CN114338400A - SDN dynamic control method and device - Google Patents
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Abstract
The present disclosure provides a method and apparatus for SDN network dynamic control; relates to the technical field of wireless communication. The method comprises the following steps: acquiring network link information and network node information of an SDN network in real time; inputting the collected information into a trained retransmission prediction model to obtain first retransmission information of each node in the SDN network, and determining a target tracking node; calculating characteristic importance information of the target tracking node based on the trained retransmission prediction model; determining key characteristics of corresponding target tracking nodes; and generating corresponding control information based on the key characteristics, and issuing the control information to the corresponding target tracking node to adjust the configuration information of the target tracking node. The method and the device can solve the problem of network delay caused by high packet loss retransmission rate of the wireless SDN network in the prior art.
Description
Technical Field
The present disclosure relates to the field of wireless network technologies, and in particular, to an SDN network dynamic control method, an SDN network dynamic control apparatus, a computer-readable storage medium, and an electronic device.
Background
Software Defined networking (sdn) (software Defined networking) is a software-based network architecture technology, supports centralized network state control, and implements transparency of underlying network facilities to upper layer applications. Meanwhile, the method has flexible software programming capability, improves the automatic management and control capability of the network, and can solve the problems of limited resource scale expansion, poor networking flexibility, difficulty in quickly meeting service requirements and the like of the conventional network system.
For a wireless SDN network, due to the sharing characteristic of media, the phenomenon that multiple network nodes transmit on the same channel at the same time to cause collision can occur, and further frame loss and retransmission of a channel-sent data packet are caused. When the packet loss rate is high, a large amount of retransmission of the link is caused, thereby causing network delay.
It is to be noted that the information disclosed in the above background section is only for enhancement of understanding of the background of the present disclosure, and thus may include information that does not constitute prior art known to those of ordinary skill in the art.
Disclosure of Invention
An object of the embodiments of the present disclosure is to provide a dynamic SDN network control method, a dynamic SDN network control apparatus, a computer-readable storage medium, and an electronic device, so as to solve, to a certain extent, a problem of network delay caused when a packet loss retransmission rate of a wireless SDN network is high in a related art.
According to a first aspect of the present disclosure, a method for SDN network dynamic control is provided, the method comprising:
acquiring network link information and network node information of an SDN network in real time;
inputting the network link information and the network node information into a trained retransmission prediction model to obtain first retransmission information of each node in the SDN network; determining a target tracking node based on the first retransmission information; the retransmission prediction model comprises a random forest model;
calculating characteristic importance information of the target tracking node based on the trained retransmission prediction model; determining key features of corresponding target tracking nodes based on the feature importance information;
and generating corresponding control information based on the key characteristics, and issuing the control information to the corresponding target tracking node to adjust the configuration information of the target tracking node.
In an exemplary embodiment of the present disclosure, based on the foregoing solution, before the acquiring, in real time, network link information and network node information of an SDN network, the method further includes:
sending a probing data packet to each node in the SDN network at a preset time interval.
In an exemplary embodiment of the present disclosure, based on the foregoing scheme, after the determining the target tracking node based on the first retransmission information, the method further includes:
and dynamically adjusting the information acquisition frequency of the target tracking node.
In an exemplary embodiment of the present disclosure, based on the foregoing scheme, the method further includes:
acquiring historical network link information, historical network node information and corresponding historical first retransmission information of the SDN network; the historical network link information comprises historical channel state information, historical signal state information and historical link packet loss information of each node, and the historical network node information comprises historical node position information and historical node energy information;
and generating a random forest model by using a random sampling and splitting mode by taking the historical channel state information, the historical signal state information, the historical link packet loss information, the historical node position information and the historical node energy information of each node as a group of training samples and the corresponding historical first retransmission information as sample labels until a training termination condition is reached, and obtaining the trained random forest model.
In an exemplary embodiment of the present disclosure, based on the foregoing scheme, the calculating, based on the trained retransmission prediction model, feature importance information of the target tracking node includes:
and determining the characteristic importance of each target tracking node by calculating the Kini index or the out-of-bag data error rate of each target tracking node based on the trained retransmission prediction model.
In an exemplary embodiment of the present disclosure, based on the foregoing scheme, the determining the key feature of the corresponding target tracking node based on the feature importance information includes:
based on the size of the feature importance information, sorting the features of each target tracking node, and screening out the features positioned at the top k positions as candidate features; k is a positive number;
deleting any candidate feature from the trained retransmission prediction model, and determining second retransmission information of the model after the feature is deleted;
and selecting the candidate feature corresponding to the second retransmission information when the second retransmission information is minimum as the key feature of the current target tracking node.
In an exemplary embodiment of the present disclosure, based on the foregoing scheme, the network link information includes channel state information, signal state information, and link packet loss information of each node, and the network node information includes node location information and node energy information; generating corresponding control information based on the key features to adjust configuration information of the target tracking node, including:
when the key feature is channel state information, generating channel adjustment control information to adjust the channel allocation of the target tracking node;
when the key feature is signal state information, generating signal state adjustment control information to adjust the signal state of the target tracking node;
when the key feature is node position information, generating node position adjustment control information to adjust the position of the target tracking node or the neighbor node;
when the key feature is node energy information, generating node energy adjustment control information to adjust the transmission energy of the target tracking node or the neighbor node;
and when the key characteristic is the link packet loss information, generating path planning control information to re-plan the transmission path of the network node.
According to a second aspect of the present disclosure, there is provided an SDN network dynamic control apparatus, including:
the system comprises an acquisition module, a processing module and a processing module, wherein the acquisition module is used for acquiring network link information and network node information of the SDN in real time;
a tracking node determination module, configured to input the network link information and the network node information into a trained retransmission prediction model to obtain first retransmission information of each node in the SDN network; determining a target tracking node based on the first retransmission information; the retransmission prediction model comprises a random forest model;
the calculation module is used for calculating the characteristic importance information of the target tracking node based on the trained retransmission prediction model; determining key features of corresponding target tracking nodes based on the feature importance information;
and the control module is used for generating corresponding control information based on the key characteristics and sending the control information to the corresponding target tracking node so as to adjust the configuration information of the target tracking node.
According to a third aspect of the present disclosure, there is provided a computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements the method of any one of the above.
According to a fourth aspect of the present disclosure, there is provided an electronic device comprising: a processor; and a memory for storing executable instructions of the processor; wherein the processor is configured to perform the method of any one of the above via execution of the executable instructions.
Exemplary embodiments of the present disclosure may have some or all of the following benefits:
in the dynamic control method for the SDN network provided in the disclosed example embodiment, on one hand, by acquiring network link information and network node information of the SDN network in real time and dynamically determining a target tracking node, namely a high-frequency retransmission node and a topology region thereof, through a trained retransmission prediction model, a node with a high retransmission rate can be dynamically tracked, a node with an excessively high retransmission rate can be quickly and timely found, and the occurrence of an excessively high packet error rate is avoided. On the other hand, calculating characteristic importance information of the target tracking node based on the trained retransmission prediction model; determining key features of corresponding target tracking nodes based on the feature importance information; based on the key characteristics, generating corresponding control information, and sending the control information to the corresponding target tracking node, so that the target tracking node adjusts the configuration information of the target tracking node based on the control information, can analyze key influence factors of the node with high retransmission rate, and further control and adjust the corresponding node, thereby avoiding the occurrence of network delay. In addition, based on the determined network retransmission rate, the error evaluation of the network condition can be avoided, and the network evaluation accuracy is improved.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure.
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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. It is to be understood that the drawings in the following description are merely exemplary of the disclosure, and that other drawings may be derived from those drawings by one of ordinary skill in the art without the exercise of inventive faculty.
Fig. 1 illustrates a schematic diagram of an exemplary scenario architecture of an existing SDN network dynamic control method and apparatus.
Fig. 2 schematically shows a flow chart of a SDN network dynamic control method according to one embodiment of the present disclosure.
FIG. 3 schematically illustrates a flow chart for determining key features in one embodiment according to the present disclosure.
Fig. 4 schematically shows a flowchart of a specific implementation process of the SDN network dynamic control method according to an embodiment of the present disclosure.
Fig. 5 schematically shows a structural block diagram of an SDN network dynamic control device according to another embodiment of the present disclosure.
FIG. 6 illustrates a block diagram of an electronic device suitable for use in implementing embodiments of the present disclosure.
Detailed Description
Example embodiments will now be described more fully with reference to the accompanying drawings. Example embodiments may, however, be embodied in many different forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of example embodiments to those skilled in the art. The described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are provided to give a thorough understanding of embodiments of the disclosure. One skilled in the relevant art will recognize, however, that the subject matter of the present disclosure can be practiced without one or more of the specific details, or with other methods, components, devices, steps, and the like. In other instances, well-known technical solutions have not been shown or described in detail to avoid obscuring aspects of the present disclosure.
Furthermore, the drawings are merely schematic illustrations of the present disclosure and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and thus their repetitive description will be omitted. Some of the block diagrams shown in the figures are functional entities and do not necessarily correspond to physically or logically separate entities. These functional entities may be implemented in the form of software, or in one or more hardware modules or integrated circuits, or in different networks and/or processor devices and/or microcontroller devices.
Fig. 1 is a schematic diagram illustrating a system architecture 100 of an exemplary application environment to which the SDN network dynamic control method and apparatus according to the embodiments of the present disclosure may be applied. As shown in fig. 1, system architecture 100 may include SDN controller 101, network device 102. SDN controller 101 may be a single server, a group of servers, or a cluster of servers, and network device 102 may include a wireless sensor, a wireless router, a cell phone, or a vehicle with a wireless transmission module, etc. The SDN controller 101 may perform overall control on the network device 102 through a southbound interface, where the number of the network devices 102 may be multiple, each network device corresponds to one wireless network node, and the wireless network nodes form an entire routing transmission network. The number of servers and network devices 102 corresponding to the SDN controller 101 is not limited in this example.
The SDN network dynamic control method provided by the embodiment of the present disclosure may be executed in the SDN controller 101, and accordingly, an SDN network dynamic control device is generally disposed in the SDN controller 101.
For a wireless SDN network, due to the sharing characteristic of media, the phenomenon that multiple network nodes transmit on the same channel at the same time to generate collision can occur, and frame loss and retransmission of data packets sent by the channel are easily caused. Especially for paths up to 19 hops or more or links with packet error rates close to or exceeding 40% may result in a large number of retransmissions and thus network delays.
In current network architectures, there is no mechanism to determine the number of retransmissions required for each packet, and in related approaches, data retransmissions are typically handled locally at the network node and the relevant information is stored at the local MAC layer. The problem that no mechanism can track the retransmission times of the data packet on the basis of the granularity of the nodes (namely hop by hop) is solved. The present disclosure provides a dynamic control method for a wireless SDN network.
The technical solution of the embodiment of the present disclosure is explained in detail below:
referring to fig. 2, an SDN network dynamic control method according to an example embodiment of the present disclosure may include:
step S210, collecting network link information and network node information of the SDN network in real time.
In this example embodiment, the SDN network may contain multiple wireless nodes, each of which collects its network link information and network node information in real time. Link information and node information of network nodes may be collected by a topology discovery module of the SDN controller. A new information acquisition module may also be added to the topology discovery module of the SDN controller to acquire link information and node information. The collected network link information can be stored in a link database, and the collected network node information can be stored in a node database.
In this example embodiment, the network link information may include channel state information, signal state information, and link packet loss information. The channel state information may include channel quality information, the number of shared channels between the node and the neighboring node, and the like, the signal state information may include signal strength information RSSI and signal to noise ratio SINR, the link packet loss information may include packet loss ratios and link packet loss numbers of all links of the node, the network node information includes node position information and node energy information, the node position information may include GPS coordinate information of the node, distance information between the node and the neighboring node, and the like, and the node energy information may include node transmission energy, transmission energy of the neighboring node, and the like.
Step S220, inputting the network link information and the network node information into a trained retransmission prediction model to obtain first retransmission information of each node in the SDN network; determining a target tracking node based on the first retransmission information; the retransmission prediction model comprises a random forest model.
In this exemplary embodiment, the network link information and the network node information of each node may be used as inputs of a pre-trained retransmission prediction model, or may be used as model inputs after performing pre-processing (such as data cleaning or normalization) on the network link information and the network node information of each node. The output of the model is the retransmission information of the node. The retransmission prediction model can be a random forest model, and the trained random forest model is generated by gradually training by utilizing a training data set. The first retransmission information may be packet loss retransmission rate or packet loss retransmission times, or may be other retransmission information, or composite information of multiple pieces of retransmission information, which is not particularly limited in this example.
In this example embodiment, several nodes with the highest first retransmission information may be used as target tracking nodes, and a topology region formed by the target tracking nodes is a target topology region. Therefore, the nodes with higher retransmission rate can be locked for key monitoring and more accurate control adjustment.
Step S230, calculating characteristic importance information of the target tracking node based on the trained retransmission prediction model; and determining key features of the corresponding target tracking nodes based on the feature importance information.
In the present exemplary embodiment, the importance information of a certain feature may be indicated by selecting a feature importance measure index, for example, the feature importance may be determined by a kini index or an out-of-bag data error rate. The feature with higher importance may be used as a key feature, and the number of the key features may be more than one or one, which is not limited by this example.
Step S240, generating corresponding control information based on the key features, and issuing the control information to the corresponding target tracking node to adjust the configuration information of the target tracking node.
In this example embodiment, the key feature may be a feature subordinate to the network link information or the network node information. The corresponding OpenFlow control information may be generated based on a key feature, for example, the key feature is a distance between the current node and a neighboring node, and then the corresponding control information may be to adjust a position of the current node or the neighboring node. The OpenFlow control information of each target tracking node can be issued to the wireless node through a node configuration module of the SDN controller. The configuration information may include information on the channel, transmission energy, location, route, etc. of the node.
In the dynamic control method for the SDN network provided in the disclosed example embodiment, on one hand, by acquiring network link information and network node information of the SDN network in real time and dynamically determining a target tracking node, namely a high-frequency retransmission node and a topology region thereof, through a trained retransmission prediction model, a node with a high retransmission rate can be dynamically tracked, a node with an excessively high retransmission rate can be quickly and timely found, and the occurrence of an excessively high packet error rate is avoided. On the other hand, calculating characteristic importance information of the target tracking node based on the trained retransmission prediction model; determining key features of corresponding target tracking nodes based on the feature importance information; based on the key characteristics, generating corresponding control information, and sending the control information to the corresponding target tracking node, so that the target tracking node adjusts the configuration information of the target tracking node based on the control information, can analyze key influence factors of the node with high retransmission rate, and further control and adjust the corresponding node, thereby avoiding the occurrence of network delay. In addition, based on the determined network retransmission rate, the error evaluation of the network condition can be avoided, and the network evaluation accuracy is improved.
Next, in another embodiment, the above steps are explained in more detail.
In some embodiments, before the acquiring network link information and network node information of the SDN network in real time, the method further includes:
sending a probing data packet to each node in the SDN network at a preset time interval.
In this example embodiment, each node may send a probe Packet "Packet Out" to a corresponding node at a preset fixed time interval, and the time intervals for sending packets of different nodes may be the same or different. The time interval may also be adapted as the detection process progresses. For example, the initial time interval of each node may be set to be the same, and then the difference of each node may be adjusted according to the node tracking condition. For example, the initial time interval may be set to 1 second, i.e., the probe packet is sent every 1 second. The network topology information (link information and node information) is collected in a data packet sending mode, so that the network topology information can be comprehensively and timely mastered, excessive network interference cannot be caused, and normal network transmission of a data plane is not influenced.
In some embodiments, after the determining a target tracking node based on the first retransmission information, the method further comprises:
and dynamically adjusting the information acquisition frequency of the target tracking node.
In this exemplary embodiment, the target tracking node is a node with a higher retransmission rate, that is, a node that needs to pay attention and adjust, so that the information acquisition frequency of these nodes can be increased to obtain more accurate node-related information, and thus the frame loss retransmission information of these nodes can be predicted more accurately, and the root cause of frame loss retransmission can be analyzed for the nodes.
In some embodiments, the information acquisition frequency may also be adjusted for the node with the lower frame loss retransmission rate, for example, the information acquisition frequency is reduced.
In some embodiments, the method further comprises:
firstly, acquiring historical network link information, historical network node information and corresponding historical first retransmission information of an SDN network; the historical network link information comprises historical channel state information, historical signal state information and historical link packet loss information of each node, and the historical network node information comprises historical node position information and historical node energy information.
In the present exemplary embodiment, network link information, network node information, and corresponding retransmission information of a period of time (e.g., several days, ten and several days, one month, or several months) before the current time are acquired for each node as historical network link information, historical network node information, and corresponding historical first retransmission information.
In the present example embodiment, the historical channel state information may be channel state information a period of time before the current time. The historical signal state information, the historical link packet loss information, the historical node position information and the historical node energy information are similar to the historical channel state information in definition. The historical first retransmission information may be retransmission rate information or retransmission number information of a period of time before the current time, may also be an average retransmission rate or average retransmission number of the node in the period of time, and may also be another operation result of the retransmission information in the period of time, which is not particularly limited in this example.
In this example embodiment, the channel state information may include channel quality information, the number of channels shared by the node and the neighboring node, and the like, the signal state information may include signal strength information RSSI and signal to noise ratio SINR, the link packet loss information may include packet loss ratios and link packet loss numbers of all links of the node, the network node information includes node location information and node energy information, the node location information may include GPS coordinate information of the node, distance information between the node and the neighboring node, and the like, and the node energy information may include node transmission energy, transmission energy of the neighboring node, and the like.
And then, generating a random forest model by taking the historical channel state information, the historical signal state information, the historical link packet loss information, the historical node position information and the historical node energy information of each node as a group of training samples and the corresponding historical first retransmission information as sample labels in a random sampling and splitting mode until a training termination condition is reached, and obtaining the trained random forest model.
In this exemplary embodiment, a set of information (historical channel state information, historical signal state information, historical link packet loss information, historical node location information, and historical node energy information) of each node may be used as a training sample, and the historical first retransmission information corresponding to the sample is used as a sample label to train and generate the random forest model.
For example, a training generation process of a random forest model is as follows:
(1) if N training samples are set, N samples are randomly selected and put back. And training a decision tree by adopting the selected N samples to serve as samples at the root node of the decision tree.
(2) When each sample has M attribute features, when each tree node of the decision tree needs to be split, randomly selecting M attribute features, M < < M, from the M attribute features. And then, a preset strategy (such as information gain) is adopted from the m attribute characteristics to select 1 attribute characteristic as the split attribute of the node.
(3) And (3) splitting each tree node in the decision tree forming process according to the step (2) until a training termination condition is reached. The training termination condition in this example may be the maximum number of decision trees or all tree nodes cannot be re-split.
A large number of decision trees can be generated according to the steps (1) - (3), so that a random forest model is formed.
In some embodiments, the calculating the feature importance information of the target tracking node based on the trained retransmission prediction model includes:
and determining the characteristic importance information of each target tracking node by calculating the Kini index or the out-of-bag data error rate of each target tracking node based on the trained retransmission prediction model.
For example, the importance of the feature X is evaluated by using the out-of-bag data error rate, and the calculation process is as follows: for each decision tree, selecting a corresponding out-of-bag data OOB to calculate out-of-bag data error errOOB 1; randomly adding noise interference to the characteristics X of all samples of the out-of-bag data OOB, and calculating out-of-bag data error errOOB2 again; assuming there are N trees in the forest, the importance of feature X ═ Σ (errOOB2-errOOB 1)/N.
The importance of each feature can be calculated by the same method.
In some embodiments, referring to fig. 3, the determining key features of corresponding target tracking nodes based on the feature importance information includes:
step S310, based on the size of the feature importance information, sorting the features of each target tracking node, and screening out the features positioned at the top k positions as candidate features; k is a positive number.
In this exemplary embodiment, the features may be sorted from high to low in importance based on the feature importance information, and then the features with the importance positioned k top may be screened as candidate features.
And step S320, deleting any candidate feature from the trained retransmission prediction model, and determining second retransmission information of the model after the feature is deleted.
In the present exemplary embodiment, a candidate feature is deleted from the trained retransmission prediction model, and the model output after the feature deletion, that is, the second retransmission information, is determined. And executing the operation on each candidate feature to obtain second retransmission information corresponding to the deleted candidate feature. The second retransmission information may be a predicted retransmission rate or a number of retransmissions after deleting the candidate feature.
And step S330, selecting the candidate feature corresponding to the second retransmission information when the second retransmission information is minimum as the key feature of the current target tracking node.
In this exemplary embodiment, the minimum second retransmission information indicates that the corresponding deleted feature (i.e., candidate feature) is a key factor affecting the current node. That is, if a certain feature is removed for prediction, and the frame loss retransmission rate of the model is significantly reduced, the feature is considered as the root cause of the excessively high frame loss retransmission rate.
In some embodiments, the network link information includes channel state information, signal state information, and link packet loss information of each node, and the network node information includes node location information and node energy information; generating corresponding control information based on the key features to adjust configuration information of the target tracking node, including:
when the key feature is channel state information, generating channel adjustment control information to adjust the channel allocation of the target tracking node; when the key feature is signal state information, generating signal state adjustment control information to adjust the signal state of the target tracking node; when the key feature is node position information, generating node position adjustment control information to adjust the position of the target tracking node or the neighbor node; when the key feature is node energy information, generating node energy adjustment control information to adjust the transmission energy of the target tracking node or the neighbor node; and when the key characteristic is the link packet loss information, generating path planning control information to re-plan the transmission path of the network node.
For example, if the analysis reason based on the key features is network interference, the channel is reallocated. And if the analysis reason based on the key characteristics is that the transmission energy of the node is too low or the transmission energy of the neighbor node is too high, adjusting the transmission energy of the corresponding node. And if the analysis reason based on the key characteristics is that the distance between the node and the neighbor node is too high and the distance can be adjusted in a physical movement mode, carrying out movement and position management on the corresponding node.
For example, referring to fig. 4, the SDN network dynamic control process is implemented by using the method of the present disclosure.
Step S401, the SDN controller sends a probe Packet "Packet Out" to each network device (wireless node) at preset time intervals. In this example, the preset time interval may be set to 1 second.
Step S402, a topology service module of the SDN controller collects network topology information, that is, link information and node information of each node.
In this example, the link information may include channel state information, signal state information, link packet loss information, and the like, the node information may include position information of the current node, transmission energy information of the current node, position information of a neighbor node, transmission energy information of the neighbor node, and the like, and the neighbor node may be a 1-hop neighbor node of the current node.
Step S403, the application layer obtains link information and node information of each node acquired by the SDN controller through the northbound interface.
Step S404, the retransmission optimization application of the application layer extracts the link information of each current node and the characteristic information in the node information.
In this example, the extracted feature information may include: the distance between the current node and the 1-hop neighbor, the number of the current node and the 1-hop neighbor using the same channel, the transmission energy of the 1-hop neighbor of the current node, the retransmission number of the 1-hop neighbor of the current node, the RSSI of each link of the current node, the SINR of each link of the current node, the packet loss rate of each link of the current node, and the historical average retransmission number of the current node (i.e., the average retransmission number in a period of time before the current time).
Step S405, the retransmission optimization application of the application layer inputs the extracted characteristic information into a trained retransmission prediction model, and predicts the retransmission rate of each node in the current network topology structure. In this example, the trained retransmission prediction model may be a random forest model.
Step S406, the retransmission optimization application of the application layer determines the target tracking node at the current moment based on the retransmission rate of each node. In this example, a node with a retransmission rate greater than a preset threshold may be used as a target tracking node, that is, a node with frequent network retransmission.
Step S407, calculating the characteristic importance information of the target tracking node by the retransmission optimization application of the application layer. In this example, the characteristic importance information of each target tracking node may be determined by calculating a kini index or an out-of-bag data error rate of each target tracking node.
Step S408, the retransmission optimization application of the application layer determines the key features of the corresponding target tracking nodes based on the feature importance information.
In this example, one or more features with the highest importance information may be used as the key features of the node.
Step S409, the retransmission optimization application of the application layer sends the key feature to the SDN controller through a northbound interface.
Step S410, the SDN controller generates corresponding control information based on the key feature.
Step S411, a node configuration module of the SDN controller issues the control information to a corresponding target tracking node through a southbound interface so as to adjust the configuration information.
In this example, when the key feature is channel state information, channel adjustment control information is generated to adjust channel allocation of the target tracking node; when the key feature is signal state information, generating signal state adjustment control information to adjust the signal state of the target tracking node; when the key feature is node position information, generating node position adjustment control information to adjust the position of the target tracking node or the neighbor node; when the key feature is node energy information, generating node energy adjustment control information to adjust the transmission energy of the target tracking node or the neighbor node; and when the key characteristic is the link packet loss information, generating path planning control information to re-plan the transmission path of the network node. For example, if the transmission energy of the 1-hop neighbor of the random forest of a certain current node is the feature with the highest importance, it may be determined that the physical positions of the node and its neighbor need to be adjusted.
In this example, the retransmission optimization application at the application layer may run on the same server as the SDN controller, or may run on a different server.
The method comprises the steps of utilizing an SDN controller to collect basic topology information of an SDN wireless network; analyzing a high-frequency retransmission node and a topological area by using a random forest model; for high-frequency retransmission nodes and topological regions, the information acquisition frequency is dynamically improved, so that the node frame loss retransmission information is more accurately measured, and the root cause analysis with high retransmission rate is carried out. And further generating corresponding OpenFlow control information according to the analyzed reasons, and issuing the control information to a wireless network node, so that network delay and blockage are avoided, and the service quality of the wireless SDN network is improved.
In the related art, the retransmission of the wireless multi-node network uses an automatic retransmission mechanism of a link layer, and there is no scheme for reducing retransmission at a system level. And the method is suitable for the limited visual field condition of the distributed system. The SDN network disclosed by the invention is a global view, and can realize global optimization on retransmission of a multi-node network. Since optimizing the required information requires the use of southbound interfaces to gather, which consumes bandwidth, the present disclosure employs selective tracking of nodes with high retransmission rates. Meanwhile, the SDN may directly perform node configuration information adjustment to reduce retransmissions, such as channel reallocation, transmission capability adjustment, route recalculation, and the like.
The method analyzes the collected topological information by using the random forest model, determines the nodes and the regional topology of the network retransmission frequently occurring in the current network topological structure, further selectively tracks the predicted high-frequency retransmission nodes and topological regions, dynamically improves the information collection frequency, and increases the node regulation and control accuracy.
Further, in the present exemplary embodiment, an SDN network dynamic control apparatus 500 is further provided, where the SDN network dynamic control apparatus 500 may be used in a server. Referring to fig. 6, the SDN network dynamic control apparatus 500 may include:
the collecting module 510 may be configured to collect network link information and network node information of the SDN network in real time.
A tracking node determining module 520, configured to input the network link information and the network node information into a trained retransmission prediction model to obtain first retransmission information of each node in the SDN network; determining a target tracking node based on the first retransmission information; the retransmission prediction model comprises a random forest model.
A calculating module 530, configured to calculate feature importance information of the target tracking node based on the trained retransmission prediction model; and determining key features of the corresponding target tracking nodes based on the feature importance information.
The control module 540 may be configured to generate corresponding control information based on the key features, and issue the control information to a corresponding target tracking node to adjust configuration information of the target tracking node.
In an exemplary embodiment of the present disclosure, the apparatus 500 further comprises:
the apparatus may further include a packet sending module, configured to send a probe packet to each node in the SDN network at a preset time interval.
In an exemplary embodiment of the present disclosure, the apparatus 500 further comprises:
and the acquisition frequency adjusting module can be used for dynamically adjusting the information acquisition frequency of the target tracking node.
In an exemplary embodiment of the present disclosure, the apparatus 500 further comprises:
the history information acquisition module can be used for acquiring history network link information, history network node information and corresponding history first retransmission information of the SDN network; the historical network link information comprises historical channel state information, historical signal state information and historical link packet loss information of each node, and the historical network node information comprises historical node position information and historical node energy information.
The training module can be used for generating a random forest model by taking the historical channel state information, the historical signal state information, the historical link packet loss information, the historical node position information and the historical node energy information of each node as a group of training samples and taking corresponding historical first retransmission information as sample labels in a random sampling and splitting mode until a training termination condition is reached, and obtaining the trained random forest model.
In an exemplary embodiment of the disclosure, the calculation module 530 is further configured to:
and determining the characteristic importance information of each target tracking node by calculating the Kini index or the out-of-bag data error rate of each target tracking node based on the trained retransmission prediction model.
In an exemplary embodiment of the present disclosure, the calculation module 530 includes:
the screening module can be used for sorting the characteristics of each target tracking node based on the size of the characteristic importance information and screening out the characteristics positioned at the top k positions as candidate characteristics; k is a positive number.
And the deletion determining module can be used for deleting any candidate feature from the trained retransmission prediction model and determining second retransmission information of the model after the feature is deleted.
And the selecting module may be configured to select a candidate feature corresponding to the second retransmission information when the second retransmission information is minimum, as a key feature of the current target tracking node.
In an exemplary embodiment of the disclosure, the control module 540 may be further configured to:
when the key feature is channel state information, generating channel adjustment control information to adjust the channel allocation of the target tracking node; when the key feature is signal state information, generating signal state adjustment control information to adjust the signal state of the target tracking node; when the key feature is node position information, generating node position adjustment control information to adjust the position of the target tracking node or the neighbor node; when the key feature is node energy information, generating node energy adjustment control information to adjust the transmission energy of the target tracking node or the neighbor node; and when the key characteristic is the link packet loss information, generating path planning control information to re-plan the transmission path of the network node.
The specific details of each module or unit in the SDN network dynamic control device have been described in detail in the corresponding SDN network dynamic control method, and therefore are not described herein again.
As another aspect, the present application also provides a computer-readable medium, which may be contained in the electronic device described in the above embodiments; or may exist separately without being assembled into the electronic device. The computer readable medium carries one or more programs which, when executed by an electronic device, cause the electronic device to implement the method as described in the embodiments below. For example, the electronic device may implement the steps shown in fig. 2 to 4, and the like.
It should be noted that the computer readable media shown in the present disclosure may be computer readable signal media or computer readable storage media or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present disclosure, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In contrast, in the present disclosure, a computer-readable signal medium may include a propagated data signal with computer-readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, fiber optic cable, RF, etc., or any suitable combination of the foregoing.
In addition, in an exemplary embodiment of the present disclosure, an electronic device capable of implementing the above method is also provided. As will be appreciated by one skilled in the art, aspects of the present disclosure may be embodied as a system, method or program product. Accordingly, various aspects of the present disclosure may be embodied in the form of: an entirely hardware embodiment, an entirely software embodiment (including firmware, microcode, etc.) or an embodiment combining hardware and software aspects that may all generally be referred to herein as a "circuit," module "or" system.
An electronic device 600 according to such an embodiment of the present disclosure is described below with reference to fig. 6. The electronic device 600 shown in fig. 6 is only an example and should not bring any limitations to the function and scope of use of the embodiments of the present disclosure.
As shown in fig. 6, the electronic device 600 is embodied in the form of a general purpose computing device. The components of the electronic device 600 may include, but are not limited to: the at least one processing unit 610, the at least one memory unit 620, a bus 630 connecting different system components (including the memory unit 620 and the processing unit 610), and a display unit 640.
Wherein the storage unit stores program code that is executable by the processing unit 610 to cause the processing unit 610 to perform steps according to various exemplary embodiments of the present disclosure as described in the above section "exemplary methods" of this specification.
The storage unit 620 may include readable media in the form of volatile memory units, such as a random access memory unit (RAM)6201 and/or a cache memory unit 6202, and may further include a read-only memory unit (ROM) 6203.
The memory unit 620 may also include a program/utility 6204 having a set (at least one) of program modules 6205, such program modules 6205 including, but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which, or some combination thereof, may comprise an implementation of a network environment.
The electronic device 600 may also communicate with one or more external devices 700 (e.g., keyboard, pointing device, bluetooth device, etc.), with one or more devices that enable a user to interact with the electronic device 600, and/or with any devices (e.g., router, modem, etc.) that enable the electronic device 600 to communicate with one or more other computing devices. Such communication may occur via an input/output (I/O) interface 650. Also, the electronic device 600 may communicate with one or more networks (e.g., a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public network such as the Internet) via the network adapter 660. As shown, the network adapter 660 communicates with the other modules of the electronic device 600 over the bus 630. It should be appreciated that although not shown in the figures, other hardware and/or software modules may be used in conjunction with the electronic device 600, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RA identification systems, tape drives, and data backup storage systems, etc.
Through the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein may be implemented by software, or by software in combination with necessary hardware. Therefore, the technical solution according to the embodiments of the present disclosure may be embodied in the form of a software product, which may be stored in a non-volatile storage medium (which may be a CD-ROM, a usb disk, a removable hard disk, etc.) or on a network, and includes several instructions to enable a computing device (which may be a personal computer, a server, a terminal device, or a network device, etc.) to execute the method according to the embodiments of the present disclosure.
Furthermore, the above-described figures are merely schematic illustrations of processes included in methods according to exemplary embodiments of the present disclosure, and are not intended to be limiting. It will be readily understood that the processes shown in the above figures are not intended to indicate or limit the chronological order of the processes. In addition, it is also readily understood that these processes may be performed synchronously or asynchronously, e.g., in multiple modules.
It should be noted that although the various steps of the methods of the present disclosure are depicted in the drawings in a particular order, this does not require or imply that these steps must be performed in this particular order, or that all of the depicted steps must be performed, to achieve desirable results. Additionally or alternatively, certain steps may be omitted, multiple steps combined into one step execution, and/or one step broken down into multiple step executions, etc., are all considered part of this disclosure.
It should be understood that the disclosure disclosed and defined in this specification extends to all alternative combinations of two or more of the individual features mentioned or evident from the text and/or drawings. All of these different combinations constitute various alternative aspects of the present disclosure. The embodiments of this specification illustrate the best mode known for carrying out the disclosure and will enable those skilled in the art to utilize the disclosure.
Claims (10)
1. An SDN network dynamic control method is characterized by comprising the following steps:
acquiring network link information and network node information of an SDN network in real time;
inputting the network link information and the network node information into a trained retransmission prediction model to obtain first retransmission information of each node in the SDN network; determining a target tracking node based on the first retransmission information; the retransmission prediction model comprises a random forest model;
calculating characteristic importance information of the target tracking node based on the trained retransmission prediction model; determining key features of corresponding target tracking nodes based on the feature importance information;
and generating corresponding control information based on the key characteristics, and issuing the control information to the corresponding target tracking node to adjust the configuration information of the target tracking node.
2. The SDN network dynamic control method of claim 1, wherein prior to the collecting network link information and network node information of the SDN network in real-time, the method further comprises:
sending a probing data packet to each node in the SDN network at a preset time interval.
3. The SDN network dynamic control method of claim 1, wherein after the determining a target tracking node based on the first retransmission information, the method further comprises:
and dynamically adjusting the information acquisition frequency of the target tracking node.
4. The SDN network dynamic control method of claim 1, further comprising:
acquiring historical network link information, historical network node information and corresponding historical first retransmission information of the SDN network; the historical network link information comprises historical channel state information, historical signal state information and historical link packet loss information of each node, and the historical network node information comprises historical node position information and historical node energy information;
and generating a random forest model by using a random sampling and splitting mode by taking the historical channel state information, the historical signal state information, the historical link packet loss information, the historical node position information and the historical node energy information of each node as a group of training samples and the corresponding historical first retransmission information as sample labels until a training termination condition is reached, and obtaining the trained random forest model.
5. The SDN network dynamic control method of claim 1, wherein the calculating feature importance information of the target tracking node based on the trained retransmission prediction model comprises:
and determining the characteristic importance information of each target tracking node by calculating the Kini index or the out-of-bag data error rate of each target tracking node based on the trained retransmission prediction model.
6. The SDN network dynamic control method of any one of claims 1-5, wherein the determining key features of corresponding target tracking nodes based on the feature importance information comprises:
based on the size of the feature importance information, sorting the features of each target tracking node, and screening out the features positioned at the top k positions as candidate features; k is a positive number;
deleting any candidate feature from the trained retransmission prediction model, and determining second retransmission information of the model after the feature is deleted;
and selecting the candidate feature corresponding to the second retransmission information when the second retransmission information is minimum as the key feature of the current target tracking node.
7. The SDN network dynamic control method of claim 1, wherein the network link information includes channel state information, signal state information, and link packet loss information of each node, and the network node information includes node location information and node energy information; generating corresponding control information based on the key features to adjust configuration information of the target tracking node, including:
when the key feature is channel state information, generating channel adjustment control information to adjust the channel allocation of the target tracking node;
when the key feature is signal state information, generating signal state adjustment control information to adjust the signal state of the target tracking node;
when the key feature is node position information, generating node position adjustment control information to adjust the position of the target tracking node or the neighbor node;
when the key feature is node energy information, generating node energy adjustment control information to adjust the transmission energy of the target tracking node or the neighbor node;
and when the key characteristic is the link packet loss information, generating path planning control information to re-plan the transmission path of the network node.
8. An SDN network dynamic control apparatus, comprising:
the system comprises an acquisition module, a processing module and a processing module, wherein the acquisition module is used for acquiring network link information and network node information of the SDN in real time;
a tracking node determination module, configured to input the network link information and the network node information into a trained retransmission prediction model to obtain first retransmission information of each node in the SDN network; determining a target tracking node based on the first retransmission information; the retransmission prediction model comprises a random forest model;
the calculation module is used for calculating the characteristic importance information of the target tracking node based on the trained retransmission prediction model; determining key features of corresponding target tracking nodes based on the feature importance information;
and the control module is used for generating corresponding control information based on the key characteristics and sending the control information to the corresponding target tracking node so as to adjust the configuration information of the target tracking node.
9. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the method according to any one of claims 1-7.
10. An electronic device, comprising:
one or more processors;
storage means for storing one or more programs which, when executed by the one or more processors, cause the one or more processors to carry out the method of any one of claims 1-7.
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