阅读说明：本技术 一种基于生成对抗网络的虚拟网络映射方法及装置 (Virtual network mapping method and device based on generation of countermeasure network ) 是由 吴立军 李志圆 于 2021-08-30 设计创作，主要内容包括：本申请公开了一种基于生成对抗网络的虚拟网络映射方法及装置。所述基于生成对抗网络的虚拟网络映射方法包括：获取动态拓扑网络；通过第一双组件图卷积网络获取动态拓扑网络中的动态拓扑网络特征；获取虚拟网络的拓扑请求信息；通过第二双组件图卷积网络获取拓扑请求信息中的虚拟网络请求特征；获取经过训练的对抗模型；将所述动态拓扑网络特征以及所述虚拟网络请求特征融合从而获取融合特征；将所述融合特征输入至所述经过训练的对抗模型中从而获取处理信息。本申请的基于生成对抗网络的虚拟网络映射方法可以实现对实时在线到达的虚拟网络请求进行处理的接受率达到近100％,并且每个请求的处理时间在100ms以内。(The application discloses a virtual network mapping method and device based on generation of a countermeasure network. The virtual network mapping method based on the generation countermeasure network comprises the following steps: acquiring a dynamic topological network; acquiring dynamic topological network characteristics in the dynamic topological network through the first dual-component graph convolutional network; acquiring topology request information of a virtual network; acquiring virtual network request characteristics in topology request information through a second dual-component graph convolutional network; obtaining a trained confrontation model; fusing the dynamic topological network characteristic and the virtual network request characteristic to obtain a fused characteristic; inputting the fusion features into the trained confrontation model to obtain processing information. The virtual network mapping method based on the generation countermeasure network can achieve that the acceptance rate of processing the real-time online arriving virtual network requests reaches nearly 100%, and the processing time of each request is within 100 ms.)

1. A virtual network mapping method based on a generation countermeasure network is characterized by comprising the following steps:

acquiring a dynamic topological network;

acquiring dynamic topological network characteristics in the dynamic topological network through the first dual-component graph convolutional network;

acquiring topology request information of a virtual network;

acquiring virtual network request characteristics in topology request information through a second dual-component graph convolutional network;

obtaining a trained confrontation model;

fusing the dynamic topological network characteristic and the virtual network request characteristic to obtain a fused characteristic;

inputting the fusion features into the trained confrontation model to obtain processing information.

2. The method for mapping a virtual network based on a generative countermeasure network as set forth in claim 1, wherein the method for mapping a virtual network based on a generative countermeasure network comprises:

the dynamic topology network features include network node features and link dependency features.

3. The method for generating a virtual network map for a counterpoise network according to claim 2, wherein said method for generating a virtual network map for a counterpoise network further comprises:

training the confrontation model.

4. The method of generating a virtual network map of a countermeasure network as set forth in claim 3, wherein the training of the countermeasure model includes:

acquiring a real topology set;

pre-training the countermeasure model using a set of real sub-topologies;

acquiring a training set;

the pre-trained confrontation model is trained using a training set, wherein the training employs a reward mechanism for reinforcement learning.

5. The method for mapping a virtual network based on a generative countermeasure network as set forth in claim 1, wherein the method for mapping a virtual network based on a generative countermeasure network further comprises:

acquiring a discrimination value corresponding to the processing information;

and adjusting the parameters of the confrontation model according to the discrimination value.

6. A virtual network mapping apparatus based on a generation countermeasure network, the apparatus comprising:

the dynamic topological network acquisition module is used for acquiring a dynamic topological network;

the dynamic topological network characteristic acquisition module is used for acquiring dynamic topological network characteristics in a dynamic topological network through a first dual-component graph convolutional network;

the topology request acquisition module is used for acquiring topology request information of the virtual network;

a virtual network request feature obtaining module, configured to obtain a virtual network request feature in the topology request information through a second dual-component graph convolutional network;

a confrontation model obtaining module for obtaining a trained confrontation model;

a fusion module for fusing the dynamic topological network feature and the virtual network request feature to obtain a fused feature;

a calculation module for inputting the fusion features into the trained confrontation model to obtain processing information.

7. The virtual network mapping apparatus based on generation of a countermeasure network of claim 6, wherein the dynamic topology network characteristics include network node characteristics and link dependency characteristics.

8. An electronic device, comprising a memory, a processor and a computer program stored in the memory and capable of running on the processor, wherein the processor, when executing the computer program, is capable of implementing the virtual network mapping method based on generation of a countermeasure network according to any one of claims 1 to 5.

9. A computer-readable storage medium, characterized in that the computer-readable storage medium stores a computer program, which when executed by a processor is capable of implementing the method for virtual network mapping based on generation of a countermeasure network according to any one of claims 1 to 5.

Technical Field

The application relates to the technical field of network virtualization, in particular to a virtual network mapping method based on a generation countermeasure network and a virtual network mapping device based on the generation countermeasure network.

Background

Network virtualization is one of key technologies for future network development, can effectively overcome the problem of 'rigor' existing in the current network, and realizes that a plurality of virtual networks coexist on one physical network simultaneously. Virtual network mapping is a research focus and a difficult point in the field of network virtualization, and limited physical resources can be reasonably and effectively distributed to different virtual networks through continuous optimization of an algorithm, so that the optimal utilization of the resources is realized.

However, most of the current virtual network mapping algorithms adopt a heuristic scheme to recursively map each node and link, which greatly limits the efficiency of the mapping algorithm.

Accordingly, a solution is desired to solve or at least mitigate the above-mentioned deficiencies of the prior art.

Disclosure of Invention

The present invention is directed to a virtual network mapping method based on generation of a countermeasure network to solve at least one of the problems described above.

In one aspect of the present invention, a method for mapping a virtual network based on a generation countermeasure network is provided, and the method for mapping a virtual network based on a generation countermeasure network includes:

acquiring a dynamic topological network;

acquiring dynamic topological network characteristics in the dynamic topological network through the first dual-component graph convolutional network;

acquiring topology request information of a virtual network;

acquiring virtual network request characteristics in topology request information through a second dual-component graph convolutional network;

obtaining a trained confrontation model;

fusing the dynamic topological network characteristic and the virtual network request characteristic to obtain a fused characteristic;

inputting the fusion features into the trained confrontation model to obtain processing information.

Optionally, the virtual network mapping method based on the generation of the countermeasure network includes:

the dynamic topology network features include network node features and link dependency features.

Optionally, the method for generating a virtual network map of the countermeasure network further includes:

training the confrontation model.

Optionally, the training the confrontation model comprises:

acquiring a real topology set;

pre-training the countermeasure model using a set of real sub-topologies;

acquiring a training set;

the pre-trained confrontation model is trained using a training set, wherein the training is trained using a reward mechanism.

Optionally, the method for generating a virtual network map of the countermeasure network further includes:

acquiring a discrimination value corresponding to the processing information;

and adjusting the parameters of the confrontation model according to the discrimination value.

The application also provides a virtual network mapping device based on the generation of the countermeasure network, which comprises:

the dynamic topological network acquisition module is used for acquiring a dynamic topological network;

the dynamic topological network characteristic acquisition module is used for acquiring dynamic topological network characteristics in a dynamic topological network through a first dual-component graph convolutional network;

the topology request acquisition module is used for acquiring topology request information of the virtual network;

a virtual network request feature obtaining module, configured to obtain a virtual network request feature in the topology request information through a second dual-component graph convolutional network;

a confrontation model obtaining module for obtaining a trained confrontation model;

a fusion module for fusing the dynamic topological network feature and the virtual network request feature to obtain a fused feature;

a calculation module for inputting the fusion features into the trained confrontation model to obtain processing information.

Optionally, the dynamic topology network features include network node features and link dependency features.

The present application further provides an electronic device, which includes a memory, a processor, and a computer program stored in the memory and capable of running on the processor, and the processor, when executing the computer program, can implement the virtual network mapping method based on generation of the countermeasure network as described above.

The present application also provides a computer-readable storage medium storing a computer program which, when executed by a processor, is capable of implementing the virtual network mapping method based on generation of a countermeasure network as described above

Advantageous effects

The virtual network mapping method based on the generation countermeasure network can achieve that the acceptance rate of processing the real-time online arriving virtual network requests reaches nearly 100%, and the processing time of each request is within 100 ms.

Drawings

Fig. 1 is a schematic flowchart of a virtual network mapping method based on generation of a countermeasure network according to an embodiment of the present application.

Fig. 2 is an exemplary block diagram of an electronic device capable of implementing a virtual network mapping method based on generation of a countermeasure network provided according to an embodiment of the present application.

Detailed Description

In order to make the implementation objects, technical solutions and advantages of the present application clearer, the technical solutions in the embodiments of the present application will be described in more detail below with reference to the drawings in the embodiments of the present application. In the drawings, the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The described embodiments are a subset of the embodiments in the present application and not all embodiments in the present application. The embodiments described below with reference to the drawings are exemplary and intended to be used for explaining the present application and should not be construed as limiting the present application. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application. Embodiments of the present application will be described in detail below with reference to the accompanying drawings.

Fig. 1 is a schematic flowchart of a virtual network mapping method based on generation of a countermeasure network according to an embodiment of the present application.

The virtual network mapping method based on generation of the countermeasure network shown in fig. 1 includes:

step 1: acquiring a dynamic topological network;

step 2: acquiring dynamic topological network characteristics in the dynamic topological network through the first dual-component graph convolutional network;

and step 3: acquiring topology request information of a virtual network;

and 4, step 4: acquiring virtual network request characteristics in topology request information through a second dual-component graph convolutional network;

and 5: obtaining a trained confrontation model;

step 6: fusing the dynamic topological network characteristic and the virtual network request characteristic to obtain a fused characteristic;

and 7: inputting the fusion features into the trained confrontation model to obtain processing information.

The virtual network mapping method based on the generation countermeasure network can achieve that the acceptance rate of processing the real-time online arriving virtual network requests reaches nearly 100%, and the processing time of each request is within 100 ms.

In this embodiment, the virtual network mapping method based on the generation of the countermeasure network includes:

the dynamic topology network features include network node features and link dependency features.

In this embodiment, the method for mapping a virtual network based on a generation countermeasure network further includes:

an confrontation model is trained.

In this embodiment, training the confrontation model comprises:

acquiring a real topology set;

pre-training the countermeasure model using a set of real sub-topologies;

acquiring a training set;

the pre-trained confrontation model is trained using a training set, wherein the training is trained using a reward mechanism.

In this embodiment, the method for mapping a virtual network based on a generation countermeasure network further includes:

acquiring a discrimination value corresponding to the processing information;

and adjusting the parameters of the confrontation model according to the discrimination value.

The virtual network mapping method based on generation of the countermeasure network of the present application is further described below by way of example, and it should be understood that the example does not constitute any limitation to the present application.

In this embodiment, the Dell Precision T7920 tower workstation is selected as the hardware platform, and is programmed by using Python language.

In this embodiment, the virtual network mapping method based on generation of the countermeasure network further includes the following pre-steps: initializing a related interface, and starting a processing flow of a request after receiving a virtual network request arriving in real time.

Acquiring topology request information of a virtual network;

acquiring the virtual network request feature in the topology request information through the second two-component graph convolutional network, specifically, the virtual network request feature acquisition module VN _ feature () performs feature extraction on the request topology by using the two components (nodes and links) thereof,

meanwhile, acquiring a dynamic topological network;

acquiring dynamic topological network characteristics in the dynamic topological network through the first dual-component graph convolutional network; specifically, a double-component graph convolutional neural network is used for extracting features of a dynamic network topology;

fusing the dynamic topological network characteristics and the virtual network request characteristics to obtain fused characteristics;

obtaining a trained confrontation model;

the fusion features are taken as input to a function GAN () of the countermeasure model,

process information is obtained for the request in combination with consideration of the network state.

The discriminator returns a discrimination value (excellent degree of operation) for the operation,

and the model adjusts the neural network parameters according to the returned values.

In this embodiment, the method first performs feature extraction of a dynamic topology, introduces a two-component graph convolution to explicitly model the correlation between nodes and links, and performs automatic feature extraction on a network topology of a non-euclidean domain.

Meanwhile, the topology request information of the virtual network is subjected to feature extraction by using another group of double-component graph convolution, and features of the virtual network request are fully mined.

And then, acquiring a trained countermeasure model, specifically, the countermeasure model consists of a generator and a discriminator, taking the network topology and the virtual network request characteristics as input, taking a real network sub-topology as a sample, and inputting the sample to generate the sub-topology. In particular, the generator takes samples from the prior distribution (i.e. the real network sub-topology) and generates a topology G with node and link information representing the topology. And the node and the edge of the G respectively represent the position of the physical node to be embedded by each virtual node in the virtual network request and the virtual link information carried by the physical link. The discriminator extracts two samples from the data set and the generator and learns how to distinguish them. The generators and discriminators are trained using the modified WGAN, learning the generators to match the experience distribution, and finally outputting the effective topology.

Because the generated confrontation model is not easy to converge and the training is unstable, a staged training mode is adopted. First, the generative confrontation model is pre-trained using a set of real sub-topologies so that the model can generate sub-topologies that conform to the rules, rather than blindly generating unreal topologies. Then, in the formal training phase, since the model can generate a real topology, but the generated result is not necessarily the result that is most used with the current virtual network request, we adopt a reward mechanism, that is, for one round of generating the result, a decision value (the goodness of the result) on the result is obtained, and training is performed based on the decision value (the generation result with a high decision value increases the probability that the action is generated again later, and vice versa).

This application has following advantage:

1. in the virtual network mapping problem, the mapping is solved in a non-recursive manner for the first time, and is completed in a single step manner.

2. In the virtual network mapping problem, a dual-component graph convolutional neural network is used for the first time, the node and link states are simultaneously subjected to feature extraction, and topology changes are effectively sensed.

3. The model adopts end-to-end training, and is convenient to deploy and debug.

The application also provides a virtual network mapping device based on the generated countermeasure network, which comprises a dynamic topological network acquisition module, a dynamic topological network characteristic acquisition module, a topological request acquisition module, a virtual network request characteristic acquisition module, a countermeasure model acquisition module, a fusion module and a calculation module; in the present embodiment, it is preferred that,

the dynamic topological network acquisition module is used for acquiring a dynamic topological network;

the dynamic topological network characteristic acquisition module is used for acquiring dynamic topological network characteristics in the dynamic topological network through the first dual-component graph convolutional network;

the topology request acquisition module is used for acquiring topology request information of the virtual network;

the virtual network request characteristic acquisition module is used for acquiring virtual network request characteristics in the topology request information through a second double-component graph convolutional network;

the confrontation model acquisition module is used for acquiring a trained confrontation model;

the fusion module is used for fusing the dynamic topological network characteristic and the virtual network request characteristic so as to obtain a fusion characteristic;

and the computing module is used for inputting the fusion features into the trained confrontation model so as to obtain processing information.

It should be noted that the foregoing explanation of the method embodiment is also applicable to the system of this embodiment, and is not repeated here.

The application also provides an electronic device, which comprises a memory, a processor and a computer program stored in the memory and capable of running on the processor, wherein the processor executes the computer program to realize the virtual network mapping method based on the generation countermeasure network.

The present application also provides a computer-readable storage medium, in which a computer program is stored, and the computer program, when executed by a processor, can implement the above virtual network mapping method based on generation of a countermeasure network.

Fig. 2 is an exemplary block diagram of an electronic device capable of implementing a virtual network mapping method based on generation of a countermeasure network provided according to an embodiment of the present application.

As shown in fig. 2, the electronic device includes an input device 501, an input interface 502, a central processor 503, a memory 504, an output interface 505, and an output device 506. The input interface 502, the central processing unit 503, the memory 504 and the output interface 505 are connected to each other through a bus 507, and the input device 501 and the output device 506 are connected to the bus 507 through the input interface 502 and the output interface 505, respectively, and further connected to other components of the electronic device. Specifically, the input device 504 receives input information from the outside and transmits the input information to the central processor 503 through the input interface 502; the central processor 503 processes input information based on computer-executable instructions stored in the memory 504 to generate output information, temporarily or permanently stores the output information in the memory 504, and then transmits the output information to the output device 506 through the output interface 505; the output device 506 outputs the output information to the outside of the electronic device for use by the user.

That is, the electronic device shown in fig. 2 may also be implemented to include: a memory storing computer-executable instructions; and one or more processors which, when executing the computer-executable instructions, may implement the virtual network mapping method based on generating a countermeasure network described in connection with fig. 1.

In one embodiment, the electronic device shown in fig. 2 may be implemented to include: a memory 504 configured to store executable program code; one or more processors 503 configured to execute executable program code stored in the memory 504 to perform the human-machine multi-turn dialog method in the above-described embodiments.

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

The memory may include forms of volatile memory in a computer readable medium, Random Access Memory (RAM) and/or non-volatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). Memory is an example of a computer-readable medium.

Computer-readable media include both non-transitory and non-transitory, removable and non-removable media that implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer 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 (DVD) 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 that can be accessed by a computing device.

As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.

Furthermore, it will be obvious that the term "comprising" does not exclude other elements or steps. A plurality of units, modules or devices recited in the device claims may also be implemented by one unit or overall device by software or hardware.

The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present application. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks identified in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.

The Processor in this embodiment may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf Programmable Gate Array (FPGA) or other Programmable logic device, a discrete Gate or transistor logic device, a discrete hardware component, and so on. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.

The memory may be used to store computer programs and/or modules, and the processor may implement various functions of the apparatus/terminal device by running or executing the computer programs and/or modules stored in the memory, as well as by invoking data stored in the memory. The memory may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required by at least one function (such as a sound playing function, an image playing function, etc.), and the like; the storage data area may store data (such as audio data, a phonebook, etc.) created according to the use of the cellular phone, and the like. In addition, the memory may include high speed random access memory, and may also include non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), at least one magnetic disk storage device, a Flash memory device, or other volatile solid state storage device.

In this embodiment, the module/unit integrated with the apparatus/terminal device may be stored in a computer-readable storage medium if it is implemented in the form of a software functional unit and sold or used as a separate product. Based on such understanding, all or part of the flow in the method according to the embodiments of the present invention may also be implemented by a computer program to instruct related hardware, where the computer program may be stored in a computer readable storage medium, and when the computer program is executed by a processor, the computer program may implement the steps of the embodiments of the method. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer readable medium may include: any entity or device capable of carrying computer program code, recording medium, U.S. disk, removable hard disk, magnetic disk, optical disk, computer Memory, Read-Only Memory (ROM), Random Access Memory (RAM), electrical carrier wave signals, telecommunications signals, software distribution media, and the like. It should be noted that the computer readable medium may contain content that is appropriately increased or decreased as required by legislation and patent practice in the jurisdiction. Although the present application has been described with reference to the preferred embodiments, it is not intended to limit the present application, and those skilled in the art can make variations and modifications without departing from the spirit and scope of the present application.

As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.

Although the invention has been described in detail hereinabove with respect to a general description and specific embodiments thereof, it will be apparent to those skilled in the art that modifications or improvements may be made thereto based on the invention. Accordingly, such modifications and improvements are intended to be within the scope of the invention as claimed.