topology map, the initial network topology map is updated, thereby quickly and effectively automating the generation of the target network topology map.

Description

Technical Field

[0001] This invention relates to the field of automatic network topology diagram generation technology, specifically to a method and system for automatically generating network topology diagrams. Background Technology

[0002] With the continuous expansion of digital infrastructure and the increasing complexity of network environments, the operation and maintenance of large and medium-sized data centers involves a large number of network devices, heterogeneous device models, and the coexistence of physical and logical connections. This brings a huge workload and accuracy risks to the identification, modeling, and visualization of network topology. Currently, static parsing algorithms are used to generate network topology maps.

[0003] However, using static parsing algorithms to generate network topology maps cannot effectively handle real-time changes in dynamic network environments, resulting in significant differences between the generated network topology maps and the actual network topology. It is also unable to drive topology map updates based on real-time alarms, load changes, and other state data, leading to poor accuracy and timeliness in network topology map generation. Summary of the Invention

[0004] To address the problem that generated network topology maps often differ significantly from actual network topology, and cannot be updated based on real-time alarms, load changes, and other status data, resulting in poor timeliness and usability of the network topology maps, this invention aims to provide a method and system for automatically generating network topology maps. The specific technical solution adopted is as follows: This invention proposes an automated method for generating network topology diagrams, the method comprising: Obtain device information for each of the multiple devices. The device information for a single device includes the device's Address Resolution Protocol (ARP) table, Media Access Control Address (MAC) table, and adjacency information. Based on the device information of each device, determine the initial network topology; Obtain the status information of each device within the target period, wherein the status information of a device includes the device's connection status, the device's port usage status, and the device's traffic change status; Based on the status information of each device within the target period and the initial network topology map, a target network topology map is generated.

[0005] Furthermore, the generation of the target network topology map based on the state information of each device within the target period and the initial network topology map may specifically include: Based on the status information of each device within the target period, the number of multiple first neighbor devices and the number of multiple second neighbor devices are determined. The number of multiple first neighbor devices is the number of neighbor devices of each device at the first moment, and the number of multiple second neighbor devices is the number of neighbor devices of each device at the second moment. The second moment is the moment following the first moment, and the first moment and the second moment are the moments included within the target period. Based on the number of multiple first neighbor devices, the number of multiple second neighbor devices, and the link state change index, the network structure disturbance degree is determined. The network structure disturbance degree characterizes the degree of network structure change in the network operation and maintenance environment, which is the operation and maintenance environment in which each device is located. If the network structure perturbation exceeds the target perception threshold, the initial network topology is updated to generate the target network topology.

[0006] Furthermore, the generation of the target network topology map based on the state information of each device within the target period and the initial network topology map may further include: Determine a first timestamp and a second timestamp, wherein the first timestamp is the timestamp of the change in the status information of each device, and the second timestamp is the timestamp of the link update of the initial network topology map; Based on the first timestamp, the second timestamp, and the topology-aware error index, the adjustment coefficient of the update strategy for the initial network topology map is determined. The topology-aware error index represents the error in generating the target network topology map. The target perception threshold is determined by adjusting the coefficients based on the update strategy and the preset perception threshold.

[0007] Furthermore, the generation of the target network topology map based on the state information of each device within the target period and the initial network topology map may further include: Determine the first number of links and the second number of links. The first number of links is the number of links in the initial network topology diagram, and the second number of links is the number of links in the actual topology diagram. The actual topology diagram is the actual physical logical connection topology diagram determined according to the manual configuration document. Based on the number of the first link, the number of the second link, and the perception bluntness index, a topology perception error index is determined. This perception bluntness index characterizes the instability of the network structure in the network operation and maintenance environment.

[0008] Furthermore, the generation of the target network topology map based on the state information of each device within the target period and the initial network topology map may further include: Based on the status information of each device within the target period, the number of first paths and the number of second paths are determined. The number of first paths is the number of topology paths included in the initial network topology map within the target period, and the number of second paths is the number of paths in the initial network topology map that have changed within the target period. The perception bluntness index is determined based on the number of the first path, the number of the second path, and the perturbation of the network structure.

[0009] Furthermore, the generation of the target network topology map based on the state information of each device within the target period and the initial network topology map may further include: Based on the status information of each device within the target period, determine the number of link status changes of the links included in the initial network topology map within the target period and the traffic detection value of the links included in the initial network topology map within the target period; The link state change index is determined based on the number of link state changes, the traffic detection value, and the duration of the target period.

[0010] Furthermore, the determination of the link state change index based on the number of link state changes, the traffic detection value, and the duration of the target period may also include: Based on the flow rate measurement value, determine the standard deviation and mean of the flow rate measurement value; The link state change index is determined based on the standard deviation of the traffic detection value, the mean of the traffic detection value, the number of link state changes, and the duration of the target period.

[0011] Furthermore, the acquisition of device information for each of the multiple devices may specifically include: Based on the Simple Network Management Protocol (SNMP) and the Secure Shell (SSH) or Telnet login method, obtain the ARP table and MAC address table for each of the multiple devices; According to the Link Layer Discovery Protocol (LLDP), obtain the adjacency information of each of the multiple devices.

[0012] Furthermore, the initial network topology diagram determined based on the device information of each device may specifically include: The network connection relationship of each device is determined based on its ARP table, MAC address table, and adjacency information. Based on the network connectivity and the graph building engine, the initial network topology is determined.

[0013] The present invention also proposes an automated network topology map generation system, including a memory, a processor, and a computer program stored in the memory and running on the processor, the processor executing the computer program to implement the steps of the above method.

[0014] The present invention has the following beneficial effects: This embodiment first obtains the device information of each of the multiple devices, wherein the device information of a device includes the device's Address Resolution Protocol (ARP) table, the device's Media Access Control (MAC) address table, and the device's adjacency information; based on the device information of each of the multiple devices, an initial network topology is determined; the status information of each of the multiple devices is obtained within a target period, wherein the status information of a device includes the device's connection status, the device's port usage status, and the device's traffic change status; based on the status information of each of the multiple devices within the target period and the initial network topology, a target network topology is generated. In this embodiment, an initial network topology map can be quickly generated based on the ARP table, MAC address table, and adjacency information of each of the multiple devices. Based on the connection status, port usage status, and traffic change status of each of the multiple devices, it can be quickly and accurately determined whether the initial network topology map has changed from the actual network topology within the target period. Thus, it can be quickly and accurately determined whether to update the initial network topology map. Based on the status information of each of the multiple devices within the target period and the initial network topology map, the initial network topology map is updated, thereby quickly and effectively automatically generating the target network topology map. Attached Figure Description

[0015] To more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0016] Figure 1 This is a flowchart illustrating an automated network topology diagram generation method according to an embodiment of the present invention. Figure 2 This is a schematic diagram illustrating the change in flow detection value according to an embodiment of the present invention; Figure 3 This is a schematic diagram of the structure of an automated network topology graph generation system provided in one embodiment of the present invention. Detailed Implementation

[0017] To further illustrate the technical means and effects adopted by the present invention to achieve its intended purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of a network topology diagram automated generation method and system proposed according to the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0018] It should be noted that the terms "first," "second," etc., used in the specification and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein.

[0019] Furthermore, the terms "comprising" and "having," and any variations thereof, used in the description of this invention are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not limited to the steps or units listed, but may optionally include other steps or units not listed, or may optionally include other steps or units inherent to such processes, methods, products, or apparatus.

[0020] It should be noted that in the embodiments of the present invention, the words "exemplary" or "for example" are used to indicate that they are examples, illustrations, or descriptions. Any embodiment or design that is described as "exemplary" or "for example" in the embodiments of the present invention should not be construed as being more preferred or advantageous than other embodiments or design options. Specifically, the use of the words "exemplary" or "for example" is intended to present the relevant concepts in a specific manner.

[0021] In the description of this invention, unless otherwise stated, "a plurality of" means two or more.

[0022] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0023] The following description, in conjunction with the accompanying drawings, details the specific scheme of the network topology diagram automatic generation method and system provided by the present invention.

[0024] Please see Figure 1 The diagram illustrates a flowchart of an automated network topology map generation method according to an embodiment of the present invention, the specific method including steps S1-S4: Step S1: Obtain device information for each of the multiple devices.

[0025] The device information of a device includes the device's Address Resolution Protocol (ARP) table, the device's Media Access Control (MAC) address table, and the device's adjacency information.

[0026] Alternatively, these multiple devices may refer to all accessible devices within a large enterprise data center.

[0027] Optionally, all reachable devices within the large enterprise data center can be determined by the location of the Internet Protocol Address (IP) range or network segment configured within the data center.

[0028] Optionally, device information for each of the multiple devices can be obtained through static device import and / or dynamic scanning discovery.

[0029] In one implementation of this invention, the method for obtaining device information of each of a plurality of devices specifically includes: Log in using Simple Network Management Protocol (SNMP) and Remote Connection Control Protocol (SSH) or Tenet to obtain the ARP table and MAC address table for each of the multiple devices. Optionally, the interface list, interface status, MAC table, ARP table, and CPU memory usage of each of the multiple devices can be obtained through the Simple Network Management Protocol (SNMP).

[0030] Optionally, by logging in via the remote connection control protocol SSH or Tenet, each of the multiple devices can be accessed through the command line, thereby obtaining the interface status, routing table, MAC forwarding table, etc. of each of the multiple devices.

[0031] According to the Link Layer Discovery Protocol (LLDP), obtain the adjacency information of each of the multiple devices.

[0032] Optionally, the adjacency information of each of the multiple devices can be obtained through the Cisco Discovery Protocol (CDP) to quickly identify the physical connection relationship of each of the multiple devices.

[0033] In one implementation, a suitable data acquisition protocol is selected based on the support capabilities of different types of devices. For example, switches, routers and other devices use the LLDP or CDP protocol to obtain device information, while firewalls, servers and other devices use the SNMP protocol.

[0034] Step S2: Determine the initial network topology based on the device information of each device.

[0035] In one implementation, the device information of each of the multiple devices is subjected to protocol parsing, field extraction, and unified format conversion. Then, based on the ARP table, MAC table, and LLDP adjacency information of each of the multiple devices, a topology reasoning algorithm is used to establish the physical topology, thereby determining the initial network topology map.

[0036] Optionally, this initial network topology diagram is used to characterize the network structure of the network operation and maintenance environment in the aforementioned large enterprise data center.

[0037] In one implementation of this invention, the method for determining the initial network topology based on the device information of each device specifically includes: Based on the ARP table, MAC address table, and adjacency information of each of the multiple devices, determine the network connection relationship of each of the multiple devices; Based on the network connectivity and the graph building engine, the initial network topology is determined.

[0038] In one implementation, IP address scanning is performed on a network segment. Device information for each of the multiple devices is obtained through SNMP polling. Adjacency information is then acquired via LLDP / CDP protocols to establish a preliminary node list and interface table for each device. The manufacturer and device type of each device are determined based on SNMP. The switch's MAC address table and ARP table are then parsed to establish an IP-MAC-port mapping, further associating port connections with devices to achieve point-to-point link reasoning. By matching the adjacency relationships and MAC addresses of each device, direct physical connections between them are identified, constructing a graph structure with devices as nodes and links as edges. A graph data structure is used to represent network connections. A graph building engine is used to render a visual topology graph, setting node and link attributes for each device. Node attributes include name, type, IP address, and status, while link attributes include speed and status.

[0039] Step S3: Obtain the status information of each device within the target period.

[0040] The status information of a device includes its connection status, port usage status, and traffic change status.

[0041] Understandably, the connection status of the device represents the changes in the link status of the device, and these changes in link status can represent changes in the link layer structure; the port usage status includes open and closed states, which represent the usage of the port. When the port is in an open state, it means that the port can transmit data, and when the port is in a closed state, it means that the port cannot transmit data; the traffic change status of the device can represent the fluctuation of the device's traffic, and thus, based on the fluctuation of the device's traffic, it can be determined whether the network of the device is in a congested state.

[0042] Optionally, the connection status, port usage status, and traffic change status of each of the multiple devices are continuously acquired at each time point within a target period, where the target period is the time period for detecting network topology changes.

[0043] Step S4: Generate the target network topology map based on the status information of each device within the target period and the initial network topology map.

[0044] It is understandable that, based on the status information of each of the multiple devices within the target period, it is determined whether the initial network topology has undergone structural changes, thereby updating the initial network topology and generating the target network topology.

[0045] In one implementation of this invention, the method for generating a target network topology map based on the state information of each device during the target period and the initial network topology map specifically includes: Based on the status information of each of the multiple devices within the target period, the number of multiple first neighbor devices and the number of multiple second neighbor devices are determined. The number of multiple first neighbor devices is the number of neighbor devices of each of the multiple devices at a first time, and the number of multiple second neighbor devices is the number of neighbor devices of the multiple devices at a second time. The second time is the time following the first time, and the first time and the second time are the times included within the target period.

[0046] Based on the number of multiple first neighbor devices, the number of multiple second neighbor devices, and the link state change index, the network structure disturbance degree is determined. This network structure disturbance degree characterizes the degree of network structure change in the network operation and maintenance environment, which is the operation and maintenance environment in which each device is located.

[0047] It should be understood that the aforementioned neighboring devices refer to the directly connected device nodes of each network device node in the network topology diagram, that is, the nodes directly connected to that node in the topology diagram.

[0048] Understandably, in the network operation and maintenance environment of a large enterprise data center, it is necessary to determine whether the structure of the initial network topology has changed based on the changes in the neighboring devices of each device among multiple devices.

[0049] For example, the formula for calculating the network structure disturbance degree in the network operation and maintenance environment of this large enterprise data center during the target period is as follows: In the formula: Indicates the target period. This represents the degree of network structure perturbation within the target period. This represents the link change index of the network operation and maintenance environment within the target period. This indicates the number of moments included within the target period. This represents the set of the first neighbor devices of a single node at time i. This represents the set of the second neighbor devices of a single node at time i-1. This represents a function for calculating the average value.

[0050] Understandable This represents the neighbor change index of a single node within the target period. The larger the value, the more significant the change in the adjacency structure, indicating that the network topology may have been restructured or the links may have been reconfigured, which can trigger the real-time update process of the network topology map. This represents the neighbor change index for all nodes.

[0051] It should be understood that The larger the product, the greater the network structure disturbance in the network operation and maintenance environment within the target period.

[0052] It should be noted that in other embodiments of the present invention, other basic mathematical operations can also be used to construct positive and negative correlations. The specific methods are well known to those skilled in the art and will not be described in detail here.

[0053] If the network structure perturbation exceeds the target perception threshold, the initial network topology is updated to generate the target network topology.

[0054] In one implementation of this invention, when the network structure disturbance degree is greater than the target perception threshold, it indicates that the structure in the network operation and maintenance environment has changed significantly, that is, the structure of the initial network topology map has been reconstructed or the links have been reconfigured. At this time, it is necessary to automatically update the initial network topology map according to the current network structure in the network operation and maintenance environment, so as to automatically generate the target network topology map.

[0055] It is understandable that the network structure perturbation degree is used to characterize the dynamic changes in the neighbor relationships of each device among multiple devices in the network operation and maintenance environment, so as to determine whether there are significant structural changes in the initial network topology based on the network structure perturbation degree.

[0056] In one implementation of this invention, the method for generating a target network topology map based on the state information of each device during the target period and the initial network topology map may further include: A first timestamp and a second timestamp are determined. The first timestamp is the timestamp of the change in the status information of each device, and the second timestamp is the timestamp of the link update of the initial network topology.

[0057] Based on the first timestamp, the second timestamp, and the topology-aware error index, the adjustment coefficient of the update strategy for the initial network topology map is determined. The topology-aware error index represents the error in generating the target network topology map.

[0058] For example, the formula for calculating the adjustment coefficient of the update strategy for the initial network topology graph is as follows: In the formula: Indicates the target period. This represents the adjustment coefficient of the update strategy within the target period. This represents the topology sensing error index within the target period. The timestamp indicating the change in the status information of the corresponding device. The timestamps of link updates in this initial network topology graph This represents the proportional normalization function.

[0059] Understandable This indicates the update lag time of the initial network topology map in the network operation and maintenance environment. The larger the value, the more serious the deviation of the current initial network topology map update strategy, and the more necessary it is to adjust the update frequency, strategy logic or reconstruct the triggering conditions in a timely manner. The larger the value, the less disturbance the network structure in the network operation and maintenance environment is required to trigger the update of the initial network topology. In other words, the perception threshold needs to be changed to avoid frequent network topology updates and the resulting false alarms.

[0060] It should be noted that in other embodiments of the present invention, other basic mathematical operations can also be used to construct positive and negative correlations. The specific methods are well known to those skilled in the art and will not be described in detail here.

[0061] The target perception threshold is determined by adjusting the coefficients based on the update strategy and the preset perception threshold.

[0062] Optionally, after determining the update strategy adjustment coefficient of the initial network topology map in the network operation and maintenance environment, the update strategy adjustment coefficient can be multiplied by a preset perception threshold to obtain the adjusted target perception threshold. The preset perception threshold is a perception threshold set in advance in the network topology map automatic generation system, which can be directly obtained in the scenario of dynamic updating of the initial network topology map.

[0063] It is understandable that the target perception threshold is the threshold for judging whether the structure in the initial network topology graph has changed significantly enough and whether it is worth triggering an update of the initial network topology graph.

[0064] In one implementation of this invention, the method for generating a target network topology map based on the state information of each of the plurality of devices during the target period and the initial network topology map may further include: Determine the first number of links and the second number of links. The first number of links is the number of links in the initial network topology diagram, and the second number of links is the number of links in the actual topology diagram, which is the actual physical logical connection topology diagram determined according to the manual configuration document.

[0065] Based on the number of the first link, the number of the second link, and the perception bluntness index, a topology perception error index is determined. This perception bluntness index characterizes the instability of the network structure in the network operation and maintenance environment.

[0066] It should be understood that the above initial network topology diagram was automatically inferred by the network topology diagram automated generation system based on data such as SNMP protocol, device adjacency information, device ARP table and device MAC address. The above actual topology diagram comes from manual configuration or hardware documentation and can accurately represent the actual physical or logical connection of each device in the above large enterprise computer room.

[0067] For example, the formula for calculating the topology-sensing error index within this target period is as follows: In the formula: Indicates the target period. This represents the topology sensing error index within the target period. The perception bluntness index represents the topological change within the target period. This indicates the number of links in the initial network topology diagram. This indicates the number of links in the actual topology diagram.

[0068] Understandable This represents the difference in the number of links between the initial network topology and the actual topology; the greater the difference, the greater the perception error. The larger the value, the greater the perceived error in the network operation and maintenance environment. A large perceived error will lead to a certain error in the update of the initial network topology.

[0069] In one implementation of this invention, the method for generating a target network topology map based on the state information of each device during the target period and the initial network topology map may further include: Based on the status information of each device within the target period, the number of first paths and the number of second paths are determined. The number of first paths is the number of topology paths included in the initial network topology map within the target period, and the number of second paths is the number of paths in the initial network topology map that have changed within the target period. The perception bluntness index is determined based on the number of the first path, the number of the second path, and the perturbation of the network structure.

[0070] For example, the formula for calculating the topology change perception bluntness index within this target period is as follows: In the formula: Indicates the target period. This represents the perception bluntness index within the target period. This represents the degree of network structure disturbance in the network operation and maintenance environment during the target period. This indicates the number of topological paths included in the initial network topology map within the target period. This indicates the number of paths in the initial network topology that changed during the target period.

[0071] Understandable This indicates inconsistency in the path changes of the initial network topology. The larger the ratio, the greater the deviation between the number of link paths in the initial network topology and the actual number of link paths, which in turn leads to poorer topology awareness and a more unstable network structure. The larger the value, the higher the topological change perception bluntness index within the target period.

[0072] In one implementation of this invention, the link change index of the network structure in the network operation and maintenance environment within the target period is obtained based on the frequency of network link switching and the fluctuation of network traffic within the target period. Then, the network structure disturbance degree within the target period is evaluated based on the neighbor changes of each of the multiple devices. Next, a subset of source-destination node pairs are selected to determine whether there are inconsistent changes in the topology paths within the network operation and maintenance environment within the target period. Then, the topology awareness error index within the target period is calculated based on the difference in the number of links between the initial network topology map and the actual topology map (i.e., the actual network topology map). Since the topology awareness error index can characterize the error of the target network topology map, the adjustment coefficient of the update strategy for the initial network topology map can be effectively determined. This allows for the dynamic adjustment of the awareness threshold in the network topology map update mechanism based on the update lag in the initial network topology map. Finally, the adjusted target awareness threshold is used to determine whether to update the initial network topology map, thereby quickly and accurately generating a network topology map automatically.

[0073] In one implementation of this invention, the method for generating a target network topology map based on the state information of each device during the target period and the initial network topology map may further include: Based on the status information of each device within the target period, determine the number of link status changes of the links included in the initial network topology map within the target period and the traffic detection value of the links included in the initial network topology map within the target period.

[0074] It is understandable that the status information of each device within the target period includes connection status, port usage status, and traffic change status.

[0075] It should be understood that the connection status of each device within the target period represents the link status change of the device. The link status change can represent the structural changes of the link layer. The port usage status of each device within the target period includes the open state and the closed state. The port usage status represents the usage of the port. Therefore, based on the connection status and port usage status of each device within the target period, the number of link status changes of the links included in the initial network topology diagram within the target period can be determined.

[0076] It should be understood that the traffic change status of each device within the target period represents the fluctuation of the device's traffic, thereby determining the traffic detection value of the links included in the initial network topology map within the target period based on the traffic change status within the target period.

[0077] The following example illustrates the change in flow detection value within the target period in an embodiment of the present invention.

[0078] For example, such as Figure 2 As shown, the horizontal axis (X-axis) represents the detection time (seconds / s), the vertical axis (Y-axis) represents the network traffic (gigabits per second / Gbps), and the line graph represents the different network traffic at different detection times. The line graph within the target period can characterize the changes in the traffic detection value within the target period.

[0079] The link state change index is determined based on the number of link state changes, the traffic detection value, and the duration of the target period.

[0080] For example, the formula for calculating the link state change index within this target period is as follows: In the formula: Indicates the target period. This represents the link state change index within the target period. This indicates the number of link state changes within the target period. This indicates the duration of the target period. This represents the flow rate detection value within the target period. This represents the standard deviation of the flow rate detection values ​​within the target period. This represents the average value of the flow rate detection within the target period.

[0081] Understandable This indicates the frequency of link state switching within the target period. The larger the ratio, the more frequent the link state switching occurs within the target period. This represents the traffic fluctuation coefficient within the target period. The larger the value, the more likely the link network in the initial network topology may be in a congested state. The larger the product, the higher the link change index within the target period, which is used to initially assess whether the network topology in the network operation and maintenance environment has changed.

[0082] In one implementation of this invention, the method for determining the link state change index based on the number of link state changes, the traffic detection value, and the duration of the target period specifically includes: Based on the flow rate measurement value, determine the standard deviation and mean of the flow rate measurement value; The link state change index is determined based on the standard deviation of the traffic detection value, the mean of the traffic detection value, the number of link state changes, and the duration of the target period.

[0083] Based on the above embodiments, it should be understood that the ratio between the standard deviation of the traffic detection value and the mean of the traffic detection value can represent the traffic fluctuation coefficient within the target period. The larger the value, the more likely the link network of the initial network topology may be in a congested state.

[0084] The technical solution provided by the above embodiments can bring at least the following beneficial effects: As can be seen from steps S1-S4, device information of each of the multiple devices is obtained, wherein the device information of a device includes the device's Address Resolution Protocol (ARP) table, the device's Media Access Control (MAC) address table, and the device's adjacency information; an initial network topology is determined based on the device information of each of the multiple devices; the status information of each of the multiple devices in the target period is obtained, wherein the status information of a device includes the device's connection status, the device's port usage status, and the device's traffic change status; and a target network topology is generated based on the status information of each of the multiple devices in the target period and the initial network topology. In this embodiment, an initial network topology map can be quickly generated based on the ARP table, MAC address table, and adjacency information of each of the multiple devices. Based on the connection status, port usage status, and traffic change status of each of the multiple devices, it can be quickly and accurately determined whether the initial network topology map has changed from the actual network topology within the target period. Thus, it can be quickly and accurately determined whether to update the initial network topology map. Based on the status information of each of the multiple devices within the target period and the initial network topology map, the initial network topology map is updated, thereby quickly and effectively automatically generating the target network topology map.

[0085] The present invention also proposes an automated network topology map generation system, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the above method steps S1-S4.

[0086] When dividing each function into modules according to its corresponding function. Figure 3 This is a possible structural diagram of a network topology map automated generation system provided in one embodiment of the present invention. This system can also be applied to other exemplary implementation environments and specifically configured in other devices; this embodiment does not limit the implementation environment to which the system is applicable. Figure 3 As shown, a network topology map automated generation system 10 may include: an acquisition module 101, a determination module 102, and a generation module 103.

[0087] The acquisition module 101 is used to acquire device information for each of the multiple devices. The device information of a device includes the device's Address Resolution Protocol (ARP) table, the device's Media Access Control (MAC) address table, and the device's adjacency information. The determination module 102 is used to determine the initial network topology based on the device information of each device; The acquisition module 101 is used to acquire the status information of each device within the target period, wherein the status information of a device includes the connection status of the device, the port usage status of the device, and the traffic change status of the device; The generation module 103 generates a target network topology map based on the status information of each device during the target period and the initial network topology map.

[0088] It should be noted that the order of the above embodiments of the present invention is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0089] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

Claims

1. A method for automatic generation of network topology graph, characterized in that, The method includes: Obtain device information for each of multiple devices, wherein the device information for a device includes the device's Address Resolution Protocol (ARP) table, the device's Media Access Control (MAC) address table, and the device's adjacency information; Based on the device information of each device, an initial network topology is determined; Obtain the status information of each device within the target period, wherein the status information of a device includes the device's connection status, the device's port usage status, and the device's traffic change status; Based on the state information of each device within the target period and the initial network topology map, a target network topology map is generated, including: determining the number of multiple first neighbor devices and multiple second neighbor devices according to the state information of each device within the target period, wherein the number of multiple first neighbor devices is the number of neighbor devices of each device at a first time point, and the number of multiple second neighbor devices is the number of neighbor devices of each device at a second time point, the second time point being the time point following the first time point, and the first time point and the second time point being the time points included within the target period; determining the network structure disturbance degree according to the number of multiple first neighbor devices, the number of multiple second neighbor devices, and the link state change index, wherein the network structure disturbance degree characterizes the degree of network structure change in the network operation and maintenance environment, and the network operation and maintenance environment being the operation and maintenance environment in which each device is located; and updating the initial network topology map to generate the target network topology map if the network structure disturbance degree is greater than the target perception threshold. The method further includes: determining the number of link state changes of the links included in the initial network topology map and the traffic detection value of the links included in the initial network topology map within the target period based on the state information of each device within the target period; determining the standard deviation and mean of the traffic detection value based on the traffic detection value; and determining the link state change index based on the standard deviation, the mean, the number of link state changes, and the duration of the target period.

2. The method of claim 1, wherein, The method further includes: A first timestamp and a second timestamp are determined. The first timestamp is the timestamp of the change in the status information of each device, and the second timestamp is the timestamp of the link update of the initial network topology map. Based on the first timestamp, the second timestamp, and the topology-aware error index, the update strategy adjustment coefficient of the initial network topology graph is determined, wherein the topology-aware error index represents the error in generating the target network topology graph; The target perception threshold is determined by adjusting the coefficients according to the update strategy and the preset perception threshold.

3. The method of claim 2, wherein, The method further includes: Determine the first number of links and the second number of links. The first number of links is the number of links in the initial network topology diagram, and the second number of links is the number of links in the actual topology diagram. The actual topology diagram is the actual physical logical connection topology diagram determined according to the manual configuration document. Based on the number of the first link, the number of the second link, and the perception bluntness index, a topology perception error index is determined, wherein the perception bluntness index characterizes the instability of the network structure in the network operation and maintenance environment.

4. The method of claim 3, wherein, The method further includes: Based on the status information of each device within the target period, the number of first paths and the number of second paths are determined. The number of first paths is the number of topology paths included in the initial network topology map within the target period, and the number of second paths is the number of paths in the initial network topology map that have changed within the target period. The perception bluntness index is determined based on the number of the first path, the number of the second path, and the network structure perturbation.

5. The method for automatic generation of network topology map of claim 1, wherein, The step of obtaining device information for each of the multiple devices includes: Log in using Simple Network Management Protocol (SNMP) and Remote Connection Control Protocol (SSH) or Tenet to obtain the ARP table and MAC address table of each device. According to the Link Layer Discovery Protocol (LLDP), the adjacency information of each device is obtained.

6. The method of claim 1, wherein, The step of determining the initial network topology based on the device information of each of the plurality of devices includes: The network connection relationship of each device is determined based on the ARP table, the MAC address table, and the adjacency information of each device; The initial network topology is determined based on the network connectivity and the graph building engine.

7. A network topology map automatic generation system comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the network topology map automatic generation method as described in any one of claims 1-6.

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