CN112367708A - Network resource allocation method and device - Google Patents
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Abstract
The application provides a network resource allocation method and a device, which can obtain a service characteristic value of a target service; and inputting the obtained service characteristic value into the trained neural network model to obtain transmission resources required by target service transmission, and issuing the transmission resources to the target equipment so that the target equipment transmits the target service by using the transmission resources. Therefore, by applying the technical scheme provided by the embodiment of the application, the service forwarding efficiency can be improved on the basis of saving network resources.
Description
Technical Field
The present application relates to the field of data transmission technologies, and in particular, to a method and an apparatus for allocating network resources.
Background
With the continuous development of mobile internet, various novel applications such as cloud service, AR/VR and car networking are continuously emerging, and in order to cope with the increase of mobile data traffic and the connection of mass equipment with future explosiveness, a fifth generation mobile communication (5G) technology is produced. The FlexE (Flexible Ethernet) technology becomes an important interface technology for realizing service isolation bearer and network fragmentation in a 5G bearer network by using the characteristics of specific physical isolation, channel binding, single set mapping and the like, and based on the important interface technology, data transmission by FlexE has the following characteristics: the multi-granularity rate is flexible and variable, the capacity of decoupling with optical transmission capacity, enhancing QoS facing multi-service bearing and the like is realized, and the method is widely applied to metropolitan area network links such as cloud computing, video, mobile communication and the like.
However, FlexE is allocated on a time slot as the smallest granularity unit. In the prior art, a corresponding time slot is allocated to a service to be forwarded in each router in a manual static configuration mode of a command line, and in practice, the traffic of the service is not fixed, and under many conditions, the actually occupied resource of the service does not reach the transmission resource allocated to the service, and even has a large difference with the transmission resource allocated to the service, which is a waste of resources for the network resource with a large margin, and meanwhile, under the condition that there are many routers, the efficiency of forwarding the service by the router is low in the manual static configuration mode of the command line.
Disclosure of Invention
In view of this, the present application provides a method and an apparatus for allocating network resources, so as to improve the service forwarding efficiency on the basis of saving network resources.
Specifically, the method is realized through the following technical scheme:
in one aspect, an embodiment of the present application provides a method for allocating network resources, where the method is applied to a network device in an SDN network, and includes:
obtaining a service characteristic value of the target service;
and inputting the obtained service characteristic value into a trained neural network model to obtain transmission resources required for transmitting the target service, and issuing the transmission resources to the target equipment so that the target equipment transmits the target service by using the transmission resources.
On the other hand, based on the same concept, an embodiment of the present application further provides a network resource allocation apparatus, where the apparatus is applied to a network device in an SDN network, and the apparatus includes:
a characteristic value obtaining unit, configured to obtain a service characteristic value of the target service;
and the transmission resource obtaining unit is used for inputting the obtained service characteristic value into the trained neural network model to obtain the transmission resource required by transmitting the target service, and sending the transmission resource to the target equipment so that the target equipment transmits the target service by using the transmission resource.
According to the technical scheme, transmission resources are distributed to the service in a manual static configuration mode of a command line, but the network device of the SDN network inputs the service characteristic value of the service into a trained neural network model, the transmission resources distributed to the service are dynamically obtained, and the obtained transmission resources are issued to the device receiving the service, so that the service forwarding efficiency can be improved on the basis of saving network resources.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present disclosure and together with the description, serve to explain the principles of the disclosure.
Fig. 1 is a schematic flowchart of a network resource allocation method according to an embodiment of the present application;
fig. 2 is a schematic flow chart of adjusting a service characteristic value according to an embodiment of the present application;
FIG. 3 is a flow chart of an exemplary video resource allocation provided by an embodiment of the present application;
fig. 4 is a schematic structural diagram of a network resource allocation apparatus according to an embodiment of the present application;
fig. 5 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
Detailed Description
The application is applied to a network device in an SDN network, the SDN network comprises a plurality of network devices, the network devices can be an SDN controller and other network devices connected with the SDN controller, and the plurality of network devices connected with the SDN controller communicate with each other through a Flexe network.
In order to make the technical solutions in the embodiments of the present invention better understood and make the above objects, features and advantages of the embodiments of the present invention more comprehensible, the technical solutions in the embodiments of the present invention are described in further detail below with reference to the accompanying drawings.
Referring to fig. 1, fig. 1 is a flowchart of a Network resource allocation method according to an embodiment of the present invention, where the method may be applied to a Network device in an SDN (Software Defined Network), and in an example, the Network device may be an SDN controller or another Network device connected to the SDN controller, which is not limited in this application.
As shown in fig. 1, the process may include the following steps:
The target service does not refer to a fixed service, but may refer to a service to be transmitted by any network, and the following description of the embodiment of the present application is omitted.
Accordingly, the target device may be any device in the SDN network, such as a router and a switch, for forwarding the target traffic.
When the target device receives the target service to be forwarded, it is not certain how many transmission resources need to be allocated to the target service to perform network transmission on the target service, and based on this, as an embodiment: the target device may send a resource allocation request to the network device to cause the network resource to send transmission resources allocated for the target service to the target device.
Some behavior characteristics of the service in the network transmission process imply transmission resources required by the service in the network transmission process, and the behavior characteristics are service characteristic values of the service. As an embodiment, the service characteristic value of the target service may include any combination of the following characteristic values: occupied bandwidth, occupied duration, total data volume, traffic type, burst traffic, latency, and ACK (acknowledgement character) information.
And 102, inputting the obtained service characteristic value into the trained neural network model to obtain transmission resources required by target service transmission, and sending the transmission resources to the target equipment so that the target equipment transmits the target service by using the transmission resources.
The input information of the trained neural network model is the service characteristic value of the service, and the output information is the transmission resource required for transmitting the service.
In the application, the trained neural network model can dynamically estimate the transmission resources required by the service according to the service characteristic value of the service at the current time.
The transmission resources may include at least: time slot, bandwidth, or duration.
As an example, the neural network model may be trained by:
obtaining sample service characteristic values corresponding to services of different service classes;
and training the neural network model according to the configured sample service characteristic value and the sample transmission resource corresponding to the sample service characteristic value.
In this embodiment, the sample traffic characteristic value is a traffic characteristic value of traffic of different traffic classes.
And taking the sample service characteristic value as input information of the neural network model, and taking the sample transmission resource corresponding to the sample service characteristic value as a training reference to train the neural network model.
Therefore, in the technical scheme provided by the embodiment of the application, the sample service characteristic values corresponding to the services of different service classes are rich and comprehensive, so that the trained neural network model can accurately predict the transmission resources of the services.
Therefore, in the technical solution provided in the embodiment of the present application, transmission resources are not allocated to a service in a manner of manual static configuration of a command line in the embodiment of the present application, but when a target device receives a service to be forwarded, a network device of an SDN network inputs a service feature value of the service into a trained neural network model, dynamically obtains the transmission resources allocated to the service, and issues the transmission resources to the target device, so that the service forwarding efficiency can be improved on the basis of saving network resources.
Thus, the flow shown in fig. 1 is completed.
In the flow shown in fig. 1, as an embodiment, before executing the flow shown in fig. 1, the target device first receives a target service to be forwarded, and on this premise, when the target device receives the target service to be forwarded, the network device executes the above steps 101 to 102, so that after the network device issues a predicted transmission resource to the target device, the target service is quickly forwarded.
As another embodiment, before executing the flow shown in fig. 1, a network manager (or an operation and maintenance person) receives a target service required by a service department (or a client), and the network manager inputs a service characteristic value of the target service in a network device, and on this premise, the network device executes the steps 101 to 102, so that after the network device issues a predicted transmission resource to the target device, when the target device receives the target service to be forwarded, the target service can be quickly forwarded, thereby further improving service forwarding efficiency.
In the flow shown in fig. 1, there are many implementation manners of step 101, and at least six implementation manners may be included as follows:
the first implementation mode comprises the following steps: and acquiring a first class characteristic value of the target service input from the outside, and determining the first class characteristic value as the service characteristic value of the target service.
For more service feature values and depending on the manual input of these feature values by the user, the burden on the user may be increased and the service forwarding efficiency may be affected, in view of this, the specific implementation manner of step 101 may be:
the second implementation mode comprises the following steps: and searching a second class characteristic value associated with the first class characteristic value from the configured characteristic value list, and determining the second class characteristic value as the service characteristic value of the target service.
By the technical scheme provided by the second implementation mode, the implementation mode can reduce the labor burden and correspondingly reduce the human error rate.
In order to reduce the artificial burden and dynamically and more realistically obtain the real service characteristic value of the service, the specific implementation manner of step 101 may be:
the third implementation manner is as follows: and receiving a third class characteristic value of the target service obtained by the target equipment through analyzing the target service, and determining the third class characteristic value as the service characteristic value of the target service.
According to the technical scheme provided by the third implementation mode, the implementation mode can reduce the labor burden, reduce the artificial error rate and improve the accuracy rate of distributing transmission resources for the service.
For the case that the traffic data of some services is not fixed and there are more service feature values of the services, in view of this, the specific implementation manner of step 101 may also be:
the fourth implementation manner is as follows: the method comprises the steps of obtaining a first class characteristic value of target service input from outside, searching a second class characteristic value associated with the first class characteristic value from a configured characteristic value list, and determining the first class characteristic value and the second class characteristic value as a service characteristic value of the target service.
The first-class characteristic value may be a characteristic value that a user inputs a transmission resource of the target service with a large influence according to an actual experience value, for example, the first-class characteristic value may be an occupied bandwidth estimated by the target service, or may be a service category of the target service.
The second-class characteristic value may be a characteristic value determined by the user according to an actual experience value, which has a small influence on the transmission resource of the target service, and based on this, after the first-class characteristic value is determined, for the second-class characteristic value, the second-class characteristic values may be configured in advance according to the first-class characteristic value. As an embodiment, a specific implementation manner of finding the second type of eigenvalue associated with the first type of eigenvalue from the configured eigenvalue list may be as follows: obtaining a first class characteristic value of externally input target service; and determining a second class characteristic value corresponding to the characteristic value range to which the first class characteristic value belongs from the configured characteristic value list.
For example, assuming that the first class of feature value obtained by the external input is a fixed bandwidth 50G, and the service class is a video, it is determined from the configured feature value list that the video class with the fixed bandwidth 50G in the range of 40G to 80G, and the second class of feature value corresponding to the video class in the range of 40G to 80G is a total data volume of 100G, and the occupied time duration is 20 minutes.
The technical proposal provided by the fourth implementation mode can show that the implementation mode not only can reduce the labor burden, but also can flexibly set the first class characteristic value and the second class characteristic value according to the characteristics of the service so as to ensure the accuracy of the service characteristic value.
For some service characteristic values, there may be a case where the target device cannot obtain the service characteristic values by analyzing the service, and in view of this, the specific implementation manner of step 101 may also be:
the fifth implementation manner is as follows: the method comprises the steps of obtaining a first class characteristic value of target service input from outside, receiving a third class characteristic value of the target service obtained by analyzing the target service through target equipment, and determining the first class characteristic value and the third class characteristic value as service characteristic values of the target service.
The third type of characteristic value is obtained by analyzing the target service by the target device, for example, determining the service type of the target service, the total data amount of the target service, and the like by analyzing the target service.
The technical proposal provided by the fifth implementation mode can show that the implementation mode not only can reduce the labor burden, but also can obtain the real service characteristic value of the service through the target equipment, and further flexibly set the first class characteristic value and the third class characteristic value, thereby further ensuring the accuracy of the service characteristic value.
Based on the descriptions of the first implementation manner to the fifth implementation manner, in order to obtain richer and more comprehensive service feature values, the specific implementation manner of step 101 may also be:
the sixth implementation manner is as follows: the method comprises the steps of obtaining a first class characteristic value of target service input from the outside, searching a second class characteristic value associated with the first class characteristic value from a configured characteristic value list, receiving a third class characteristic value of the target service obtained by target equipment through analyzing the target service, and determining the second class characteristic value and the third class characteristic value as a service characteristic value of the target service.
In this implementation manner, the first class of eigenvalues may be eigenvalues that are input by the user according to actual experience values and have a large influence on the transmission resources of the target service and cannot be obtained from the target device, the second class of eigenvalues may be eigenvalues that are determined by the user according to actual experience values and have a small influence on the transmission resources of the target service and cannot be obtained from the target device, and the third class of eigenvalues are eigenvalues obtained by analyzing the target service by the target device.
In the technical solution provided by the sixth implementation manner, not only can the burden of inputting the service characteristic value to the human power be reduced, but also the real characteristic value of the service can be obtained through the target device, so that the obtained service characteristic value is rich and comprehensive, and the accuracy of the service characteristic value can be further ensured.
Referring to fig. 2, fig. 2 is a flowchart of a further exemplary embodiment based on various specific implementation manners for implementing the step 101 described above according to an embodiment of the present application. As shown in fig. 2, the process may include the following steps:
In this step, the transmission resource actually occupied when obtaining the target service may include two cases:
the first case is: the transmission resource actually occupied when transmitting the target service is larger than the transmission resource transmitted to the target device by the network device in step 102.
The second case is: and the transmission resource actually occupied when the target service is transmitted is smaller than the transmission resource issued to the target equipment.
Based on the above two cases, as an embodiment, the implementation manner of the implementing step 201 may include the following steps:
and if the transmission resources of the target equipment are completely occupied, determining the transmission resources actually occupied by the target service according to the transmission characteristics of the target service.
In this step, the transmission resources issued to the target device are fully occupied, which means that the first situation is met. That is, when the transmission resource actually occupied by the target service is greater than the transmission resource issued by the network device to the target device, there may be transmission characteristics of packet loss and code break.
In addition, for the first case, as an embodiment, after determining the transmission resource actually occupied by the target service, the transmission resource actually occupied may also be reported, so that the user may determine whether to further increase the transmission resource.
And if the transmission resources issued to the target equipment are not completely occupied, acquiring the transmission resources actually occupied by the target equipment for transmitting the target service from the target equipment.
In this step, the transmission resources delivered to the target device are fully occupied, which means that the second situation is met.
As an embodiment, the network device may be triggered to execute step 202 by reporting the transmission resource actually occupied by the transmission target service when it is determined that the ratio of the transmission resource delivered to the target device to the transmission resource actually occupied by the transmission target service is greater than the threshold.
For the first situation, based on the above embodiment, if the user determines to increase the transmission resource, the service feature value of the target service may be readjusted according to the transmission resource actually occupied by the reported target service.
When the service characteristic value is obtained through the first implementation manner, the first type characteristic value is adjusted according to the obtained transmission resource actually occupied by the target device when the target service is transmitted.
When the service characteristic value is obtained through the second implementation manner, the second type characteristic value is adjusted according to the obtained transmission resource actually occupied by the target device when the target device transmits the target service.
And when the service characteristic value is obtained through the fourth implementation manner, the fifth implementation manner or the sixth implementation manner, adjusting the first class characteristic value and the second class characteristic value according to the obtained transmission resource actually occupied by the target device when the target device transmits the target service.
Therefore, in the technical solution of the embodiment of the present application, the embodiment of the present application adjusts the service characteristic value according to the transmission resource actually occupied by the target device when transmitting the target service, so that when the target device receives the service to be forwarded next time, the network device of the SDN network inputs the adjusted service characteristic value of the service into the trained neural network model, dynamically obtains the transmission resource allocated to the service, and issues the transmission resource to the target device, so that the transmission resource issued to the target device better conforms to the transmission resource actually occupied when transmitting the target service, thereby further saving network resources.
Thus, the flow shown in fig. 2 is completed.
Referring to fig. 3, for an architecture schematic diagram of an application scenario provided in an embodiment of the present application, as shown in fig. 3, a network resource is provided with 2 calendars, and 20 time slots are provided in 1 calendar, and 1 time slot is a small rectangle in fig. 3, in the application scenario, a target device is a router, and a target service received by the router is a video service, in this embodiment, a network device applied in an SDN network may be an SDN controller, and a specific flow of the embodiment is as follows:
firstly, a network manager receives a new video service required by a service department (or a client), the network manager inputs a fixed bandwidth and a service type of the video service into an interactive interface of an SDN controller, the SDN controller obtains the fixed bandwidth and the service type input by a user, and searches occupation duration, time delay and ACK information associated with the obtained fixed bandwidth and service type from a configured characteristic value list, and a router which is subsequently routed by the video service obtains the total data volume and burst flow of the video by analyzing the video, so as to determine the service characteristic value of the video as the occupation bandwidth, the occupation duration, the total data volume, the service type, the burst flow, the time delay and the ACK information.
Secondly, inputting the service characteristic values obtained in the first step into the trained neural network model to obtain 5 time slots required for transmitting the video, such as A, B, C, D and E in fig. 3, and sending the 5 time slots to the router, when the router receives a newly added video service, the router can transmit the video by using the 5 time slots, and after the time slots are allocated, the video can be transmitted in the 5 time slots.
As can be seen from fig. 3, the router forwards the video in turn in the order of the 5 time slots after receiving A, B, C, D and E, which are 5 time slots.
And thirdly, in the forwarding process, if the 5 time slots of the router are occupied completely, the fact that the bandwidth occupied by the video actually is more than 5 time slots is indicated, and the situations of packet loss, congestion and the like already occur, and based on the situations, the time slots occupied by the video actually are determined according to the transmission characteristics of the video service, such as the packet loss rate, the bit-break rate and the like, and the actual state of the network is reported, namely the time slots occupied by the video actually.
If the 5 time slots of the router are not fully occupied (for example, a large number of Error Control Block fillings occur in the time slots), it indicates that the time slot actually occupied by the video is less than 5 time slots, and the transmission resource actually occupied by the router when the router transmits the video is acquired from the router.
And fourthly, adjusting the fixed bandwidth and the service type according to the time slot actually occupied by the router when the video is transmitted, which is obtained in the third step, searching the occupied duration, the time delay and the ACK information associated with the adjusted fixed bandwidth and the adjusted service type from the configured characteristic value list, and determining the total data volume and the burst flow obtained by analyzing the video from the router as the service characteristic value of the next video.
The examples provided in this application were analyzed above.
Based on the same application concept as the method described above, an embodiment of the present application further provides a network resource allocation apparatus 400, which is applied to a network device in an SDN network, and is shown in fig. 4, and is a structural diagram of the apparatus, where the apparatus includes:
a characteristic value obtaining unit 401, configured to obtain a service characteristic value of the target service.
A transmission resource obtaining unit 402, configured to input the obtained service feature value into a trained neural network model, obtain a transmission resource required for transmitting the target service, and send the transmission resource to the target device, so that the target device transmits the target service by using the transmission resource.
As an embodiment, the feature value obtaining unit 401 may include:
the first-class characteristic value obtaining subunit is used for obtaining a first-class characteristic value of externally input target service; and/or the presence of a gas in the gas,
a second-class characteristic value obtaining subunit, configured to search, from a configured characteristic value list, a second-class characteristic value associated with the first-class characteristic value; and/or the presence of a gas in the gas,
a third-class eigenvalue obtaining subunit, configured to receive a third-class eigenvalue of the target service, where the third-class eigenvalue of the target service is obtained by analyzing the target service by the target device;
and the characteristic value determining subunit is configured to determine the first class of characteristic values, and/or the second class of characteristic values, and/or the third class of characteristic values as service characteristic values of the target service.
As an embodiment, the apparatus may further include:
a transmission resource obtaining unit, configured to obtain a transmission resource actually occupied by the target device when transmitting the target service;
and the characteristic value adjusting unit is used for adjusting the first type characteristic value and/or the second type characteristic value according to the obtained transmission resource actually occupied by the target equipment when the target equipment transmits the target service.
As an embodiment, the transmission resource obtaining unit is specifically configured to:
if the transmission resources issued to the target equipment are completely occupied, determining the transmission resources actually occupied by the target service according to the transmission characteristics of the target service;
and if the transmission resources issued to the target equipment are not completely occupied, acquiring the transmission resources actually occupied by the target equipment when the target equipment transmits the target service from the target equipment.
As an embodiment, the apparatus may further include: the model training unit is used for training the neural network model;
wherein the model training unit is specifically configured to:
obtaining sample service characteristic values corresponding to services of different service classes;
and training the neural network model according to the configured sample service characteristic value and the sample transmission resource corresponding to the sample service characteristic value.
As an embodiment, the service characteristic value of the target service may include any combination of the following characteristic values:
occupied bandwidth, occupied duration, total data volume, service type, burst flow, time delay and ACK information.
As an embodiment, the transmission resource at least includes: time slot, bandwidth and duration.
In summary, in the technical solution provided in the embodiment of the present application, instead of allocating transmission resources for a service in a manner of manual static configuration of a command line, a network device of an SDN network inputs a service feature value of the service into a trained neural network model, dynamically obtains the transmission resources allocated for the service, and sends the obtained transmission resources to a device receiving the service, so as to improve service forwarding efficiency on the basis of saving network resources.
The implementation process of the functions and actions of each unit in the above device is specifically described in the implementation process of the corresponding step in the above method, and is not described herein again.
In the electronic device provided in the embodiment of the present application, from a hardware level, a schematic diagram of a hardware architecture can be seen as shown in fig. 5. The method comprises the following steps: a machine-readable storage medium and a processor, wherein: the machine-readable storage medium stores machine-executable instructions executable by the processor; the processor is configured to execute machine-executable instructions to perform the network resource allocation operations disclosed in the above examples.
Machine-readable storage media are provided by embodiments of the present application that store machine-executable instructions that, when invoked and executed by a processor, cause the processor to implement the network resource allocation operations disclosed in the examples above.
Here, a machine-readable storage medium may be any electronic, magnetic, optical, or other physical storage device that can contain or store information such as executable instructions, data, and so forth. For example, the machine-readable storage medium may be: a RAM (random Access Memory), a volatile Memory, a non-volatile Memory, a flash Memory, a storage drive (e.g., a hard drive), a solid state drive, any type of storage disk (e.g., an optical disk, a dvd, etc.), or similar storage medium, or a combination thereof.
The systems, devices, modules or units illustrated in the above embodiments may be implemented by a computer chip or an entity, or by a product with certain functions. A typical implementation device is a computer, which may take the form of a personal computer, laptop computer, cellular telephone, camera phone, smart phone, personal digital assistant, media player, navigation device, email messaging device, game console, tablet computer, wearable device, or a combination of any of these devices.
For convenience of description, the above devices are described as being divided into various units by function, and are described separately. Of course, the functionality of the units may be implemented in one or more software and/or hardware when implementing 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, embodiments of 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.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
Furthermore, these computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
For the device embodiments, since they substantially correspond to the method embodiments, reference may be made to the partial description of the method embodiments for relevant points. The above-described embodiments of the apparatus are merely illustrative, and the units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the scheme of the application. One of ordinary skill in the art can understand and implement it without inventive effort.
The above description is only exemplary of the present application and should not be taken as limiting the present application, as any modification, equivalent replacement, or improvement made within the spirit and principle of the present application should be included in the scope of protection of the present application.
Claims (14)
1. A network resource allocation method is applied to a network device in an SDN network, and comprises the following steps:
obtaining a service characteristic value of the target service;
and inputting the obtained service characteristic value into a trained neural network model to obtain transmission resources required for transmitting the target service, and issuing the transmission resources to the target equipment so that the target equipment transmits the target service by using the transmission resources.
2. The method of claim 1, wherein the obtaining the service characteristic value of the target service comprises:
obtaining a first class characteristic value of externally input target service; and/or the presence of a gas in the gas,
searching a second class characteristic value associated with the first class characteristic value from a configured characteristic value list; and/or the presence of a gas in the gas,
receiving a third class characteristic value of the target service obtained by the target equipment through analyzing the target service;
and determining the first class characteristic value, the second class characteristic value and/or the third class characteristic value as the service characteristic value of the target service.
3. The method of claim 2, wherein after the target device utilizes the transmission resource to transmit the target traffic, the method further comprises:
acquiring transmission resources actually occupied by the target equipment when transmitting the target service;
and adjusting the first class characteristic value and/or the second class characteristic value according to the obtained transmission resource actually occupied by the target equipment when transmitting the target service.
4. The method of claim 3, wherein the obtaining transmission resources actually occupied by the target device when transmitting the target service comprises:
if the transmission resources issued to the target equipment are completely occupied, determining the transmission resources actually occupied by the target service according to the transmission characteristics of the target service;
and if the transmission resources issued to the target equipment are not completely occupied, acquiring the transmission resources actually occupied by the target equipment when the target equipment transmits the target service from the target equipment.
5. The method of claim 1, wherein the neural network model is trained by:
obtaining sample service characteristic values corresponding to services of different service classes;
and training the neural network model according to the configured sample service characteristic value and the sample transmission resource corresponding to the sample service characteristic value.
6. The method of claim 1, wherein the service characteristic value of the target service comprises any combination of the following characteristic values:
occupied bandwidth, occupied duration, total data volume, service type, burst flow, time delay and ACK information.
7. The method of claim 1, wherein the transmission resources comprise at least: time slot, bandwidth and duration.
8. A network resource allocation device, applied to a network device in an SDN network, includes:
a characteristic value obtaining unit, configured to obtain a service characteristic value of the target service;
and the transmission resource obtaining unit is used for inputting the obtained service characteristic value into the trained neural network model to obtain the transmission resource required by transmitting the target service, and sending the transmission resource to the target equipment so that the target equipment transmits the target service by using the transmission resource.
9. The apparatus of claim 8, wherein the eigenvalue obtaining unit comprises:
the first-class characteristic value obtaining subunit is used for obtaining a first-class characteristic value of externally input target service; and/or the presence of a gas in the gas,
a second-class characteristic value obtaining subunit, configured to search, from a configured characteristic value list, a second-class characteristic value associated with the first-class characteristic value; and/or the presence of a gas in the gas,
a third-class eigenvalue obtaining subunit, configured to receive a third-class eigenvalue of the target service, where the third-class eigenvalue of the target service is obtained by analyzing the target service by the target device;
and the characteristic value determining subunit is configured to determine the first class of characteristic values, and/or the second class of characteristic values, and/or the third class of characteristic values as service characteristic values of the target service.
10. The apparatus of claim 9, further comprising:
a transmission resource obtaining unit, configured to obtain a transmission resource actually occupied by the target device when transmitting the target service;
and the characteristic value adjusting unit is used for adjusting the first type characteristic value and/or the second type characteristic value according to the obtained transmission resource actually occupied by the target equipment when the target equipment transmits the target service.
11. The apparatus according to claim 10, wherein the transmission resource obtaining unit is specifically configured to:
if the transmission resources issued by the target equipment are detected to be completely occupied, determining the transmission resources actually occupied by the target service according to the transmission characteristics of the target service;
and if the transmission resources issued to the target equipment are not completely occupied, acquiring the transmission resources actually occupied by the target equipment when the target equipment transmits the target service from the target equipment.
12. The apparatus of claim 8, further comprising: the model training unit is used for training the neural network model;
wherein the model training unit is specifically configured to:
obtaining sample service characteristic values corresponding to services of different service classes;
and training the neural network model according to the configured sample service characteristic value and the sample transmission resource corresponding to the sample service characteristic value.
13. The apparatus of claim 8, wherein the traffic characteristic value of the target traffic comprises any combination of the following characteristic values:
occupied bandwidth, occupied duration, total data volume, service type, burst flow, time delay and ACK information.
14. The apparatus of claim 1, wherein the transmission resources comprise at least: time slot, bandwidth and duration.
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