Network topology adjustment method and apparatus

Abstract

An embodiment of the specification provides a network topology adjustment method and device, the method comprising: firstly acquiring traffic data of preset key nodes in a network topology, inputting the acquired traffic data into a traffic prediction model to perform traffic feature prediction, and outputting traffic parameters and a traffic trend sequence; secondly performing network topology analysis based on network topology data, the traffic parameters and the traffic trend sequence to obtain key nodes and key paths of the network topology; thirdly generating a node deployment strategy of a convergence node in the key nodes according to the key nodes and the key paths; and finally performing deployment adjustment on the convergence node in the network topology based on the node deployment strategy to realize flexible adjustment and management of the network topology.

Description

TECHNICAL FIELD

[0001] The present document relates to the technical field of Internet, and particularly relates to a network topology structure adjustment method and device. BACKGROUND

[0002] With the continuous development of information technology, network has become an indispensable infrastructure in modern society, and efficient network management can improve the speed and quality of traffic data transmission, thereby improving the overall service level and user experience. As the core of network management, the adjustment of network topology structure greatly affects the operation efficiency of the network.

[0003] At present, many network management systems manage traffic data and network topology structure through pre-set configurations to ensure the stable operation of the network. However, with the rapid growth of business and increasingly complex demands, when processing large-scale data or high-concurrency requests, the pre-set configuration-based management of traffic data may not effectively reduce network latency and may not timely adjust the network topology structure to ensure network operation efficiency. Therefore, how to adjust the network topology structure to improve the efficiency of traffic data management and transmission has become a problem to be solved in the current Internet technical field. SUMMARY

[0004] An embodiment of the present specification aims to provide a network topology structure adjustment method and device to realize flexible adjustment and management of the network topology structure.

[0005] To solve the above technical problems, an embodiment of the present specification is implemented as follows:

[0006] In a first aspect, an embodiment of the present specification provides a network topology structure adjustment method, comprising:

[0007] obtaining traffic data of a pre-set key node in a network topology structure, inputting the traffic data into a traffic prediction model to perform traffic feature prediction, and outputting traffic parameters and a traffic trend sequence;

[0008] performing network topology analysis based on network topology data, the traffic parameters and the traffic trend sequence to obtain key nodes and key paths of the network topology structure;

[0009] generating a node deployment strategy of a convergence node in the key nodes according to the key nodes and the key paths;

[0010] performing deployment adjustment of the convergence node in the network topology structure based on the node deployment strategy.

[0011] In a second aspect, an embodiment of the present specification provides a network topology structure adjustment device, comprising:

[0012] The prediction module is configured to acquire traffic data of preset key nodes in the network topology structure, input the traffic data into a traffic prediction model for traffic feature prediction, and output traffic parameters and a traffic trend sequence.

[0013] The network topology analysis module is configured to perform network topology analysis based on network topology data, the traffic parameters, and the traffic trend sequence, and obtain key nodes and key paths of the network topology structure.

[0014] The policy generation module is configured to generate a node deployment policy of a convergence node in the key nodes according to the key nodes and the key paths.

[0015] The deployment adjustment module is configured to perform deployment adjustment on the convergence node in the network topology structure based on the node deployment policy.

[0016] In a third aspect, a network topology structure adjustment device is provided in still another embodiment of the present specification, and includes a memory, a processor, and computer executable instructions stored on the memory and executable on the processor, and when the computer executable instructions are executed by the processor, the steps of the network topology structure adjustment method according to the first aspect are implemented.

[0017] In a fourth aspect, a computer readable storage medium is provided in yet another embodiment of the present specification, and the computer readable storage medium is configured to store computer executable instructions, and when the computer executable instructions are executed by a processor, the steps of the network topology structure adjustment method according to the first aspect are implemented.

[0018] In a fifth aspect, a computer program product is provided in yet another embodiment of the present specification, and the computer program product includes a network topology structure adjustment program, and when the network topology structure adjustment program is executed by a processor, the steps of the network topology structure adjustment method according to the first aspect are implemented.

[0019] The network topology adjustment method provided by the embodiment first acquires traffic data of preset key nodes in the network topology structure, inputs the traffic data into a traffic prediction model for traffic feature prediction, and outputs traffic parameters and a traffic trend sequence. The traffic data of the preset nodes provides a data basis for subsequent analysis and improves the accuracy and timeliness of the data. Secondly, network topology analysis is performed based on network topology data, traffic parameters and the traffic trend sequence to obtain key nodes and key paths of the network topology structure, so as to reasonably distribute the traffic data. Then, according to the key nodes and the key paths, a node deployment strategy of a convergence node in the key nodes is generated to determine the deployment position and the deployment quantity of the convergence node. Finally, the convergence node is deployed and adjusted in the network topology structure based on the node deployment strategy, and the network topology structure is adjusted through the deployment adjustment of the convergence node, so that flexible adjustment and management of the network topology structure are realized. BRIEF DESCRIPTION OF DRAWINGS

[0020] In order to more clearly illustrate the technical solutions in the one or more embodiments of the present specification, the drawings needed to be used in the embodiments or prior art description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments described in the present specification, and other drawings can be obtained by those skilled in the art without creative labor.

[0021] Figure 1 A network topology adjustment method processing flowchart is provided for one or more embodiments of the present specification.

[0022] Figure 2 A network topology adjustment method processing flowchart applied to a network topology adjustment scenario is provided for one or more embodiments of the present specification.

[0023] Figure 3 A network topology adjustment device schematic diagram is provided for one or more embodiments of the present specification.

[0024] Figure 4 A structure schematic diagram of a network topology adjustment device is provided for one or more embodiments of the present specification. DETAILED DESCRIPTION

[0025] In order to make the person skilled in the art better understand the technical solutions in one or more embodiments of the present specification, the technical solutions in one or more embodiments of the present specification will be described clearly and completely in the following with reference to the drawings in one or more embodiments of the present specification. Obviously, the described embodiments are only a part of the embodiments of the present specification, not all. Based on one or more embodiments of the present specification, all other embodiments obtained by those skilled in the art without creative labor should belong to the protection scope of the present document.

[0026] The present specification provides an embodiment of a network topology adjustment method:

[0027] Referring to Figure 1 It shows a network topology adjustment method processing flowchart provided by the present embodiment, and the network topology adjustment method provided by the present embodiment specifically includes the following steps S102 to S108.

[0028] Step S102, obtain the traffic data of the preset key node in the network topology structure, input the traffic data into the traffic prediction model to predict the traffic characteristics, and output the traffic parameters and the traffic trend sequence.

[0029] The network topology in the present embodiment refers to the layout of each node and its connection mode in the network. Based on the network topology, the physical or logical connection mode of each node in the network can be described, and the transmission path of the traffic data in the network can also be determined based on the network topology.

[0030] The traffic prediction model refers to a model for predicting the traffic characteristics of the traffic data based on the traffic data in the network topology structure. The input of the traffic prediction model can be traffic data, which can include timestamp, delay, number of data packets, data packet loss rate and / or bandwidth utilization rate. The output of the traffic prediction model can be traffic parameters and / or a traffic trend sequence. The traffic parameters can be traffic peak, traffic trough and / or traffic abnormal state. The traffic trend sequence is a series of traffic prediction values changing over time, reflecting the change trend of the traffic data in the network topology structure in the future period of time. The change trend of the traffic data can be that the traffic data increases, decreases or remains relatively stable over time.

[0031] In specific implementation, for the preset key node pre-set in the network topology structure, the traffic data of the network topology structure is obtained by monitoring the traffic data of the preset key node. The obtained traffic data is input into the trained traffic prediction model to predict the traffic characteristics of the traffic data, so as to obtain the traffic parameters and the traffic trend sequence of the traffic data of the network topology structure.

[0032] In a specific implementation process, in the process of obtaining the traffic data of the preset key nodes in the network topology structure, the traffic data to be obtained can be determined in advance, such as delay, packet loss rate and / or bandwidth utilization, and then the traffic data monitoring software can be configured at the preset key nodes or the Internet of Things (IoT) sensors can be used to obtain the traffic data; wherein the delay is a measure of the network response time, the packet loss rate is the proportion of data packets that do not receive a response, and the bandwidth utilization can be calculated by monitoring the ratio of the total amount of traffic passing through the interface within a certain time to the total available bandwidth of the interface.

[0033] In actual application, in order to more effectively manage and more accurately schedule the traffic data, the traffic data can be predicted based on the traffic prediction model, and based on the traffic parameters and / or traffic trend sequence obtained by prediction, the nodes and traffic data transmission paths in the network topology structure can be reasonably planned by identifying the traffic peak, the traffic trough and / or the traffic trend in advance.

[0034] Specifically, in order to improve the accuracy and processing efficiency of the traffic prediction model, the traffic prediction model can be trained based on the traffic data samples, and in an optional implementation manner provided by the embodiment, the traffic prediction model can be trained and obtained in the following manner:

[0035] The traffic data samples are input into the to-be-trained model to predict the traffic features, and the predicted traffic features of at least one data sample in the traffic data samples are output;

[0036] The error between the output predicted traffic features and the actual traffic features is evaluated, and the training loss is determined;

[0037] The to-be-trained model is adjusted according to the training loss, so as to obtain the traffic prediction model after training is completed.

[0038] The traffic data samples can be the traffic data obtained at the preset key nodes, and the actual traffic features can be the traffic features of the traffic data obtained by data cleaning and aggregation on the traffic data obtained at the preset key nodes.

[0039] For example, taking the training of an LSTM (Long Short-Term Memory) model as a traffic prediction model as an example, first, the traffic data obtained at the preset key nodes and stored in the time series database is taken as a traffic data sample, and an LSTM model is constructed by defining an LSTM network structure, selecting the number of layers and the number of neurons in each layer, second, the training process of the LSTM model can be configured by selecting a loss function, an optimizer, the number of training rounds, etc., the traffic data sample is input into the LSTM model to be trained to predict the traffic features, the error between the predicted traffic features and the actual traffic features is evaluated and the training loss is determined, the model parameters are updated and iteratively optimized based on the training loss, so that the to-be-trained model gradually learns the correct pattern of predicting traffic features, the process is repeated until the loss function converges, and after the loss function converges, the training is completed, and the trained model is taken as a traffic feature prediction model.

[0040] For another example, in the process of evaluating the error between the predicted traffic features and the actual traffic features to determine the training loss, mean square error (MSE) or root mean square error (RMSE) can be used to determine the training loss; when using mean square error (MSE) to determine the training loss, the calculation formula of mean square error (MSE) can be as follows:

[0041] MSE = 1 / n å (yi - y^i)2 i - i ,

[0042] wherein, i yi is the actual traffic feature, i y^i is the model predicted traffic feature, and n is the number of traffic data samples.

[0043] Step S104, based on the network topology data, the traffic parameters and the traffic trend sequence, network topology analysis is performed to obtain the key nodes and key paths of the network topology structure.

[0044] In the above obtaining the traffic data of the preset key node in the network topology structure, and inputting the traffic data into the traffic prediction model to perform traffic feature prediction, and outputting the traffic parameters and the traffic trend sequence, in this step, based on the network topology data, the traffic parameters and the traffic trend sequence, network topology analysis is performed to obtain the key node and the key path in the network topology structure; the network topology analysis refers to analyzing the data of each node and the mutual connection relationship between each node in the network topology structure, the traffic data at each node and the like, identifying the key node and the key path in the network topology structure, and thus performing the process of network optimization and resource allocation based on the data information obtained by the analysis; wherein the key node can be a node carrying higher traffic or being at a key position in the network topology structure.

[0045] The network topology data refers to data describing the mutual connection relationship between each node and each node in the network topology structure. The network topology data can be position data, type data of the node and / or link information data between nodes, and can also be other data for node management in the network topology structure.

[0046] In specific implementation, first, the network topology data is obtained based on the network topology structure, and the network topology analysis is performed based on the obtained network topology data, and the predicted traffic parameters and the traffic trend sequence output by the traffic prediction model, to determine the key node in the network topology structure and the key path connecting the key node, thereby providing a data basis for subsequent determination of the deployment strategy of the aggregation node.

[0047] In the specific execution process, after obtaining the predicted traffic feature, the network topology analysis can be performed on the network topology structure based on the network topology data and the predicted traffic feature to obtain the key node and the key path of the network topology structure; in an optional implementation provided in this embodiment, the network topology analysis is performed based on the network topology data, the traffic parameters and the traffic trend sequence to obtain the key node and the key path of the network topology structure, including:

[0048] Obtaining the network topology data, and constructing a network topology graph model based on the network topology data;

[0049] Evaluating the frequency of each node in the network topology graph model being located in the shortest path based on the evaluation algorithm to obtain a high-frequency node;

[0050] Determining the key node in the high-frequency node based on the traffic parameters and the traffic trend sequence, and determining the path between the key nodes as the key path.

[0051] Specifically, first, network topology data is acquired, a graph model of a network topology structure with network devices as nodes and connection paths as edges is constructed based on the acquired network topology data, second, the frequency of each node on a shortest path in the network topology structure is calculated and counted through an evaluation algorithm, and nodes with high frequency are determined as high-frequency nodes, and finally, nodes with a greater impact on traffic carrying capacity in the high-frequency nodes are identified as key nodes based on the traffic parameters and traffic trend sequence obtained through a traffic prediction model, and the paths between the key nodes are determined as key paths based on the key nodes.

[0052] For example, in the process of evaluating high-frequency nodes, the betweenness centrality can be used to evaluate whether a node can be determined as a high-frequency node. Nodes with high betweenness centrality have a relatively higher frequency on a shortest path, and nodes with low betweenness centrality have a relatively lower frequency on a shortest path.

[0053] Step S106, generating a node deployment strategy of a sink node in the key nodes according to the key nodes and the key paths.

[0054] In a specific implementation, after obtaining the key nodes and the key paths through network topology analysis based on network topology data, traffic parameters and traffic trend sequences, the optimal deployment position and the optimal deployment quantity of sink nodes in the network topology structure can be calculated through a minimum spanning tree algorithm and / or a clustering algorithm in graph theory based on the obtained key nodes and key paths, and a node deployment strategy of the sink nodes is generated.

[0055] The sink node refers to a key node in the network topology structure for centralized processing and forwarding of traffic data. The sink node can acquire traffic data from multiple source nodes and forward the acquired traffic data to the next level or a destination node.

[0056] In a specific implementation process, in order to optimize the network topology structure and improve the transmission efficiency of traffic data, the node deployment strategy of the sink nodes can be determined based on the key nodes and the key paths in the network topology structure, and the sink nodes can be deployed and adjusted based on the node deployment strategy, so as to realize the optimization and adjustment of the network topology structure. In an optional implementation provided in this embodiment, the node deployment strategy of the sink nodes in the key nodes is generated according to the key nodes and the key paths, including:

[0057] Constructing a feature space of the key nodes based on position data and traffic data of the key nodes;

[0058] Calculating the similarity of the key nodes in the feature space according to a clustering algorithm and clustering the key nodes based on the similarity to obtain a cluster center;

[0059] Generating a node deployment strategy at the cluster center based on network configuration data.

[0060] For example, taking the calculation of the deployment location and the deployment number of the aggregation node by using the Minimum Spanning Tree (MST) and the DBSCAN (Density-Based Spatial Clustering of Applications with Noise) algorithm in the clustering algorithm as an example, first, the position data and the traffic data of the key nodes are acquired, and a feature space with the key nodes as the vertices and including the spatial coordinates and the traffic size of the key nodes is constructed, second, the neighborhood size and the minimum point number are selected, the key nodes are clustered based on the DBSCAN algorithm according to the spatial proximity and the traffic similarity of the key nodes, and the clustering centers are obtained, each clustering center can be used as a deployment area of a potential aggregation node; then, the network topology graph model is represented as a graph G=(V, E) based on the minimum spanning tree, where V is the node set and E is the edge set, the weight of each edge can be the connection cost, the delay or the inverse of the bandwidth, the minimum spanning tree T is found on the graph G, and in the minimum spanning tree T, the nodes with high degrees or the nodes located on the critical path are selected as the candidate positions of the aggregation nodes; finally, the specific node deployment strategy of the aggregation node is generated by comprehensively considering the network state, the quality of service demand, the cost benefit and / or other network configuration data.

[0061] In step S108, the aggregation node is deployed and adjusted in the network topology based on the node deployment strategy.

[0062] In the implementation, the optimal deployment location and the optimal deployment number of the aggregation node in the network topology can be determined based on the generated node deployment strategy, and the aggregation node in the network topology is deployed and adjusted based on the determined deployment location and the deployment number of the aggregation node, so as to realize the adjustment of the network topology.

[0063] In the implementation process, the aggregation node can be added, removed and / or migrated by the SDN (Software-Defined Networking) controller in the process of deploying and adjusting the aggregation node in the network topology based on the node deployment strategy.

[0064] The following provides three ways of adding, removing and migrating the aggregation node for deploying and adjusting the aggregation node, and each of the three ways is described in detail.

[0065] (1) Adding the aggregation node

[0066] Specifically, when the traffic data in the network topology structure significantly increases and the existing aggregation nodes cannot meet the demand, in order to support the demand for new traffic data and improve the transmission efficiency of the traffic data, an aggregation node can be added in the network topology structure. In a first optional implementation provided by the embodiment, the aggregation node is deployed and adjusted in the network topology structure based on a node deployment strategy, including:

[0067] The deployment position of the new aggregation node is determined by the SDN controller based on the node deployment strategy;

[0068] The SDN controller sends a configuration command of the new aggregation node to the corresponding network device to enable the new aggregation node at the deployment position;

[0069] The new aggregation node is added to the network topology structure, and the new aggregation node is managed for traffic data.

[0070] In the process of adding the new aggregation node based on the node deployment strategy, the SDN controller can determine the deployment position of the new aggregation node in the network topology structure according to the node deployment strategy, then send a configuration command to the corresponding network device to enable the new aggregation node at the deployment position, and add the new aggregation node to the network topology structure, apply traffic management and security policies to the new aggregation node, and ensure that the new aggregation node can operate as expected.

[0071] (2) Removing an aggregation node

[0072] Specifically, when a certain aggregation node in the network topology structure fails, in order to reduce the influence of the aggregation node on the network topology structure, the aggregation node in the network topology structure can be removed. In a second optional implementation provided by the embodiment, the aggregation node is deployed and adjusted in the network topology structure based on a node deployment strategy, including:

[0073] The deployment position of the aggregation node to be removed is determined by the SDN controller based on the node deployment strategy;

[0074] The flow table in the network topology structure is modified by the SDN controller to redirect the traffic data of the aggregation node to be removed to other aggregation nodes;

[0075] The aggregation node to be removed is removed from the network topology structure.

[0076] In the process of removing an existing aggregation node (to-be-removed aggregation node) in the network topology based on the node deployment strategy, the SDN controller can first modify the flow table to redirect the traffic data originally processed by the to-be-removed aggregation node to other more active aggregation nodes, and after confirming that the traffic data has successfully bypassed the to-be-removed aggregation node, the SDN controller can issue a removal command to remove the to-be-removed aggregation node from the network topology.

[0077] (3) Migrating aggregation nodes

[0078] Specifically, when the traffic data in the network topology changes, in order to optimize the network topology, the aggregation nodes in the network topology can be migrated to more appropriate positions to achieve better transmission of traffic data. In the third optional implementation provided in this embodiment, the node deployment strategy is used to adjust the deployment of the aggregation nodes in the network topology, including:

[0079] The node deployment strategy determines, by the SDN controller, the deployment positions of the new aggregation nodes and the to-be-migrated aggregation nodes.

[0080] The SDN controller sends a configuration command of the new aggregation node to the corresponding network device to enable the new aggregation node at the deployment position.

[0081] The SDN controller modifies the flow table in the network topology to redirect the traffic data of the to-be-migrated aggregation node to the new aggregation node.

[0082] The to-be-migrated aggregation node is removed from the network topology.

[0083] In the process of migrating the aggregation nodes in the network topology based on the node deployment strategy, the addition of new aggregation nodes and the removal of old aggregation nodes are involved, that is, the SDN controller can first configure and activate the new aggregation nodes according to the above steps of adding aggregation nodes, and gradually migrate the traffic data from the to-be-migrated aggregation nodes to the new aggregation nodes by gradually modifying the flow table. After completing the migration of the traffic data, the SDN controller can remove the to-be-migrated aggregation nodes according to the above steps of removing aggregation nodes, thereby achieving the migration of the aggregation nodes in the network topology.

[0084] In actual applications, after the aggregation nodes are deployed and adjusted based on the node deployment strategy of the aggregation nodes to achieve the adjustment of the network topology, in order to enhance the data security and protection capability of the network topology while maintaining the stable state of the network topology, the traffic data of the aggregation nodes in the network topology can also be encrypted based on the adjusted network topology, and an access control mechanism can be configured at the aggregation nodes to implement access authentication.

[0085] In a specific implementation process, after the network topology adjustment, in order to improve the security of the traffic data in the transmission process, the traffic data of the aggregation node can be encrypted and access authentication of the access request for the encrypted data is performed; in an optional implementation provided by the embodiment, after the deployment adjustment of the aggregation node in the network topology based on the node deployment strategy, the method further includes:

[0086] encrypting the traffic data of the aggregation node based on an encryption algorithm to obtain encrypted data;

[0087] performing access authentication based on the access request for the encrypted data submitted by the access terminal.

[0088] (1) encrypting the traffic data of the aggregation node based on an encryption algorithm to obtain encrypted data

[0089] Specifically, in order to prevent the traffic data from being read and / or modified by unauthorized parties in the transmission process, and at the same time improve the security of the traffic data in the transmission process, the traffic data of the aggregation node can be encrypted; in an optional implementation provided by the embodiment, the traffic data of the aggregation node is encrypted based on an encryption algorithm to obtain encrypted data, including:

[0090] generating a key conforming to a preset encryption length based on a random number generator;

[0091] transmitting the key to the aggregation node and the destination node, and encrypting the traffic data of the aggregation node based on the key through the encryption algorithm to obtain encrypted data;

[0092] transmitting the encrypted data to the destination node.

[0093] In the process of transmitting the traffic data, in order to improve the security of the traffic data transmission, the encryption length of the key can be preset according to actual needs, the key is generated based on the preset encryption length through the random number generator, and the key management is performed on the key to enable the key to be securely transmitted to the aggregation node and the destination node, and at the same time, the encryption module can be configured on the aggregation node to encrypt the traffic data to obtain encrypted data, and the obtained encrypted data is transmitted to the destination node.

[0094] For example, AES (Advanced Encryption Standard) can be used for encryption, and on this basis, AES-128, AES-192 and AES-256 can be selected as the encryption length.

[0095] (2) performing access authentication based on the access request for the encrypted data submitted by the access terminal

[0096] Specifically, after the traffic data is encrypted, in order to realize that only authorized parties can access the encrypted data and reduce the risk of traffic data leakage, access authentication can be implemented for an access request for accessing the encrypted data after the traffic data is encrypted; in an optional implementation of the embodiment, the access authentication is based on an access request for the encrypted data submitted by an access terminal, and the access authentication includes:

[0097] If the access request submitted by the access terminal is obtained, an authentication prompt is issued to the access terminal;

[0098] An authentication factor submitted by the access terminal based on the authentication prompt is obtained, and the authentication factor is compared with authentication data to obtain a comparison result;

[0099] If the comparison result is consistent, the encrypted data is decrypted based on a key, and the decrypted traffic data is issued.

[0100] After the traffic data is encrypted, if the access terminal attempts to access the encrypted data, the access request submitted by the access terminal can be obtained first, and an authentication prompt is issued to the access terminal based on the access request, so that the access terminal submits an authentication factor based on the authentication prompt, thereby verifying the access authority of the access terminal. After the authentication factor submitted by the access terminal is obtained, the authentication factor can be compared with pre-stored authentication data. The authentication data can include authentication information of all authorized parties. If the comparison result of the authentication factor and the authentication data is consistent, it indicates that the authentication factor submitted by the access terminal matches the authentication data, that is, the access terminal can be considered as a verified authorized party. After authentication succeeds, a decryption module configured by the destination node can decrypt the encrypted data based on a key to restore the encrypted data to original readable traffic data, and issue the decrypted traffic data to the access terminal.

[0101] For example, the authentication factor can be a knowledge factor, such as a username and password, an answer to a verification question, and / or other information that an access party must know. The authentication factor can also be a possession factor, such as a dynamic token, a biometric feature, and / or a verification code received by a mobile phone. The authentication factor can also be a biometric factor, such as a fingerprint and / or facial recognition. The authentication factor required to be submitted by the access terminal can be determined based on the importance of the traffic data to be accessed. For example, when the access terminal requests to access traffic data, single-factor authentication can be implemented on the access terminal, that is, the access terminal can be required to submit any one of a knowledge factor, a possession factor, or a biometric factor based on the authentication prompt. For another example, when the access terminal requests to access high-value traffic data or critical traffic data, multi-factor authentication can be implemented on the access terminal, that is, the access terminal can be required to submit any two or more of a knowledge factor, a possession factor, and / or a biometric factor based on the authentication prompt.

[0102] In addition, if the comparison result of the authentication factor and the authentication data is inconsistent, it indicates that the authentication factor submitted by the access terminal is inconsistent with the authentication data. In this case, the access terminal may have input an error, the access terminal may not be authorized, the authentication information may be expired and / or the authentication information may be changed. In the case where the authentication factor submitted by the access terminal is inconsistent with the authentication data, the access request can be rejected.

[0103] In addition, after the network topology structure is adjusted, in order to improve the security of the traffic data in the transmission process and reduce the delay of the traffic data in the transmission process, the traffic data of the convergence node can be encrypted based on an encryption algorithm to obtain encrypted data, and the encrypted data is decrypted after being transmitted to the destination node. In another optional implementation of the embodiment, after the convergence node is deployed and adjusted in the network topology structure based on the node deployment strategy, the method further comprises:

[0104] According to the preset encryption length and the random number generator, a key conforming to the preset encryption length is generated;

[0105] The key is transmitted to the convergence node and the destination node, and the traffic data of the convergence node is encrypted based on the key through the encryption algorithm to obtain encrypted data;

[0106] The encrypted data is transmitted to the destination node, and the encrypted data is decrypted based on the key.

[0107] Specifically, the encryption length of the key can be preset according to actual needs, the key is generated based on the preset encryption length through the random number generator, and the key is managed to securely transmit the key to the convergence node and the destination node. At the same time, the encryption module and the decryption module can be configured at the convergence node and the destination node respectively, the traffic data is encrypted at the convergence node to obtain encrypted data, the obtained encrypted data is transmitted to the destination node, and the encrypted data is decrypted based on the key at the destination node to restore the encrypted data to the original readable traffic data.

[0108] In actual application, in order to further prevent unauthorized access to traffic data and ensure that only verified and authorized access terminals can access traffic data, access control and authentication mechanisms can be implemented before access authentication based on the access request submitted by the access terminal.

[0109] For example, first, network access control (NAC) is implemented, 802.1X authentication is configured at the aggregation node, an authentication request is made to all access terminals attempting to access traffic data, and user credentials and access policies are stored, then an access control list (ACL) is configured, the ACL is defined on the aggregation node, and the ACL rules are set based on source address, destination address, service type, etc. to allow or deny specific access terminals, ensuring that only authorized access terminals can access traffic data.

[0110] It should be noted that after the network topology is adjusted based on the node deployment strategy, the adjusted network topology can also be monitored and managed; specifically, the running state of the network topology can be monitored through the deployment of an alarm system and an integrated management interface, real-time feedback of data indicators in the network topology can be performed, and flow data analysis can be performed based on the data indicators obtained through monitoring, so that fault elimination and performance optimization of the network topology are performed, finally the effect of the node deployment strategy can be regularly evaluated from the perspectives of network topology running efficiency, cost benefit analysis and / or service quality, and the node deployment strategy can be iteratively optimized according to the evaluation results, so that the aggregation node is continuously deployed and adjusted, thereby realizing continuous optimization of the network topology.

[0111] To sum up, the network topology adjustment method provided in this embodiment first acquires traffic data of preset key nodes in the network topology, inputs the traffic data into a traffic prediction model to perform traffic feature prediction, outputs traffic parameters and a traffic trend sequence, acquires traffic data of the preset nodes to provide a data basis for subsequent analysis, and improves the accuracy and timeliness of the data, secondly, network topology analysis is performed based on network topology data, traffic parameters and the traffic trend sequence, key nodes and key paths of the network topology are obtained to reasonably distribute traffic data, then node deployment strategies of aggregation nodes in the key nodes are generated according to the key nodes and the key paths to determine deployment positions and deployment quantities of the aggregation nodes, and finally the aggregation nodes are deployed and adjusted in the network topology based on the node deployment strategies to perform adding, removing and migration operations on the aggregation nodes, thereby realizing flexible adjustment and management of the network topology through deployment and adjustment of the aggregation nodes.

[0112] The following describes the network topology adjustment method provided in this embodiment in combination with the accompanying drawings. Figure 2 The network topology adjustment method provided in this embodiment is further described below with reference to the application of the network topology adjustment method provided in this embodiment in a network topology adjustment scenario. Figure 2 The network topology adjustment method applied in the network topology adjustment scenario specifically includes steps S202 to S224.

[0113] Step S202, obtain the traffic data of the preset key nodes in the network topology structure.

[0114] Step S204, input the traffic data into the traffic prediction model to perform traffic feature prediction, and output the traffic parameters and the traffic trend sequence.

[0115] Step S206, obtain the network topology data and construct a network topology graph model based on the network topology data.

[0116] Step S208, evaluate the frequency of each node in the network topology graph model being located in the shortest path based on an evaluation algorithm, and obtain high-frequency nodes.

[0117] Step S210, determine the key nodes among the high-frequency nodes based on the traffic parameters and the traffic trend sequence, and determine the paths between the key nodes as key paths.

[0118] Step S212, construct a feature space of the key nodes based on the position data and the traffic data of the key nodes.

[0119] Step S214, calculate the similarity of the key nodes in the feature space according to a clustering algorithm, and cluster the key nodes based on the similarity to obtain clustering centers.

[0120] Step S216, generate a node deployment strategy at the clustering centers based on network configuration data.

[0121] Step S218, deploy and adjust the sink nodes in the network topology structure based on the node deployment strategy, so as to adjust the network topology structure.

[0122] Step S220, generate a key conforming to a preset encryption length according to the preset encryption length and a random number generator, and transmit the key to the sink nodes and the destination nodes.

[0123] Step S222, encrypt the traffic data of the sink nodes based on an encryption algorithm to obtain encrypted data.

[0124] Step S224, transmit the encrypted data to the destination nodes.

[0125] It should be noted that after step S224 is performed, if an access request submitted by an access terminal is obtained, first, an authentication prompt can be issued to the access terminal in response to the access request, and then an authentication factor submitted by the access terminal based on the authentication prompt is obtained, the authentication factor is compared with the authentication data, if the comparison result is consistent, the encrypted data is decrypted based on the key, and the decrypted traffic data is issued.

[0126] It should be noted that any one of steps S202 to S224 or a combination of any multiple steps can be combined into a new implementation mode according to the needs of the implementation deployment, and any one or more technical features in the technical solution composed of steps S202 to S224 can also be combined to form a new implementation mode according to the actual deployment needs, or the technical features in one or more optional implementation modes provided by steps S102 to S108 are combined to form a new implementation mode, which will not be described one by one here.

[0127] Figure 3 A network topology adjustment device provided by an embodiment of the present application is shown in a schematic diagram as shown in the figure, and the device comprises: Figure 3

[0128] A prediction module 302 is configured to obtain traffic data of preset key nodes in a network topology structure, input the traffic data into a traffic prediction model for traffic feature prediction, and output traffic parameters and a traffic trend sequence.

[0129] A network topology analysis module 304 is configured to perform network topology analysis based on network topology data, the traffic parameters and the traffic trend sequence, and obtain key nodes and key paths of the network topology structure.

[0130] A policy generation module 306 is configured to generate a node deployment policy of a convergence node in the key nodes according to the key nodes and the key paths.

[0131] A deployment adjustment module 308 is configured to perform deployment adjustment on the convergence node in the network topology structure based on the node deployment policy.

[0132] The network topology adjustment device provided by the embodiment first runs the prediction module 302 to obtain traffic data of preset key nodes in a network topology structure, input the traffic data into a traffic prediction model for traffic feature prediction, and output traffic parameters and a traffic trend sequence. Secondly, the network topology analysis module 304 is run to perform network topology analysis based on network topology data, traffic parameters and a traffic trend sequence, and obtain key nodes and key paths of the network topology structure. Then, the policy generation module 306 is run to generate a node deployment policy of a convergence node in the key nodes according to the key nodes and the key paths. Finally, the deployment adjustment module 308 is run to perform deployment adjustment on the convergence node in the network topology structure based on the node deployment policy, so as to realize flexible adjustment and management of the network topology structure through deployment adjustment of the convergence node. ​

[0133] The network topology adjustment apparatus provided by one embodiment of the present specification can implement the processes in the foregoing method embodiments and achieve the same functions and effects, which are not repeated here.

[0134] Further, one embodiment of the present specification also provides a network topology adjustment device, Figure 4 A structural schematic diagram of the network topology adjustment device provided by one embodiment of the present specification is shown in the figure, which includes a memory 401, a processor 402, a bus 403 and a communication interface 404. The memory 401, the processor 402 and the communication interface 404 communicate through the bus 403. The communication interface 404 can include an input and output interface, which includes but is not limited to a keyboard, a mouse, a display, a microphone, a loudspeaker, etc. Figure 4

[0135] Figure 4 In the network topology adjustment device provided by one embodiment of the present specification, the memory 401 stores computer executable instructions that can run on the processor 402. When the computer executable instructions are executed by the processor 402, the following processes are implemented:

[0136] Obtain traffic data of preset key nodes in a network topology structure, input the traffic data into a traffic prediction model to perform traffic feature prediction, and output traffic parameters and a traffic trend sequence;

[0137] Perform network topology analysis based on network topology data, the traffic parameters and the traffic trend sequence to obtain key nodes and key paths of the network topology structure;

[0138] Generate a node deployment strategy of a convergence node in the key nodes according to the key nodes and the key paths;

[0139] Perform deployment adjustment of the convergence node in the network topology structure based on the node deployment strategy.

[0140] The network topology adjustment device provided by the present embodiment, through the cooperation of the memory 401, the processor 402, the bus 403 and the communication interface 404, first obtains traffic data of preset key nodes in a network topology structure, inputs the traffic data into a traffic prediction model to perform traffic feature prediction, and outputs traffic parameters and a traffic trend sequence, then performs network topology analysis based on network topology data, the traffic parameters and the traffic trend sequence to obtain key nodes and key paths of the network topology structure, and then generates a node deployment strategy of a convergence node in the key nodes according to the key nodes and the key paths, and finally performs deployment adjustment of the convergence node in the network topology structure based on the node deployment strategy, thereby realizing the adjustment of the network topology structure through the deployment adjustment of the convergence node, and achieving the flexible adjustment and management of the network topology structure.​

[0141] The network topology adjustment device provided by one embodiment of the present specification can implement the processes in the foregoing method embodiments and achieve the same functions and effects, which are not repeated here.

[0142] Further, another embodiment of the present specification also provides a computer readable storage medium for storing computer executable instructions, which, when executed by a processor, implement the following processes:

[0143] Obtain traffic data of preset key nodes in a network topology, input the traffic data into a traffic prediction model to perform traffic feature prediction, and output traffic parameters and a traffic trend sequence;

[0144] Perform network topology analysis based on network topology data, the traffic parameters, and the traffic trend sequence to obtain key nodes and key paths of the network topology;

[0145] Generate a node deployment strategy for a convergence node in the key nodes according to the key nodes and the key paths;

[0146] Perform deployment adjustment on the convergence node in the network topology based on the node deployment strategy.

[0147] The computer readable storage medium provided by the present embodiment first obtains traffic data of preset key nodes in a network topology, inputs the obtained traffic data into a traffic prediction model to perform traffic feature prediction, and outputs traffic parameters and a traffic trend sequence, then performs network topology analysis based on network topology data, the traffic parameters, and the traffic trend sequence to obtain key nodes and key paths of the network topology, and then generates a node deployment strategy for a convergence node in the key nodes according to the key nodes and the key paths, and finally performs deployment adjustment on the convergence node in the network topology based on the node deployment strategy, thereby achieving adjustment on the network topology through deployment adjustment on the convergence node, and achieving flexible adjustment and management of the network topology.

[0148] The computer readable storage medium includes a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk, etc.

[0149] The computer readable storage medium provided by one embodiment of the present specification can implement the processes in the foregoing method embodiments and achieve the same functions and effects, which are not repeated here.

[0150] Further, another embodiment of the present specification also provides a computer program product, which realizes the following flow when executed by a processor:

[0151] Obtain traffic data of preset key nodes in the network topology structure, input the traffic data into a traffic prediction model to perform traffic feature prediction, and output traffic parameters and a traffic trend sequence;

[0152] Perform network topology analysis based on network topology data, the traffic parameters and the traffic trend sequence, and obtain key nodes and key paths of the network topology structure;

[0153] Generate a node deployment strategy of a convergence node in the key nodes according to the key nodes and the key paths;

[0154] Perform deployment adjustment of the convergence node in the network topology structure based on the node deployment strategy.

[0155] The computer program product provided by the embodiment first obtains traffic data of preset key nodes in the network topology structure, inputs the obtained traffic data into a traffic prediction model to perform traffic feature prediction, and outputs traffic parameters and a traffic trend sequence, then performs network topology analysis based on network topology data, the traffic parameters and the traffic trend sequence, and obtains key nodes and key paths of the network topology structure, and then generates a node deployment strategy of a convergence node in the key nodes according to the key nodes and the key paths, and finally performs deployment adjustment of the convergence node in the network topology structure based on the node deployment strategy, so as to realize adjustment of the network topology structure through the deployment adjustment of the convergence node, thereby realizing flexible adjustment and management of the network topology structure.

[0156] The computer program product provided by the embodiment can realize each process in the foregoing method embodiment and achieve the same functions and effects, which will not be repeated here.

[0157] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the present application can adopt a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can adopt a computer program product implemented on one or more computer readable storage media (including but not limited to disk memory, CD-ROM, optical memory, etc.) containing computer usable program codes.

[0158] The computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart block or blocks. Figure 1 one or more flow or blocks Figure 1 means for functionally implementing the steps in the flowchart block or blocks

[0159] These computer program instructions can also be stored in a computer- readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instructions which implement the function specified in the flowchart block or blocks. Figure 1 one or more flow or blocks Figure 1 means for functionally implementing the steps in the flowchart block or blocks

[0160] The computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart block or blocks. Figure 1 one or more flow or blocks Figure 1 means for functionally implementing the steps in the flowchart block or blocks

[0161] In one typical configuration, the computing device includes one or more processors (CPUs), input / output interfaces, network interfaces, and memory.

[0162] The memory can include non-persistent memory and / or volatile memory, such as random access memory (RAM) and / or cache memory, non-volatile memory, etc. in the form of computer-readable storage media. The memory is an example of computer-readable storage media.

[0163] Computer-readable storage media includes permanent and non-permanent, removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data. Examples of computer-readable storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disc read-only memory (CD-ROM), digital versatile discs (DVDs) or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information accessible to a computing device. According to the definition herein, computer-readable storage media does not include transitory media, such as modulated data signals and carrier waves.

[0164] It should also be noted that the terms "comprising", "containing", or any other similar term are intended to encompass non-exclusive inclusions such that a process, method, article, or apparatus that comprises a list of elements does not include those elements solely, but can also include other elements not expressly listed or inherent to such process, method, article, or apparatus.

[0165] Those skilled in the art will appreciate that embodiments of the present application can be devised for a method, a system, or a computer program product. Accordingly, the present application can take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application can take the form of a computer program product on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROMs, optical storage devices, etc.) embodying computer readable program code.

[0166] The embodiments of the present application described above are only used to explain the principles of the present application and should not be used to limit the scope of the present application. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present application should be included in the scope of protection of the present application.

Claims

1. A method for adjusting network topology, characterized in that, The method includes: The system acquires traffic data of preset key nodes in the network topology, inputs the traffic data into a traffic prediction model to predict traffic characteristics, and outputs traffic parameters and traffic trend sequences. A network topology graph model is constructed based on network topology data. The frequency of each node in the network topology graph model being located on the shortest path is evaluated according to the evaluation algorithm to obtain high-frequency nodes. Based on the traffic parameters and the traffic trend sequence, key nodes are determined among the high-frequency nodes, and the paths between the key nodes are determined as critical paths. The feature space of the key nodes is constructed based on the location data and traffic data of the key nodes. The similarity of the key nodes in the feature space is calculated according to the clustering algorithm, and the key nodes are clustered based on the similarity to obtain cluster centers. The node deployment strategy is generated based on the network configuration data at the cluster centers. Based on the node deployment strategy, the deployment of the aggregation node among the key nodes in the network topology is adjusted.

2. The network topology adjustment method according to claim 1, after the step of adjusting the deployment of the aggregation node among the key nodes in the network topology based on the node deployment strategy is executed, it further includes: The traffic data of the aggregation node is encrypted using an encryption algorithm to obtain encrypted data; Access authentication is performed based on the access request for the encrypted data submitted by the access terminal.

3. The network topology adjustment method according to claim 2, wherein encrypting the traffic data of the aggregation node based on an encryption algorithm to obtain encrypted data includes: A key conforming to the preset encryption length is generated based on the preset encryption length and a random number generator; The key is transmitted to the aggregation node and the destination node, and the traffic data of the aggregation node is encrypted using the encryption algorithm based on the key to obtain the encrypted data; The encrypted data is transmitted to the destination node.

4. The network topology adjustment method according to claim 3, wherein the access authentication based on the access request for the encrypted data submitted by the access terminal includes: If an access request submitted by the access terminal is received, an authentication prompt is sent to the access terminal; Obtain the authentication factor submitted by the access terminal based on the authentication prompt, and compare the authentication factor with the authentication data to obtain the comparison result; If the comparison result is a match, the encrypted data is decrypted based on the key and the decrypted traffic data is sent out.

5. A network topology adjustment device, characterized in that, The device includes: The prediction module is configured to acquire traffic data of preset key nodes in the network topology, input the traffic data into the traffic prediction model to predict traffic characteristics, and output traffic parameters and traffic trend sequences. The network topology analysis module is configured to construct a network topology graph model based on network topology data, evaluate the frequency of each node in the network topology graph model being located on the shortest path according to the evaluation algorithm to obtain high-frequency nodes, determine key nodes among the high-frequency nodes based on the traffic parameters and the traffic trend sequence, and determine the path between the key nodes as the critical path. The strategy generation module is configured to construct a feature space for the key nodes based on their location data and traffic data, calculate the similarity of the key nodes in the feature space using a clustering algorithm, cluster the key nodes based on the similarity to obtain cluster centers, and generate node deployment strategies based on network configuration data at the cluster centers. The deployment adjustment module is configured to adjust the deployment of the aggregation node among the key nodes in the network topology based on the node deployment strategy.

6. A network topology adjustment device, characterized in that, The method includes a memory and a processor, wherein the memory stores computer-executable instructions that, when executed on the processor, enable the implementation of the steps of the method described in any one of claims 1-4.

7. A computer-readable storage medium storing computer-executable instructions, characterized in that, When the computer-executable instructions are executed by a processor, they can implement the steps of the method described in any one of claims 1-4.

8. A computer program product, characterized in that, The computer program product includes a network topology adjustment program, which, when executed by a processor, can implement the steps of the method described in any one of claims 1-4.

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