IP network routing simulation reasoning method and device based on community detection

Abstract

As the IP network topology often has a distinct hierarchical structure, the topology can be decomposed into a plurality of areas with high cohesion and low coupling by using a community detection algorithm in a graph theory, and routing update in the areas is selectively diffused to other areas, so that the calculation amount of full-quantity routing simulation can be effectively avoided, and rapid and accurate IP network routing simulation reasoning is realized. The invention provides an IP (Internet Protocol) network routing simulation reasoning method based on community detection, which comprises the following steps of: firstly, dividing the whole network into a plurality of cluster structures based on a community detection algorithm, enabling a shortest path between nodes in a cluster not to pass through other clusters, and taking a division result as a basis for calculating routing update; then, the change operation of the network topology is accurately recognized, network element node pairs needing to be recalculated are further arranged according to network elements involved in change points and cluster information to which the network elements belong, and recalculation and routing updating are conducted on SPT trees of the node pairs. Meanwhile, the invention provides an IP network routing simulation reasoning device based on community detection, and the IP network routing simulation reasoning device comprises a topology management module, a network configuration module, a network segmentation module, a change analysis module and a routing calculation module so as to realize the topology segmentation and routing simulation reasoning process of the IP network.

Description

Technical Field

[0001] The invention relates to an IP network routing simulation reasoning method and device based on community detection, belonging to the technical field of communication. Background Art

[0002] When simulating IP networks, there are usually two mainstream technical methods: numerical simulation and analog simulation.

[0003] (1) Simulation runs the actual network operating system through a virtual machine or container, and builds connections between simulated network elements based on a virtualization platform. In the simulation method, each virtual machine contains a complete operating system and network protocol stack to ensure high consistency with the behavior of physical devices. However, this method is limited by the performance of the server and requires the use of a large number of server computing resources to support the simulation of large-scale networks.

[0004] (2) Numerical simulation is mainly based on discrete event simulation technology, which constructs a mathematical model of the virtual network to simulate the physical network. The current mainstream numerical simulation software includes OPNET, OMNeT++ and NS-3. It can define the logical behavior of each network device through program code, such as the forwarding mechanism and queue management of routers and switches, and use message queues and event scheduling to simulate the message transmission, packet loss, retransmission and other operations of the protocol. This method can simulate network behavior and analyze data in a short time and at a low cost, but due to the complexity of the network system, the calculation time is long when simulating a large-scale network, which is difficult to meet the real-time requirements of the digital twin network.

[0005] Network routing simulation is the core content of network simulation, and it is also a necessary prerequisite for key applications such as simulation network planning analysis, fault analysis, and scheduling analysis. In the current mainstream numerical simulation schemes, it is often implemented by writing the protocol stack of each route, which has a large amount of calculation and low simulation efficiency. Since network routing protocols often use the Dijkstra algorithm for routing calculations, some schemes abstract this problem into the shortest path algorithm in graph theory for simulation (for example, CN108418757), and use i-SPF and PRC methods to narrow the convergence range of routing (for example, CN102916879), thereby effectively improving the calculation efficiency of routing.

[0006] Community detection refers to the discovery of densely connected subnetworks in a graph structure. It is one of the important techniques in graph analysis. Its typical algorithm, such as the Girvan-Newman (GN) algorithm, discovers communities in a graph by iteratively removing the edges of the graph based on the edge betweenness centrality (EBC) value of the graph. Summary of the invention

[0007] When simulating routing protocols of IP networks, the Dijkstra algorithm is usually used for routing calculations, which requires a lot of computation and results in low routing calculation efficiency. Although some solutions simulate by abstracting the shortest path algorithm in graph theory and use methods such as i-SPF and PRC to narrow the convergence range of routing, it is still difficult to meet the real-time requirements of deduction calculations in large-scale networks, especially in network environments that require fast response, and there is room for improvement in calculation efficiency.

[0008] Although there are some methods to improve the efficiency of network routing simulation, there is no solution to reduce the calculation scope by dividing the topology into regions. Since IP network topology often has a clear hierarchical structure, the community detection algorithm in graph theory can be used to decompose the topology into several highly cohesive and low-coupled regions, and selectively spread the routing updates within the region to other regions, which can effectively avoid the computational complexity of full routing simulation and achieve fast and accurate IP network routing simulation reasoning.

[0009] In order to achieve the above object, the technical solution of the present invention is as follows: an IP network routing simulation reasoning method based on community detection, the method comprising the following steps:

[0010] Step 1: According to the network simulation scenario, build an IP network simulation environment through a visualization or programming interface, extract the basic model of network elements and links, instantiate simulation network elements, add links between network elements, and form the initial network topology.

[0011] Step 2: Configure the IP simulation protocol for the simulated network element through the configuration channel, including the configuration of the interconnection interface between network elements, the configuration of the ISIS protocol neighbor relationship, and the configuration of the network routing prefix import. After the initial configuration is completed, start the simulation task.

[0012] Step 3: The routing calculation module performs a full calculation of the entire network based on the initial network topology and network element configuration information through the Dijkstra algorithm to obtain an SPT tree with each node as the root node, and uses this SPT tree to build a node routing library.

[0013] Step 4: Take the simulated network topology and the SPT tree set with each node as the root node as the input of the GN algorithm. The GN algorithm cuts the network topology structure to obtain several network clusters. Each network cluster is a sub-topology of the entire simulated network topology. The final effect of the cutting is that there is no path from any node in each cluster to the SPT tree of other nodes in the cluster that passes through another cluster.

[0014] Step 5: Based on the steady-state IP simulation network, change the network topology structure, including adding and deleting links, adding and deleting nodes, and changing the configuration of network elements.

[0015] Step 6: After the change is completed, the change analysis module accurately identifies the change operations of the network topology, including adding intra-cluster links, deleting intra-cluster links, adding inter-cluster links, deleting inter-cluster links, adding single-link nodes within a cluster, deleting single-link nodes within a cluster, adding multi-link nodes within a cluster, and deleting multi-link nodes within a cluster. According to the network elements involved in the above change points and the network cluster information segmented by the GN algorithm, the network element node pairs that need to be recalculated are further arranged, and the routing calculation module recalculates the SPT tree of the node pairs and updates the routes.

[0016] Among them, step 4 is as follows:

[0017] Topology Figure 5 It is shown that the community detection algorithm is used to divide the topology into clusters. Since network routing protocols often use shortest path algorithms such as Dijsktra for path calculation, community detection algorithms based on edge betweenness (the number of shortest paths passing through an edge in the network) are more suitable for regional division, such as the Girvan-Newman algorithm (GN algorithm), Newman fast algorithm (NF algorithm) and related improved algorithms. Such algorithms measure the degree of community division based on modularity. The modularity calculation method is as follows:

[0018]

[0019] In the formula, e is a k×k matrix, e ij represents the ratio of the number of edges connecting vertices between community i and community j to the total number of edges, a i represents the ratio of the number of edges connected to the nodes in community i to the total number of edges, Tr(e) represents the sum of the ratios of the number of edges within the same community to the total number of edges, ||E 2 || represents the sum of matrix elements. The modularity function Q measures the difference between the proportion of edges within a community and the proportion of edges within a community in the case of random connection. When Q is close to 1, the community structure of the network is better.

[0020] However, in the IP network simulation partitioning scenario, in order to decompose the network structure into highly cohesive and low-coupled regional structures and support the simulation goals of local routing calculation and global diffusion, modularity evaluation cannot be used as the only evaluation parameter. At the same time, it is necessary to add the restriction that the shortest path between nodes in the region does not cross other regions, that is, the routing independence judgment. In summary, the steps for regional division are as follows: Figure 4 As shown, the details are as follows:

[0021] Step 4.1: Convert the network topology G = (V, E) into the adjacency matrix A for easy calculation. Convert the topology G into the adjacency matrix A.

[0022]

[0023] Step 4.2: Calculate the shortest paths between all nodes and obtain the edge betweenness of each edge in turn. The following is the edge betweenness adjacency matrix B of topology G.

[0024]

[0025] Step 4.3: Delete the edge with the largest edge betweenness. If a deleted edge is generated in step 5, exclude the deleted edge and determine the largest edge to be deleted. At the same time, set the value of the corresponding edge in the adjacency matrix A to zero.

[0026] Step 4.4: Determine whether there is a new independent cluster structure in the topology after deleting the edge, R n Represents the n independent regions divided. and If no independent cluster structure is obtained, repeat steps 4.2 and 4.3.

[0027] Step 4.5: Determine whether the shortest path between nodes in each cluster after deleting the edge passes through the external node of the cluster. Use P(u,v) to represent the shortest path between nodes u and v. For all edges e∈P(u,v) (where e=(x,y) is an edge in the path P(u,v)), satisfying x,y∈R i , then the cluster R i Meet the routing independence judgment. If it does not meet the routing independence, restore the deleted edge e re , stored in set E re and repeat steps 4.3 and 4.4.

[0028] Step 4.6: Calculate the modularity Q(n) of the split topology and store it in the set Q total middle,

[0029] Step 4.7: Determine whether all nodes have degenerated into an independent cluster (excluding the restored edges), that is, remove E from the adjacency matrix. re The values ​​of all other elements are zero, and the formula is expressed as If the target is not reached, repeat steps 4.2-4.6.

[0030] Step 4.8: Q total Get the value of modularity Q closest to 1 The cluster corresponding to this value is the optimal partitioning scheme for the current topology.

[0031] A community detection-based IP network routing simulation and reasoning device comprises a topology management module, a network configuration module, a network segmentation module, a change analysis module and a routing calculation module to implement the topology segmentation and routing simulation and reasoning process of the IP network.

[0032] Among them, the topology management module is the functional basis of this simulation equipment. It includes the management of basic network models such as network elements and links, supports the arrangement of network models through graphical interfaces or programming interfaces, defines network topology structures, generates specific simulation network element instances such as routers and switches, and the connection methods between them. The network environment can be built according to specific network simulation scenarios.

[0033] Network configuration module. The network configuration module configures and manages the IP network protocol for the simulation network element instance created by the topology management module, including the configuration of the link interconnection interface IP, the configuration of the ISIS protocol neighbor relationship, the configuration of the network routing prefix import, etc., providing the network control plane configuration capability for the network simulation scenario.

[0034] Network segmentation module: Based on the improved GN algorithm, the network segmentation module divides the cluster structure for the simulation network topology and provides the segmentation results for the change analysis module to support the acceleration of routing reasoning calculation.

[0035] Change analysis module: The change analysis module analyzes and judges the adjustment actions of the topology management module on the network topology during the simulation process, and identifies which cluster and network elements to change and adjust in the case of multiple network clusters, including adding and deleting links, adding and deleting nodes, etc. Combined with the division of network clusters, the change analysis module analyzes and identifies the specific computing tasks required for routing convergence, so as to narrow the calculation scope and increase the convergence speed. Its operation process is as follows: Figure 2 shown.

[0036] Routing calculation module, the routing calculation module includes two aspects of calculation. The first is the initial full calculation when the IP simulation task is started. The SPT tree with each node as the root node is calculated through the Dijkstra algorithm, and the node routing library is built based on this SPT tree. The second is that after the simulation task is started, according to the changes in the topology, the calculation content obtained by the analysis of the change analysis module is used as input, and the node SPT tree calculation and routing library construction are executed on demand.

[0037] This solution is generally divided into two stages. In the first stage, after configuring the control plane of the ISIS protocol based on the constructed simulation network, the traditional Dijkstra algorithm is first used to calculate the full SPT tree of all nodes in the topology to form an SPT tree set with the node as the root, and the node routing library is further generated from the SPT tree; then the SPT tree set is used as input to cut the network topology through the GN algorithm to form several network clusters, ensuring that the SPT path between any two nodes in a cluster does not pass through another cluster. In the second stage, based on the first stage, when the network topology structure is changed during the network simulation process, the topology change action is accurately identified, and the node SPT tree and network cluster information are combined to achieve minimized protocol convergence calculation and improve network simulation efficiency.

[0038] Compared with the prior art, the advantages of the present invention are as follows: the present invention aggregates and divides the network nodes in the network, and uses the improved community detection algorithm to decompose the topology into several areas with high cohesion and low coupling. Compared with the traditional community detection algorithm, the routing independence judgment of the cluster is increased, thereby ensuring the accuracy of cluster division and providing a local calculation basis for network routing behavior simulation; in the network topology change scenario, by identifying specific change types such as adding and deleting nodes, adding and deleting links, on the basis of the Dijkstra algorithm, the network cluster information divided by the improved community detection algorithm is superimposed, and the minimized SPT tree convergence calculation amount is analyzed in combination with evaluation, and the routing convergence calculation is further performed through the incremental SPT tree construction result, and finally a steady-state network routing library is formed. This routing calculation method through network cluster division can effectively improve the overall efficiency of large-scale network simulation. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 A schematic diagram of an IP network routing simulation and reasoning device based on community detection provided in an embodiment of the present application;

[0040] Figure 2 A schematic diagram of a cluster-based topology change routing calculation process provided in an embodiment of the present application;

[0041] Figure 3 Interaction flow between modules provided for the embodiments of the present application;

[0042] Figure 4 A network segmentation process based on community detection provided in an embodiment of the present application;

[0043] Figure 5 An example of a topology to be divided provided in an embodiment of the present application;

[0044] Figure 6 The independent cluster structure topology after division provided by the embodiment of the present application;

[0045] Figure 7 A schematic diagram of a newly added intra-cluster link in one of the topology change situations provided in an embodiment of the present application;

[0046] Figure 8 A schematic diagram of deleting a link within a cluster in one of the topology change situations provided in an embodiment of the present application;

[0047] Fig. 9 A schematic diagram of a newly added inter-cluster link in one of the topology change situations provided in an embodiment of the present application;

[0048] Fig.10A schematic diagram of deleting an inter-cluster link in one of the topology change situations provided in an embodiment of the present application;

[0049] Fig.11 A schematic diagram of a newly added single-link node in a cluster in one of the topology change situations provided in an embodiment of the present application;

[0050] Fig.12 A schematic diagram of deleting a single-link node in a cluster in one of the topology change situations provided in an embodiment of the present application;

[0051] Fig.13 A schematic diagram of a newly added multi-link node in a cluster in one of the topology change situations provided in an embodiment of the present application;

[0052] Fig.14 A schematic diagram of deleting a multi-link node within a cluster, which is one of the topology change situations provided in an embodiment of the present application. DETAILED DESCRIPTION

[0053] In order to deepen the recognition and understanding of the present invention, the present invention is further described below in conjunction with the accompanying drawings and implementation methods.

[0054] Embodiment 1: The present invention covers the following modules, such as Figure 1 As shown: an IP network routing simulation and reasoning device based on community detection, the device includes a topology management module, a network configuration module, a network segmentation module, a change analysis module and a routing calculation module to realize the topology segmentation and routing simulation and reasoning process of the IP network.

[0055] Module 1: Topology management module. The topology management module is the functional basis of this simulation equipment. It includes the management of basic network models such as network elements and links, supports the arrangement of network models through graphical interfaces or programming interfaces, defines network topology structures, generates specific simulation network element instances such as routers and switches, and the connection between them, and can build a network environment according to specific network simulation scenarios.

[0056] Module 2: Network configuration module. The network configuration module configures and manages the IP network protocol for the simulated network element instance created by the topology management module, including the configuration of the link interconnection interface IP, the configuration of the ISIS protocol neighbor relationship, the configuration of the network routing prefix import, etc., providing the network control plane configuration capability for the network simulation scenario.

[0057] Module 3: Network segmentation module. Based on the improved GN algorithm, the network segmentation module performs cluster structure division for the simulation network topology and provides the division results for the change analysis module to support the acceleration of routing reasoning calculation.

[0058] Module 4: Change Analysis Module. The change analysis module analyzes and judges the adjustment actions of the topology management module on the network topology during the simulation process, and identifies which cluster and network elements to change and adjust in the case of multiple network clusters, including adding and deleting links, adding and deleting nodes, etc. Combined with the division of network clusters, it analyzes and identifies the specific computing tasks required for routing convergence, so as to narrow the calculation scope and increase the convergence speed. Its operation process is as follows: Figure 2 shown.

[0059] Module 5: Routing calculation module. The routing calculation module includes two aspects of calculation. The first is the initial full calculation when the IP simulation task is started. The SPT tree with each node as the root node is calculated by the Dijkstra algorithm, and the node routing library is constructed based on this SPT tree. The second is that after the simulation task is started, according to the topology changes, the calculation content obtained by the change analysis module is used as input, and the node SPT tree calculation and routing library construction are performed on demand.

[0060] The interaction process between modules is as follows Figure 3 As shown, the specific process is as follows:

[0061] Step 1: According to the network simulation scenario, build an IP network simulation environment through a visualization or programming interface, extract the basic model of network elements and links, instantiate simulation network elements, add links between network elements, and form the initial network topology.

[0062] Step 2: Configure the IP simulation protocol for the simulated network element through the configuration channel, including the configuration of the interconnection interface between network elements, the configuration of the ISIS protocol neighbor relationship, the configuration of the network routing prefix import, etc. After the initial configuration is completed, start the simulation task.

[0063] Step 3: The routing calculation module performs a full calculation of the entire network based on the initial network topology and network element configuration information through the Dijkstra algorithm, obtains an SPT tree with each node as the root node, and uses this SPT tree to build a node routing library.

[0064] Step 4: The simulated network topology and the SPT tree set with each node as the root node are used as the input of the GN algorithm. The GN algorithm cuts the network topology structure to obtain several network clusters. Each network cluster is a sub-topology of the entire simulated network topology. The final effect of the cutting is that there is no path from any node in each cluster to the SPT tree of other nodes in the cluster that passes through another cluster. This embodiment is illustrated by dividing into two clusters.

[0065] The following is a detailed description of step 4:

[0066] Topology Figure 5It is shown that the community detection algorithm is used to divide the topology into clusters. Since network routing protocols often use shortest path algorithms such as Dijsktra for path calculation, community detection algorithms based on edge betweenness (the number of shortest paths passing through an edge in the network) are more suitable for regional division, such as the Girvan-Newman algorithm (GN algorithm), Newman fast algorithm (NF algorithm) and related improved algorithms. Such algorithms measure the degree of community division based on modularity. The modularity calculation method is as follows:

[0067]

[0068] In the formula, e is a k×k matrix, e ij represents the ratio of the number of edges connecting vertices between community i and community j to the total number of edges, a i represents the ratio of the number of edges connected to the nodes in community i to the total number of edges, Tr(e) represents the sum of the ratios of the number of edges within the same community to the total number of edges, ||E 2 || represents the sum of matrix elements. The modularity function Q measures the difference between the proportion of edges within a community and the proportion of edges within a community in the case of random connection. When Q is close to 1, the community structure of the network is better.

[0069] However, in the IP network simulation partitioning scenario, in order to decompose the network structure into highly cohesive and low-coupled regional structures and support the simulation goals of local routing calculation and global diffusion, modularity evaluation cannot be used as the only evaluation parameter. At the same time, it is necessary to add the restriction that the shortest path between nodes in the region does not cross other regions, that is, the routing independence judgment. In summary, the steps for regional division are as follows: Figure 4 As shown, the details are as follows:

[0070] Step 4.1: Convert the network topology G = (V, E) into the adjacency matrix A for ease of calculation. The following is an example of converting the topology G into the adjacency matrix A.

[0071]

[0072] Step 4.2: Calculate the shortest paths between all nodes and obtain the edge betweenness of each edge in turn. The following is an example of the edge betweenness adjacency matrix B of topology G.

[0073]

[0074] Step 4.3: Delete the edge with the largest edge betweenness. If a deleted edge is generated in step 5, exclude the deleted edge and determine the largest edge to be deleted. At the same time, set the value of the corresponding edge in the adjacency matrix A to zero.

[0075] Step 4.4: Determine whether there is a new independent cluster structure in the topology after deleting the edge, R nRepresents the n independent regions divided. and If no independent cluster structure is obtained, repeat steps 4.2 and 4.3. Figure 6 This is an example graph after splitting the topology G and obtaining independent clusters for the first time.

[0076] Step 4.5: Determine whether the shortest path between nodes in each cluster after deleting the edge passes through the external node of the cluster. Use P(u,v) to represent the shortest path between nodes u and v. For all edges e∈P(u,v) (where e=(x,y) is an edge in the path P(u,v)), x,y∈R i , then the cluster R i If the route independence is not met, the deleted edge e is restored. re , stored in set E re and repeat steps 4.3 and 4.4.

[0077] Step 4.6: Calculate the modularity Q(n) of the split topology and store it in the set Q total middle.

[0078] Step 4.7: Determine whether all nodes have degenerated into an independent cluster (excluding the restored edges), that is, remove E from the adjacency matrix. re The values ​​of all other elements are zero, and the formula is expressed as If the target is not reached, repeat steps 4.2-4.6.

[0079] Step 4.8: Q total Get the value of modularity Q closest to 1 The cluster corresponding to this value is the optimal partitioning scheme for the current topology.

[0080] Through the above steps 1 to 4, a stable state of the IP simulation network is formed, including the SPT tree information and routing information of each node, as well as the network cluster information divided based on the GN algorithm. Subsequent changes in the network topology structure will be based on this basic information to achieve the purpose of rapid network convergence and simplified calculation under several topology changes.

[0081] Step 5: Based on the steady-state IP simulation network, change the network topology structure, including adding and deleting links, adding and deleting nodes, and other actions, as well as configuration changes of network elements.

[0082] Step 6: After the change is completed, the change analysis module accurately identifies the change operations of the network topology, which may include adding intra-cluster links, deleting intra-cluster links, adding inter-cluster links, deleting inter-cluster links, adding single-link nodes within a cluster, deleting single-link nodes within a cluster, adding multi-link nodes within a cluster, and deleting multi-link nodes within a cluster. According to the network elements involved in the above change points and the network cluster information segmented by the GN algorithm, the network element node pairs that need to be recalculated are further arranged, and the routing calculation module recalculates the SPT tree of the node pairs and updates the routing. The specific calculation situation is determined by different change situations. Examples of the change scenarios involved are as follows:

[0083] Scenario 1: Figure 7 As shown in the figure, if the change is to add a link within the cluster, the nodes within the cluster need to be fully calculated in both directions, and the nodes within the cluster need to be fully calculated in both directions from the nodes in another cluster. No calculation is required between the nodes in the other cluster.

[0084] Scenario 2: If Figure 8 As shown in FIG. 1 , if the change is to delete a link within a cluster, the SPT trees within the cluster, between the cluster and another cluster, and between another cluster and the cluster need to be recalculated bidirectionally between nodes passing through the link, but no calculation is required between nodes in another cluster.

[0085] Scenario 3: If Fig. 9 As shown, if the change is the addition of a new inter-cluster link, this may cause the network cluster structure to change, so jump to step 4 to recalculate and divide the network cluster.

[0086] Scenario 4: Fig.10 As shown in FIG. 1 , if the change is to delete an inter-cluster link, only the SPT tree passing through the link between the nodes of this cluster and another cluster needs to be recalculated, and no calculation is required within this cluster or within another cluster.

[0087] Scenario 5: Fig.11 As shown in the figure, if the change is to add a single-link node in the cluster, the new node in the cluster needs to be calculated pairwise to other nodes in the cluster and another cluster, but no calculation is required between nodes in the other cluster.

[0088] Scenario 6: Fig.12 As shown in FIG. 1 , if the change is to delete a single-link node in a cluster, the nodes from other nodes in this cluster and nodes in another cluster to the deleted node are recalculated, and the nodes from other nodes in this cluster to another cluster and nodes in another cluster do not need to be calculated.

[0089] Scenario 7: Fig.13 As shown in FIG. 1 , if the change is to add a multi-link node within a cluster, two-way calculations are required between nodes within the cluster and between nodes within the cluster and nodes in another cluster, but no calculations are required between nodes in another cluster.

[0090] Scenario 8: Fig.14As shown in the figure, if the change is to delete a multi-link node in the cluster, the other nodes in the cluster and all nodes in another cluster to the node are recalculated unidirectionally. Only the SPT tree passing through the link of the node between other nodes in the cluster and nodes in another cluster needs to be recalculated, and no calculation is required between nodes in another cluster.

[0091] It should be noted that the above embodiments are not intended to limit the protection scope of the present invention, and equivalent changes or substitutions made on the basis of the above technical solutions all fall within the protection scope of the claims of the present invention.

Claims

1. An IP network routing simulation reasoning method based on community detection, characterized in that: The method comprises the following steps: Step 1: According to the network simulation scenario, build an IP network simulation environment through a visualization or programming interface, extract the basic model of network elements and links, instantiate simulation network elements, add links between network elements, and form the initial network topology. Step 2: Configure the IP simulation protocol for the simulated network element through the configuration channel, including the configuration of the interconnection interface between network elements, the configuration of the ISIS protocol neighbor relationship, and the configuration of the network routing prefix import. After the initial configuration is completed, start the simulation task. Step 3: The routing calculation module performs a full calculation of the entire network based on the initial network topology and network element configuration information through the Dijkstra algorithm to obtain an SPT tree with each node as the root node, and uses this SPT tree to build a node routing library. Step 4: Take the simulated network topology and the SPT tree set with each node as the root node as the input of the GN algorithm. The GN algorithm cuts the network topology structure to obtain several network clusters. Each network cluster is a sub-topology of the entire simulated network topology. The final effect of the cutting is that there is no path from any node in each cluster to the SPT tree of other nodes in the cluster that passes through another cluster. Step 5: Based on the steady-state IP simulation network, change the network topology structure, including adding and deleting links, adding and deleting nodes, and changing the configuration of network elements. Step 6: After the change is completed, the change analysis module accurately identifies the change operation of the network topology, and further arranges the network element node pairs that need to be recalculated based on the network elements involved in the change point and the network cluster information segmented by the GN algorithm. The routing calculation module recalculates the SPT tree of the node pair and updates the route.

2. According to the method for simulating and reasoning IP network routing based on community detection in claim 1, it is characterized in that: Step 4 is as follows: Step 4.1: Convert the network topology G = (V, E) into the adjacency matrix A for easy calculation. Convert the topology G into the adjacency matrix A. Step 4.2: Calculate the shortest paths between all nodes and obtain the edge betweenness of each edge in turn. The following is the edge betweenness adjacency matrix B of topology G. Step 4.3: Delete the edge e with the largest edge betweenness max If deleted edges are generated in step 4.5, it is necessary to ensure Then set the corresponding edge value in the adjacency matrix A to zero. Step 4.4: Determine whether there is a new independent cluster structure in the topology after deleting the edge, R n Represents the n independent regions divided. and If no independent cluster structure is obtained, repeat steps 4.2 and 4.

3. Step 4.5: Determine whether the shortest path between the internal nodes of the independent cluster obtained in step 4.4 in the original topology passes through the external nodes of the cluster. Use P(u,v) to represent the shortest path between nodes u and v. For all edges e∈P(u,v) (where e=(x,y) is an edge in the path P(u,v)), satisfy x,y∈R i , then the cluster R i Meet the routing independence judgment. If it does not meet the routing independence, restore the deleted edge e re , stored in set E re and repeat steps 4.3 and 4.

4. Step 4.6: Calculate the modularity Q(n) of the split topology and store it in the set Q total middle, Step 4.7: Determine whether all nodes have degenerated into an independent cluster, that is, except for E in the adjacency matrix re The values ​​of all other elements are zero, and the formula is expressed as If the target is not reached, repeat steps 4.2-4.

6. Step 4.8: Q total Get the value of modularity Q closest to 1 The cluster corresponding to this value is the optimal partitioning scheme for the current topology.

3. The method for simulating and reasoning IP network routing based on community detection according to claim 1, characterized in that: In step 6, after the change is completed, the change analysis module accurately identifies the change operations of the network topology, including adding intra-cluster links, deleting intra-cluster links, adding inter-cluster links, deleting inter-cluster links, adding single-link nodes within a cluster, deleting single-link nodes within a cluster, adding multi-link nodes within a cluster, and deleting multi-link nodes within a cluster. Different routing inference strategies are executed for the above scenarios.

4. An IP network routing simulation and reasoning device based on community detection, characterized in that: Used to implement the IP network routing simulation reasoning method based on community detection as described in any one of claims 1-3, the device includes a topology management module, a network configuration module, a network segmentation module, a change analysis module and a routing calculation module to implement the topology segmentation and routing simulation reasoning process of the IP network.

5. The IP network routing simulation reasoning device based on community detection according to claim 4 is characterized in that: The topology management module is the functional basis of this simulation equipment. It includes the management of basic network models such as network elements and links. It supports the arrangement of network models through a graphical interface or programming interface, defines the network topology structure, generates specific simulation network element instances such as routers and switches, and the connection between them. The network environment can be built according to the specific network simulation scenario. The network configuration module configures and manages the IP network protocol for the simulated network element instance created by the topology management module, including the configuration of the link interconnection interface IP, the configuration of the ISIS protocol neighbor relationship, the configuration of the network routing prefix import, etc., providing the network control plane configuration capability for the network simulation scenario. Network segmentation module: Based on the improved GN algorithm, the network segmentation module divides the cluster structure for the simulation network topology and provides the segmentation results for the change analysis module to support the acceleration of routing reasoning calculation. Change analysis module: The change analysis module analyzes and judges the adjustment actions of the topology management module on the network topology during the simulation process, and identifies which cluster and network elements to change and adjust in the case of multiple network clusters, including adding and deleting links, adding and deleting nodes, etc. Combined with the division of network clusters, the change analysis module analyzes and identifies the specific computing tasks required for routing convergence, so as to narrow the calculation scope and increase the convergence speed. Routing calculation module,The routing calculation module includes two aspects of calculation. The first is the first full calculation when the IP simulation task is started. The SPT tree with each node as the root node is calculated by the Dijkstra algorithm, and the node routing library is constructed based on this SPT tree; Second, after the simulation task is started, according to the topology changes, the calculation content obtained by the change analysis module is used as input to execute the SPT tree calculation and routing library construction of the node on demand.

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