Virtual network topology recommendation method and device, equipment and storage medium

Abstract

The invention relates to a virtual network topology recommendation method and device, equipment and a storage medium. The method comprises the following steps: acquiring virtual network configuration information which is sent by a client and comprises the number of to-be-deployed flow path nodes, a flow inlet node, a plurality of flow outlet nodes and service nodes; based on the virtual network configuration information, constructing a virtual network topology, and constructing a link cost model; adding a virtual end point node in the link cost model, and adding a virtual link between the virtual end point node and each flow outlet node, so that the cost of each virtual link is the same; iteratively executing a preset loop body to update a pheromone concentration value of a link in the link cost model; updating the pheromone concentration value of the link in the link cost model again according to a preset link importance weight and the newest pheromone concentration value of the link; and searching an optimal target virtual network topology from the updated link cost model. By adopting the method, the transmission efficiency of the virtual network can be improved.

Description

Technical Field

[0001] The present application relates to the field of cloud computing technology, and in particular to a virtual network topology recommendation method, apparatus, computer equipment, computer-readable storage medium, and computer program product. Background Art

[0002] In the current information technology field, the demand for cloud computing by individual users, enterprises and organizations has increased dramatically. Cloud platforms can be used not only to host traditional business applications, but also to explore innovative cloud-based solutions. For example, the promotion of NFV (Network Functions Virtualization) transforms traditional hardware-dependent network functions (such as firewalls, load balancing, network address translation, etc.) into software solutions that can run on standard virtual servers, allowing faster deployment and expansion of network services and responding to changes in business needs.

[0003] However, when customers deploy various components based on a large number of virtual machines on the cloud, it is difficult to ensure the transmission efficiency of the backbone links, as well as the accessibility of the planning or expansion of the network topology, resulting in low transmission efficiency of the cloud virtual network topology. Summary of the invention

[0004] Based on this, it is necessary to provide a virtual network topology recommendation method, device, computer equipment, computer-readable storage medium and computer program product that can improve the virtual network transmission efficiency in response to the above technical problems.

[0005] In a first aspect, the present application provides a virtual network topology recommendation method, comprising:

[0006] Obtain virtual network configuration information sent by the client, the virtual network configuration information including the number of traffic path nodes to be deployed, as well as a traffic entry node, multiple traffic exit nodes and service nodes;

[0007] Based on the virtual network configuration information, a virtual network topology is constructed, and a link cost model is constructed based on the virtual network topology;

[0008] Add a virtual endpoint node to the link cost model, and add a virtual link between the virtual endpoint node and each traffic egress node so that the cost of each virtual link is the same;

[0009] Iteratively executing a preset loop body to update the pheromone concentration value of the link in the link cost model until a preset number of iterations is reached, and then stopping executing the preset loop body;

[0010] According to the preset link importance weight and the latest link pheromone concentration value, the pheromone concentration value of the link in the link cost model is updated again;

[0011] Finding the optimal target virtual network topology from the updated link cost model;

[0012] When the loop body is executed, the following steps are performed:

[0013] A plurality of nodes are selected from a plurality of business nodes, a node set is constructed, the access probability of the traffic entry node and each node in the node set is determined, the node with the highest access probability is determined as the target node to be visited, the target node is visited, the visited target node is removed from the node set, the node set is updated, the access probability of the target node and each node in the updated node set is determined, and the step of determining the node with the highest access probability as the target node to be visited is returned until the number of visited nodes reaches a preset number threshold, and the pheromone concentration value of the link in the link cost model is updated.

[0014] In one embodiment, constructing a link cost model based on a virtual network topology includes:

[0015] Determine the latency and bandwidth between traffic ingress nodes, traffic egress nodes, and service nodes;

[0016] Determine the link cost between the traffic ingress node, traffic egress node, and service node based on latency and bandwidth;

[0017] A link cost model is constructed based on the virtual network topology and link cost.

[0018] In one embodiment, determining the delay and bandwidth between the traffic ingress node, the traffic egress node, and the service node includes:

[0019] Based on the virtual network configuration information, collect the virtual machine information, network connection information and communication path of the traffic ingress node, traffic egress node and business node;

[0020] Based on the virtual machine information, network connection information and communication path of the traffic inlet node, traffic outlet node and business node, the communication between the traffic inlet node, traffic outlet node and business node is simulated, and the communication data during the communication process is collected;

[0021] Based on the communication data, the latency and bandwidth between the traffic ingress node, traffic egress node, and service nodes are determined.

[0022] In one embodiment, finding the optimal target virtual network topology from the updated link cost model includes:

[0023] Based on the pheromone concentration value and link cost of the link in the updated link cost model, a target virtual network topology with the minimum sum of link costs of the path from the traffic entry node to the traffic exit node is found from the updated link cost model through breadth-first search.

[0024] In one embodiment, updating the pheromone concentration value of a link in the link cost model includes:

[0025] Determine the increment of pheromone concentration value on the visited link in the link cost model;

[0026] According to the pheromone concentration value increment and the preset pheromone volatility coefficient, the pheromone concentration value of the visited link is updated;

[0027] According to the pheromone concentration value on the unvisited link in the link cost model and the preset pheromone volatility coefficient, the pheromone concentration value of the unvisited link is updated.

[0028] In one embodiment, before determining the delay and bandwidth between the traffic ingress node, the traffic egress node, and the service node based on the communication data, the method further includes:

[0029] The communication data is preprocessed, where the data preprocessing includes at least one of dirty data removal and missing data processing.

[0030] In a second aspect, the present application also provides a virtual network topology device, including:

[0031] A configuration information acquisition module, used to acquire virtual network configuration information sent by the client, the virtual network configuration information including the number of traffic path nodes to be deployed, as well as a traffic entry node, multiple traffic exit nodes and service nodes;

[0032] A model building module is used to build a virtual network topology based on the virtual network configuration information, and to build a link cost model based on the virtual network topology; to add a virtual end node to the link cost model, and to add a virtual link between the virtual end node and each traffic exit node so that the cost of each virtual link is the same;

[0033] The pheromone concentration value adjustment module is used to iteratively execute a preset loop body to update the pheromone concentration value of the link in the link cost model until a preset number of iterations is reached, and the execution of the preset loop body is stopped; according to the preset link importance weight and the latest pheromone concentration value of the link, the pheromone concentration value of the link in the link cost model is updated again; when the loop body is executed, the following operations are implemented: multiple nodes are screened out from multiple business nodes, a node set is constructed, the access probability of the traffic entry node and each node in the node set is determined, the node with the highest access probability is determined as the target node to be visited, the target node is visited, the visited target node is removed from the node set, the node set is updated, the access probability of the target node and each node in the updated node set is determined, and the step of determining the node with the highest access probability as the target node to be visited is returned until the number of visited nodes reaches a preset number threshold, and the pheromone concentration value of the link in the link cost model is updated;

[0034] The topology determination module is used to find the optimal target virtual network topology from the updated link cost model.

[0035] In a third aspect, the present application further provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the steps in any one of the above-mentioned virtual network topology recommendation method embodiments are implemented.

[0036] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the steps in any one of the above-mentioned virtual network topology recommendation method embodiments are implemented.

[0037] In a fifth aspect, the present application also provides a computer program product, including a computer program, which, when executed by a processor, implements the steps in any one of the above-mentioned virtual network topology recommendation method embodiments.

[0038] The virtual network topology recommendation method obtains the virtual network configuration information sent by the client, builds a link cost model based on the virtual network configuration information, adds a virtual terminal node in the link cost model, and adds a virtual link between the virtual terminal node and each traffic exit node, and the virtual link cost is the same. On the one hand, in view of the problem that there are multiple traffic exits in the virtual network of the cloud user, in the problem modeling stage, a virtual terminal node is fictionalized, and each traffic exit is connected to the virtual terminal node with the same link cost, which is conducive to balancing the link cost in the subsequent process of determining the optimal target virtual network topology based on the link cost model, and neutralizing the influence of the newly added false link on the determination of the target virtual network topology, which is conducive to improving the accuracy of determining the target virtual network topology. On the other hand, the improved algorithm loop is designed, by screening out the nodes to be visited from multiple business nodes, constructing the set of nodes to be visited, changing the node access mechanism, determining the access node by determining the probability of the current access node accessing the nodes to be visited in the set of nodes to be visited, and updating the pheromone concentration value of the link in the link cost model, so as to facilitate the diversity of the target network topology exploration, reduce the possibility of falling into the local optimal solution, and improve the convergence speed of the algorithm. Thirdly, by iteratively executing the loop body, the pheromone concentration value of the link in the link cost model is updated, and according to the preset link importance weight and the updated link pheromone concentration value, the pheromone concentration value of the link in the link cost model is updated again, and the optimal target virtual network topology is found from the updated link cost model. In this way, determining the target virtual network topology by the pheromone concentration of the link is conducive to improving the transmission efficiency of the virtual network. At the same time, combined with the preset link importance requirements, the difficulty of solving the problem in the case of extremely complex and large client networks is fully considered, so that the preset link importance fully intervenes in the topology optimization process, making the results more customer-oriented, which is conducive to improving the matching degree between the target network topology and client requirements. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the drawings required for use in the embodiments of the present application or related technical descriptions will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0040] Figure 1 A diagram of an application environment of a virtual network topology recommendation method in an embodiment;

[0041] Figure 2 A schematic diagram of a flow chart of a virtual network topology recommendation method in one embodiment;

[0042] Figure 3is a schematic diagram of a link cost model in an embodiment;

[0043] Figure 4 is a schematic diagram of important weights of links in one embodiment;

[0044] Figure 5 A schematic diagram of a flow chart of a virtual network topology recommendation method in another embodiment;

[0045] Figure 6 A schematic diagram of a flow chart of a virtual network topology recommendation method in yet another embodiment;

[0046] Figure 7 is a schematic diagram of a virtual network topology in one embodiment;

[0047] Figure 8 is a schematic diagram of a target virtual network topology in one embodiment;

[0048] Fig. 9 A schematic diagram of a flow chart of a virtual network topology recommendation method in yet another embodiment;

[0049] Fig.10 is a structural block diagram of a virtual network topology device in one embodiment;

[0050] Fig.11 FIG. 4 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0051] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0052] The virtual network topology recommendation method provided in the embodiment of the present application can be applied to Figure 1 In the application environment shown. Among them, the client 102 communicates with the server 104 through the network. The server 104 monitors and manages the virtual network in the cloud service platform. The virtual network is composed of several cloud virtual devices. Each virtual device corresponds to a node. The node can be a traffic entry node, a traffic path node, a traffic exit node or a business node. The data storage system can store the data that the server 104 needs to process. The data storage system can be integrated on the server 104, or it can be placed on the cloud or other network servers.

[0053] Specifically, the client 102 may send virtual network configuration information to the server 104, and the server 104 obtains the virtual network configuration information sent by the client, including the number of traffic path nodes to be deployed, as well as a traffic entry node, multiple traffic exit nodes and service nodes. Secondly, based on the virtual network configuration information, a virtual network topology is constructed, and a link cost model is constructed based on the virtual network topology. A virtual terminal node is added to the link cost model, and a virtual link is added between the virtual terminal node and each traffic exit node so that the cost of each virtual link is the same. Then, a preset loop body is iteratively executed to update the pheromone concentration value of the link in the link cost model until a preset number of iterations is reached, and the execution of the preset loop body is stopped. When the loop body is executed, the following steps are implemented: from multiple A plurality of nodes are screened out from the business nodes, a node set is constructed, the access probability of the traffic entry node and each node in the node set is determined, the node with the largest access probability is determined as the target node to be visited, the target node is visited, the visited target node is removed from the node set, the node set is updated, the access probability of the target node and each node in the updated node set is determined, and the step of determining the node with the largest access probability as the target node to be visited is returned until the number of visited nodes reaches a preset number threshold, the pheromone concentration value of the link in the link cost model is updated, and finally, according to the preset link importance weight and the latest pheromone concentration value of the link, the pheromone concentration value of the link in the link cost model is updated again, and the optimal target virtual network topology is found from the updated link cost model.

[0054] The client 102 may be, but is not limited to, various personal computers, laptops, smart phones, tablet computers, IoT devices, and portable wearable devices. IoT devices may be smart speakers, smart TVs, smart air conditioners, smart car devices, projection devices, etc. Portable wearable devices may be smart watches, smart bracelets, head-mounted devices, etc. Head-mounted devices may be virtual reality (VR) devices, augmented reality (AR) devices, smart glasses, etc. The server 104 may be an independent physical server, or a server cluster or distributed system consisting of multiple physical servers, or a cloud server that provides cloud computing services.

[0055] In an exemplary embodiment, Figure 2 As shown, a virtual network topology recommendation method is provided, which is applied to Figure 1 The server 104 in the example is used for explanation, including the following S100 to S700. Among them:

[0056] S100, obtaining virtual network configuration information sent by the client, the virtual network configuration information includes the number of traffic path nodes to be deployed, as well as a traffic entry node, multiple traffic exit nodes and service nodes.

[0057] The virtual network configuration information is the device information in the virtual network deployed by the user on the public cloud. The virtual network configuration information is used to characterize the client network topology optimization requirements. The virtual network configuration information includes the IDs of the virtual machines corresponding to the traffic inlet nodes, traffic outlet nodes and service nodes in the user's virtual network, as well as the number of traffic path nodes to be deployed. The virtual network is a network resource in the public cloud environment. Traditional network functions (such as firewalls, load balancers, intrusion detection systems, etc.) can be implemented in the form of software through NFV technology and run as virtual machines on standard server hardware.

[0058] In actual applications, cloud service providers can provide a graphical virtual network configuration interface, through which users can create, configure and manage virtual networks. Obtain the virtual machine IDs of the traffic entry node, traffic exit node and business node in the user's virtual network configured by the client through the virtual network configuration interface, as well as the number of traffic path nodes to be deployed. Exemplarily, the virtual network configuration information includes 2 traffic path nodes to be deployed, 1 traffic entry node, 10 business nodes, and 2 traffic exit nodes. The client's needs are characterized by selecting 2 target business nodes that assume the function of traffic path nodes from 10 business nodes, and providing a network topology including traffic entry nodes, traffic path nodes and traffic exit nodes.

[0059] S200, constructing a virtual network topology based on the virtual network configuration information, and constructing a link cost model based on the virtual network topology.

[0060] Among them, the link cost model is used to understand the structure of the virtual network and optimize network performance.

[0061] In actual applications, based on the information of virtual network devices in the virtual network configuration, the network information (IP address, etc.) of the devices in the virtual network can be determined, the configuration files of the devices in the virtual network can be obtained, the connection relationship between the devices can be determined, and the virtual network topology can be constructed through the network topology drawing tool. The indicators for evaluating link costs (such as bandwidth, delay, packet loss rate, and link stability, etc.) can be determined, the link costs can be determined through the preset cost evaluation standards, and cost annotations can be added to the virtual network topology to construct a link cost model.

[0062] S300, adding a virtual terminal node in the link cost model, and adding a virtual link between the virtual terminal node and each traffic egress node, so that the cost of each virtual link is the same.

[0063] In practical applications, in order to respond to the user's topology optimization needs (for example, the user needs to filter out the target business node that is used to assume the function of the traffic passing node from the business node, and provide a topology containing the traffic entry node, traffic exit node, and traffic passing node), in order to solve the problem of multiple traffic exits in the user's virtual network, a virtual end point can be created, and each traffic exit node can be connected to the virtual end point, and the virtual link cost is the same, so as to better balance the cost of each link when the optimized topology is determined, and reduce the impact of the newly added links on the optimized topology solution. Figure 3 As shown, the link cost model includes a traffic entry node, ten service nodes, and two traffic exit nodes. The links between the nodes are shown as dotted lines in the figure. The boxes represent the default service nodes and links. timeN is used to represent the communication cost between nodes, such as the cost, and N is used to distinguish the communication costs between different devices. A virtual terminal node is added to the link cost model, and a virtual link is added between the virtual terminal node and each traffic exit node. The link costs of the virtual links are configured to be the same. For example, the link cost of the virtual link is determined to be a preset link cost value, which can be set based on experience.

[0064] S400, iteratively executing a preset loop body to update the pheromone concentration value of the link in the link cost model until a preset number of iterations is reached, and then stopping the execution of the preset loop body.

[0065] Among them, the pheromone concentration value is used to characterize the quality of the link and is a key factor affecting the performance of the algorithm in the ant colony algorithm.

[0066] In practical applications, a solution loop body for determining the optimal path with the highest transmission efficiency including a traffic inlet node, a traffic outlet node, and a target service node (a traffic path node) can be pre-set. In this embodiment, an optimal path solution loop body is designed based on an ant colony algorithm, and the loop body is iteratively executed until a preset number of iterations (such as 200) is reached, and the execution of the preset loop body is stopped. Thus, the pheromone concentration value of the link in the link cost model is updated by the ant colony algorithm to facilitate the determination of the optimal path.

[0067] S500: updating the pheromone concentration value of the link in the link cost model again according to the preset link importance weight and the latest pheromone concentration value of the link.

[0068] Among them, the preset link importance weight is the important weight of the link between the traffic entry node, the traffic path node to be deployed, and the traffic exit node, which is used to characterize the importance of the business, such as Figure 4As shown, the important weights of the links between the traffic entry node, the traffic path node to be deployed, and the traffic exit node are shown. The preset link weight can be configured by the client and obtained through the virtual network configuration information, that is, the virtual network configuration information sent by the client also includes the preset link weight, timeN is used to represent the communication cost between nodes, and N is used to distinguish the communication cost between different devices.

[0069] In practical applications, a preset link importance weight is obtained from the virtual network configuration information, and for each link, the pheromone concentration value of the link is multiplied by the corresponding link weight to obtain an updated pheromone concentration value of the link.

[0070] S600: Find the optimal target virtual network topology from the updated link cost model.

[0071] Among them, the optimal target virtual network topology may refer to the topology corresponding to the path with the highest transmission efficiency from the traffic inlet node to the traffic outlet node.

[0072] In practical applications, after updating the link cost model, the optimal target virtual network topology can be found from the link cost model according to a search algorithm. For example, a path with the smallest sum of link costs from a traffic inlet node to a traffic outlet node can be found according to a Dijkstra algorithm or an A* search algorithm, and the target virtual network topology can be drawn according to the path.

[0073] S700, when the loop body is executed, the following steps are implemented: screening out multiple nodes from multiple business nodes, constructing a node set, determining the access probability of the traffic entry node and each node in the node set, determining the node with the highest access probability as the target node to be visited, visiting the target node, removing the visited target node from the node set, updating the node set, determining the access probability of the target node and each node in the updated node set, returning to the step of determining the node with the highest access probability as the target node to be visited, until the number of visited nodes reaches a preset number threshold, and updating the pheromone concentration value of the link in the link cost model.

[0074] In this embodiment, the mechanism of ant access to nodes in the ant colony algorithm is improved. Among them, the ant colony algorithm is a heuristic optimization algorithm that simulates the foraging behavior of ants in nature. The access path of ants to nodes represents a feasible solution to the target virtual network topology recommendation problem. Through the positive feedback of the ant colony, the pheromone concentration value of the link in the link cost model is updated so that the pheromone concentration value of the link in the better path is higher. In practical applications, the algorithm initialization is performed: the number of ants is initialized, the pheromone concentration value of the link in the link cost model, the pheromone volatility coefficient of the ant colony algorithm, and the heuristic function is defined based on the link cost. It is beneficial to improve the diversity of ant exploration, reduce the possibility of falling into a local optimal solution, and improve the convergence speed of the ant colony algorithm. When the loop body is executed, the following processing is performed:

[0075] A plurality of nodes are randomly selected from the service nodes to construct a node set to represent the set of nodes to be visited by the ant colony in this cycle. The access probability between the traffic entry node and each node in the node set is determined. Specifically, for each ant, the access probability between the traffic entry node and any node in the node set is determined. The access probability can be calculated by applying the state transition equation of the ant colony algorithm based on the link cost between the traffic entry node and the node. The node with the highest access probability is determined as the target node to be visited, the target node is visited, the visited target node is removed from the node set, and the node set is updated. The access probability between the target node and each node in the updated node set is determined. Specifically, for each ant, the access probability between the target node and any node in the node set is determined. The access probability can be calculated by applying the state transition equation of the ant colony algorithm based on the link cost between the target node and the node. Return to the step of determining the node with the highest access probability as the target node to be visited, until the number of visited nodes reaches a preset number threshold. The preset number threshold is determined according to the number of traffic passing nodes to be deployed. Specifically, the preset number threshold can be the sum of the number of traffic entry nodes (1 node), the traffic passing nodes to be deployed, and the traffic exit nodes (counted as 1 node). For each ant, if the number of nodes visited by the ant meets the preset number threshold, it indicates that the ant has reached the traffic exit node, and the next node to be visited is the virtual terminal node. The ant stops searching.

[0076] After the number of nodes visited by all ants in the ant colony meets the preset threshold, the single ant path exploration ends. According to the access paths of all ants, the pheromone concentration value of the link in the link cost model is updated according to the pheromone volatility coefficient of the ant colony algorithm, so that the pheromone concentration value of the link visited by multiple ants is higher.

[0077] In the above virtual network topology recommendation method, the virtual network configuration information sent by the client is obtained, a link cost model is constructed based on the virtual network configuration information, a virtual terminal node is added to the link cost model, and a virtual link is added between the virtual terminal node and each traffic exit node, and the virtual link cost is the same. On the one hand, in view of the problem that there are multiple traffic exits in the virtual network of the cloud user, in the problem modeling stage, a virtual terminal node is fictionalized, and each traffic exit is connected to the virtual terminal node with the same link cost, which is conducive to balancing the link cost in the subsequent process of determining the optimal target virtual network topology based on the link cost model, and neutralizing the influence of the newly added false link on the determination of the target virtual network topology, which is conducive to improving the accuracy of determining the target virtual network topology. On the other hand, an improved algorithm loop is designed, by screening out the nodes to be visited from multiple business nodes, constructing a set of nodes to be visited, changing the node access mechanism, determining the access node by determining the probability of the current access node accessing the nodes to be visited in the set of nodes to be visited, and updating the pheromone concentration value of the link in the link cost model, so as to facilitate the diversity of the target network topology exploration, reduce the possibility of falling into the local optimal solution, and improve the convergence speed of the algorithm. Thirdly, by iteratively executing the loop body, the pheromone concentration value of the link in the link cost model is updated, and according to the preset link importance weight and the updated link pheromone concentration value, the pheromone concentration value of the link in the link cost model is updated again, and the optimal target virtual network topology is found from the updated link cost model. In this way, determining the target virtual network topology by the pheromone concentration of the link is conducive to improving the transmission efficiency of the virtual network. At the same time, combined with the preset link importance requirements, the difficulty of solving the problem in the case of extremely complex and large client networks is fully considered, so that the preset link importance fully intervenes in the topology optimization process, making the results more customer-oriented, which is conducive to improving the matching degree between the target network topology and client requirements.

[0078] In an exemplary embodiment, S200 includes S220 to S260. Among them:

[0079] S220, determine the delay and bandwidth between the traffic ingress node, the traffic egress node, and the service node.

[0080] In actual applications, the latency and bandwidth between traffic ingress nodes, traffic egress nodes, and business nodes can be queried in the management console of the cloud service based on the network configuration information.

[0081] S240, based on the delay and bandwidth, determine the link cost between the traffic ingress node, the traffic egress node, and the service node.

[0082] In practical applications, weights may be allocated in advance for delay and bandwidth, and the link costs between the traffic ingress node, the traffic egress node, and the service node may be determined by taking the weighted sum of the delay and bandwidth.

[0083] In other embodiments, the link cost of the false link may be determined by determining the average of the link costs between the traffic ingress node, the traffic egress node, and the service node as the link cost of the false link.

[0084] S260: construct a link cost model according to the virtual network topology and the link cost.

[0085] In practical applications, a topology model may be constructed based on a tool, a network model may be constructed based on a virtual network topology, link cost labels may be added to links in the network model, and a link cost model may be constructed.

[0086] In this embodiment, by determining the delay and bandwidth between the traffic inlet node, the traffic outlet node, and the service node, calculating the link cost between these nodes, and constructing a link cost model, it is beneficial to optimize the topology according to the link cost to reduce the possibility of network congestion, reduce data transmission delay, and improve data transmission efficiency.

[0087] In order to improve the accuracy of topology determination, in an exemplary embodiment, as Figure 5 As shown, S240 also includes S242 to S246. Among them:

[0088] S242, based on the virtual network configuration information, collect the virtual machine information, network connection information and communication path of the traffic ingress node, traffic egress node and business node.

[0089] The virtual machine information may include the virtual machine's ID, IP address, MAC address, and host machine ID, etc. The network connection information may include the virtual machine's network port.

[0090] In actual applications, the virtual machine information, network connection information and communication path of the traffic entry node, traffic exit node and business node can be collected according to the virtual configuration of the virtual machine, such as security groups, network access control lists, etc.

[0091] S244, based on the virtual machine information, network connection information and communication path of the traffic inlet node, the traffic outlet node and the business node respectively, simulate the communication between the traffic inlet node, the traffic outlet node and the business node, and collect the communication data during the communication process.

[0092] The communication data may include the traffic data received by the virtual machine, the traffic data sent by the virtual machine, the size of the data packet, the frequency of sending the data packet, etc.

[0093] In actual applications, the business communication between the traffic inlet node, traffic outlet node, and business node can be simulated through virtual switching software based on the virtual machine information, network connection information, and communication path of the traffic inlet node, traffic outlet node, and business node. For example, the packet In and packet Out functions of OpenVSwitch (open virtual switching standard) can be used to simulate the communication between devices on the business network plane and collect communication data during the communication process.

[0094] S246, based on the communication data, determine the delay and bandwidth between the traffic ingress node, the traffic egress node, and the service node.

[0095] In practical applications, the delay and bandwidth between the traffic inlet node, traffic outlet node, and business node are calculated based on the time difference between the sending and receiving of data packets in the communication data and the amount of data transmitted per unit time.

[0096] In this embodiment, based on the virtual network configuration information, the communication between the traffic ingress node, the traffic egress node and the service node is simulated, the communication data is collected, and the delay and bandwidth between the traffic ingress node, the traffic egress node and the service node are determined based on the communication data, which is conducive to topology optimization based on the delay and bandwidth data, thereby facilitating improving the accuracy of determining the optimal topology.

[0097] In an exemplary embodiment, Figure 6 As shown, S600 includes S620. Among them:

[0098] S620, based on the pheromone concentration value and link cost of the link in the updated link cost model, through breadth-first search, find the target virtual network topology with the minimum sum of link costs of the path from the traffic inlet node to the traffic outlet node from the updated link cost model.

[0099] In practical applications, according to the pheromone concentration value and link cost of the link in the updated link cost model, the optimal next node is searched starting from the traffic entry node according to the breadth-first search algorithm. When selecting the next node, the probability of selecting each unvisited adjacent node is determined according to the pheromone concentration value and link cost of the link between the current node and the unvisited adjacent node, and the adjacent node with the highest probability is selected as the next node. The sum of the link costs from the starting point to the node is calculated and recorded, and the priority queue is updated according to the sum of the link costs. Iterate to the virtual end node and output the path with the smallest sum of link costs from the traffic entry node to the traffic exit node. Figure 7As shown in the figure, the sum of the link costs of the path from the traffic entry node -> service node 4 -> service node 5 to the traffic exit node is the smallest, and the traffic passing nodes are determined to be service node 4 and service node 5. timeN is used to represent the communication cost between nodes, and N is used to distinguish the communication cost between different devices. Based on this path, the target virtual network topology is determined, such as Figure 8 As shown, timeN is used to represent the communication cost between nodes, and N is used to distinguish the communication cost between different devices.

[0100] In this embodiment, a breadth-first search is performed on the link cost model based on the pheromone concentration value and link cost of the link, which increases the exploratory nature of node selection in the breadth-first search, improves the search efficiency, enhances the robustness and flexibility of the algorithm, and improves the adaptability to the optimization of complex user network models. This is conducive to improving the accuracy of determining the target virtual network model.

[0101] In order to accelerate the convergence of the path solution, in an exemplary embodiment, as Fig. 9 As shown, S400 includes S420 to S460. Among them:

[0102] S420, updating the pheromone concentration value of the link in the link cost model according to the pheromone concentration value of the link in the link cost model and a preset pheromone volatility coefficient.

[0103] S440, determining the increment of the pheromone concentration value on the visited link in the link cost model.

[0104] S460: Update the pheromone concentration value of the visited link according to the pheromone concentration value increment.

[0105] After a single ant colony exploration is completed, the pheromone concentration value of each link in the link cost model is decayed according to the preset pheromone volatility coefficient, and the pheromone concentration value of the link in the link cost model is updated to simulate the natural volatilization process of pheromone over time.

[0106] According to the positive feedback mechanism of the ant colony algorithm, the pheromone concentration value on the link visited by more ants in the link cost model is increased. For each ant, the sum of the link costs in the ant's node access path is determined based on the ant's node access path. According to the sum of the link costs in the ant's node access path, the pheromone concentration increment value on the link that the ant has visited is determined.

[0107] After the pheromone concentration increment value of the visited link is determined, the pheromone concentration value of the visited link is updated according to the pheromone concentration increment value.

[0108] After updating the pheromone concentration value of the visited link according to the pheromone concentration increment value, find the current optimal path with the minimum sum of link costs of the path from the current traffic entry node to the traffic exit node, determine the pheromone concentration increment value of the current optimal path (such as 1 / sum of link costs) according to the sum of link costs of the current optimal path, and update the pheromone concentration value of the link on the current optimal path according to the pheromone concentration increment value to strengthen the pheromone concentration of the current optimal solution.

[0109] In this embodiment, for the visited links in the link cost model, the pheromone concentration value increment is determined, and the visited link value is updated to strengthen the pheromone concentration value on the visited link. For the unvisited links, the pheromone concentration value of the unvisited links is updated according to the pheromone volatility coefficient to reduce the pheromone concentration value of the unvisited links, which is beneficial to improve the speed of determining the optimal path.

[0110] In order to improve the accuracy of processing communication data, in an exemplary embodiment, before S246, S245 is also included.

[0111] S245, performing data preprocessing on the communication data, where the data preprocessing includes at least one of dirty data elimination and missing data processing.

[0112] Among them, dirty data can be inaccurate, incomplete or duplicate data in communication data.

[0113] In practical applications, network vibrations (such as packet loss, delay fluctuations, etc.) may affect the quality of communication data during simulation and communication data collection. In order to improve the reliability and accuracy of communication data, use hash tables or sets to store information about processed data packets to avoid duplicate records, and use the ‌Z-score method to detect outliers in communication data to remove outliers. Fill in missing data through interpolation, such as weighted completion based on the values ​​of the sampling points before and after the missing data.

[0114] In this embodiment, data cleaning preprocessing by performing dirty data removal and missing data processing on the communication data is helpful to reduce the impact of network vibration on the collected communication data. By completing the missing data processing to fill in the missing data, the vibration of the original network is reflected in the overall stability of the link, which is helpful to improve the accuracy of determining the delay and bandwidth between nodes.

[0115] In order to make a clearer description of the virtual network topology recommendation method provided by the present application, a specific embodiment is described below, and the specific embodiment includes the following steps:

[0116] S1, obtain the virtual network configuration information sent by the client, the virtual network configuration information includes the number of traffic path nodes to be deployed, as well as a traffic entry node, multiple traffic exit nodes and business nodes.

[0117] S2, based on the virtual network configuration information, collects the virtual machine information, network connection information and communication path of the traffic inlet node, the traffic outlet node and the business node, simulates the communication between the traffic inlet node, the traffic outlet node and the business node based on the virtual machine information, network connection information and communication path of the traffic inlet node, the traffic outlet node and the business node, collects the communication data during the communication process, and performs data preprocessing on the communication data, the data preprocessing includes at least one of dirty data removal and missing data processing.

[0118] S3, based on the communication data, determines the delay and bandwidth between the traffic entry node, the traffic exit node, and the service node. Based on the delay and bandwidth, determines the link cost between the traffic entry node, the traffic exit node, and the service node. According to the virtual network topology and link cost, a link cost model is constructed.

[0119] S4, adding a virtual end node to the link cost model, and adding a virtual link between the virtual end node and each traffic exit node, so that the cost of each virtual link is the same.

[0120] S5, iteratively executing the preset loop body to update the pheromone concentration value of the link in the link cost model until the preset number of iterations is reached, and then stopping the execution of the preset loop body.

[0121] Among them, when the loop body is executed, the following steps are implemented: screening out multiple nodes from multiple business nodes, building a node set, determining the access probability of the traffic entry node and each node in the node set, determining the node with the highest access probability as the target node to be visited, visiting the target node, removing the visited target node from the node set, updating the node set, determining the access probability of the target node and each node in the updated node set, returning to the step of determining the node with the highest access probability as the target node to be visited, until the number of visited nodes reaches a preset number threshold, and updating the pheromone concentration value of the link in the link cost model.

[0122] S6, updating the pheromone concentration value of the link in the link cost model again according to the preset link importance weight and the latest link pheromone concentration value.

[0123] S7, based on the pheromone concentration value and link cost of the link in the updated link cost model, through breadth-first search, find the target virtual network topology with the minimum sum of link costs of the path from the traffic entry node to the traffic exit node from the updated link cost model.

[0124] It should be understood that, although the various steps in the flowcharts involved in the above-mentioned embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-mentioned embodiments can include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0125] In an exemplary embodiment, Fig.10 As shown, a virtual network topology device 600 is provided, comprising: a configuration information acquisition module 610, a model building module 620, a pheromone concentration value adjustment module 630 and a topology determination module 640, wherein:

[0126] The configuration information acquisition module 610 is used to acquire the virtual network configuration information sent by the client, where the virtual network configuration information includes the number of traffic path nodes to be deployed, as well as a traffic ingress node, multiple traffic egress nodes, and service nodes;

[0127] The model building module 620 is used to build a virtual network topology based on the virtual network configuration information, and build a link cost model based on the virtual network topology; add a virtual end node to the link cost model, and add a virtual link between the virtual end node and each traffic exit node so that the cost of each virtual link is the same;

[0128] The pheromone concentration value adjustment module 630 is used to iteratively execute a preset loop body to update the pheromone concentration value of the link in the link cost model until a preset number of iterations is reached, and the execution of the preset loop body is stopped; according to the preset link importance weight and the latest pheromone concentration value of the link, the pheromone concentration value of the link in the link cost model is updated again; when the loop body is executed, the following operations are implemented: multiple nodes are screened out from multiple business nodes, a node set is constructed, the access probability of the traffic entry node and each node in the node set is determined, the node with the highest access probability is determined as the target node to be visited, the target node is visited, the visited target node is removed from the node set, the node set is updated, the access probability of the target node and each node in the updated node set is determined, and the step of determining the node with the highest access probability as the target node to be visited is returned until the number of visited nodes reaches a preset number threshold, and the pheromone concentration value of the link in the link cost model is updated;

[0129] The topology determination module 640 is used to find out the optimal target virtual network topology from the updated link cost model.

[0130] In an exemplary embodiment, the model building module 620 is also used to determine the latency and bandwidth between the traffic ingress node, the traffic egress node, and the service node; based on the latency and bandwidth, determine the link cost between the traffic ingress node, the traffic egress node, and the service node; and build a link cost model based on the virtual network topology and link cost.

[0131] In an exemplary embodiment, the model building module 620 is also used to collect the virtual machine information, network connection information and communication path of the traffic inlet node, the traffic outlet node and the business node based on the virtual network configuration information; simulate the communication between the traffic inlet node, the traffic outlet node and the business node based on the virtual machine information, network connection information and communication path of the traffic inlet node, the traffic outlet node and the business node, and collect communication data during the communication process; determine the delay and bandwidth between the traffic inlet node, the traffic outlet node and the business node based on the communication data.

[0132] In an exemplary embodiment, the pheromone concentration value adjustment module 630 is also used to find the target virtual network topology with the minimum sum of link costs of the path from the traffic entry node to the traffic exit node from the updated link cost model through breadth-first search based on the pheromone concentration value and link cost of the link in the updated link cost model.

[0133] In an exemplary embodiment, the virtual network topology recommendation device 600 further includes a data preprocessing module 650 for performing data preprocessing on the communication data, where the data preprocessing includes at least one of dirty data removal and missing data processing.

[0134] Each module in the virtual network topology device 600 can be implemented in whole or in part by software, hardware, or a combination thereof. Each module can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in a computer device in the form of software, so that the processor can call and execute operations corresponding to each module.

[0135] In an exemplary embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as shown in FIG. Fig.11As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. The processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store data. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a virtual network topology recommendation method is implemented.

[0136] Those skilled in the art will understand that Fig.11 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0137] In an exemplary embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and when the processor executes the computer program, the steps in any one of the above-mentioned virtual network topology recommendation method embodiments are implemented.

[0138] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps in any one of the above-mentioned virtual network topology recommendation method embodiments are implemented.

[0139] In one embodiment, a computer program product is provided, including a computer program, which, when executed by a processor, implements the steps in any one of the above-mentioned virtual network topology recommendation method embodiments.

[0140] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0141] A person of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment method can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, an artificial intelligence (AI) processor, etc., but are not limited to this.

[0142] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0143] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A virtual network topology recommendation method, characterized in that: The method comprises: Obtaining virtual network configuration information sent by the client, the virtual network configuration information including the number of traffic path nodes to be deployed, as well as a traffic entry node, multiple traffic exit nodes, and a service node; Based on the virtual network configuration information, construct a virtual network topology, and construct a link cost model based on the virtual network topology; Adding a virtual terminal node to the link cost model, and adding a virtual link between the virtual terminal node and each of the traffic egress nodes, so that the cost of each virtual link is the same; Iteratively executing a preset loop body to update the pheromone concentration value of the link in the link cost model until a preset number of iterations is reached, and then stopping the execution of the preset loop body; According to the preset link importance weight and the latest pheromone concentration value of the link, the pheromone concentration value of the link in the link cost model is updated again; Finding the optimal target virtual network topology from the updated link cost model; The following steps are implemented when the loop body is executed: A plurality of nodes are selected from the plurality of service nodes, a node set is constructed, the access probability of the traffic entry node and each node in the node set is determined, the node with the highest access probability is determined as the target node to be visited, the target node is visited, the visited target node is removed from the node set, the node set is updated, the access probability of the target node and each node in the updated node set is determined, the step of determining the node with the highest access probability as the target node to be visited is returned to, until the number of visited nodes reaches a preset number threshold, and the pheromone concentration value of the link in the link cost model is updated.

2. The method according to claim 1, characterized in that The constructing of a link cost model based on the virtual network topology includes: Determine the delay and bandwidth between the traffic ingress node, the traffic egress node, and the service node; Determine, based on the delay and the bandwidth, a link cost between the traffic ingress node, the traffic egress node, and the service node; A link cost model is constructed according to the virtual network topology and the link cost.

3. The method according to claim 2, characterized in that The determining of the delay and bandwidth between the traffic ingress node, the traffic egress node, and the service node includes: Based on the virtual network configuration information, collect the virtual machine information, network connection information and communication path of each of the traffic ingress node, the traffic egress node and the service node; Based on the virtual machine information, network connection information and communication path of each of the traffic inlet node, the traffic outlet node and the service node, simulate the communication between the traffic inlet node, the traffic outlet node and the service node, and collect communication data during the communication process; Based on the communication data, the delay and bandwidth between the traffic ingress node, the traffic egress node, and the service node are determined.

4. The method according to any one of claims 1 to 3, characterized in that: The step of finding the optimal target virtual network topology from the updated link cost model includes: Based on the pheromone concentration value and link cost of the link in the updated link cost model, through breadth-first search, the target virtual network topology with the smallest sum of link costs of the path from the traffic entry node to the traffic exit node is found from the updated link cost model.

5. The method according to claim 3, characterized in that: The updating of the pheromone concentration value of the link in the link cost model comprises: According to the pheromone concentration value of the link in the link cost model and the preset pheromone volatility coefficient, updating the pheromone concentration value of the link in the link cost model; Determining an increment of a pheromone concentration value on a visited link in the link cost model; The pheromone concentration value of the visited link is updated according to the pheromone concentration value increment.

6. The method according to claim 5, characterized in that Before determining the delay and bandwidth between the traffic inlet node, the traffic outlet node, and the service node based on the communication data, the method further includes: The communication data is preprocessed, wherein the data preprocessing includes at least one of dirty data elimination and missing data processing.

7. A virtual network topology recommendation device, characterized in that: The device comprises: A configuration information acquisition module, used to acquire virtual network configuration information sent by the client, wherein the virtual network configuration information includes the number of traffic path nodes to be deployed, as well as a traffic entry node, multiple traffic exit nodes and service nodes; A model building module, used to build a virtual network topology based on the virtual network configuration information, and build a link cost model based on the virtual network topology; add a virtual terminal node to the link cost model, and add a virtual link between the virtual terminal node and each of the traffic egress nodes so that the cost of each virtual link is the same; The pheromone concentration value adjustment module is used to iteratively execute a preset loop body to update the pheromone concentration value of the link in the link cost model until a preset number of iterations is reached, and the execution of the preset loop body is stopped; according to the preset link importance weight and the latest pheromone concentration value of the link, the pheromone concentration value of the link in the link cost model is updated again; when the loop body is executed, the following operations are implemented: multiple nodes are screened out from multiple business nodes, a node set is constructed, the access probability of the traffic entry node and each node in the node set is determined, the node with the highest access probability is determined as the target node to be visited, the target node is visited, the visited target node is removed from the node set, the node set is updated, the access probability of the target node and each node in the updated node set is determined, and the step of determining the node with the highest access probability as the target node to be visited is returned until the number of visited nodes reaches a preset number threshold, and the pheromone concentration value of the link in the link cost model is updated; The topology determination module is used to find the optimal target virtual network topology from the updated link cost model.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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