CN115209431B - Triggering method, device, equipment and computer storage medium - Google Patents
Triggering method, device, equipment and computer storage medium Download PDFInfo
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Abstract
The invention discloses a triggering method, which comprises the following steps: and acquiring a slice instance of the network slice, inputting the slice instance into a trained Node2Vec model, outputting a vector of a Node in the acquired slice instance, and determining whether to trigger reconstruction of the slice instance according to the vector of the Node. The embodiment of the invention also discloses a triggering device, equipment and a computer storage medium, which improve the decision efficiency of whether the network slice needs to be reconstructed or not, and further improve the reconstruction efficiency of the network slice.
Description
Technical Field
The present invention relates to a reconstruction triggering technique of a network slice, and in particular, to a triggering method, apparatus, device and computer storage medium.
Background
Currently, network slicing is an important content in 5G construction, and the technology meets the differentiated requirements of the vertical industry on network services by constructing a plurality of logically independent proprietary networks on the same physical base platform. Different from the traditional single network management mode, the network slicing technology provides a larger selection space for personalized demand customization, and provides a more convenient, efficient, safe and low-cost operation and maintenance scheme for operators to bear diversified network services.
The third generation partnership project (3GPP,3rd Generation Partnership Project) specifies four requisite phases of the slice lifecycle, the preparation phase, the instantiation, the configuration, the activation phase, the runtime phase, and the offline phase, respectively. In the preparation stage, all types of requirements of the services to be carried by the network need to be definitely carried, and different slicing templates are customized according to different types of requirements. In the stage of instantiation, configuration and activation, the service requirement is required to be converted into the network performance requirement, a corresponding slicing template is selected, the association of virtual topology and physical bearing topology is realized, corresponding network resources are configured, and the instantiation of slicing based on the template is realized. In the runtime phase, traffic needs to be carried onto the instantiated slice. In the offline stage, it is necessary to delete the already serviced instantiation slice and reclaim the relevant underlying resources.
In the operation period, the intelligent network operation and maintenance method can be used for monitoring the slicing condition, reconstructing the instantiated slicing according to the change of the service requirement, and realizing the self-adaption of the slicing structure and the resource facing the change of the requirement, thereby increasing the flexibility of the slicing and improving the service quality of the network.
In the related art, the reconstruction of the network slice is usually triggered by identifying and calculating the network slice based on a matrix, however, the method has larger calculation amount, so that the triggering method has higher complexity, and the reconstruction triggering efficiency of the network slice is poor; therefore, the existing reconstruction triggering method of the network slice has the technical problem of low efficiency.
Disclosure of Invention
In view of the above, the present invention provides a triggering method, device, apparatus and computer storage medium, so as to solve the technical problem of low efficiency of the network slice reconstruction triggering method in the prior art.
The technical scheme of the invention is realized as follows:
in a first aspect, an embodiment of the present invention provides a triggering method, where the method includes:
acquiring a slice instance of a network slice;
inputting the slice instance into a trained Node2Vec model, and outputting a vector of a Node in the slice instance; wherein the vector elements of the node include: an amount representing the structure of the node and an amount representing the load of the node;
and determining whether to trigger reconstruction of the slice instance according to the vector of the node.
In the method, the trained Node2Vec model is obtained by adopting the following modes:
acquiring a starting node and a next-hop node of the starting node from a slice example to be trained, determining the next-hop node as a current node, and setting an initial value of sampling times i to be 1;
acquiring adjacent nodes of the current node;
when i is smaller than the sampling step length, calculating the offset migration probability of the current node and the adjacent node according to a preset offset migration probability algorithm; the preset bias migration probability is positively correlated with network performance parameters of the current service when the slice instance is adopted at the current moment;
sequencing the calculated offset walk probability according to the order from large to small, selecting the adjacent nodes ranked in the previous M, determining the selected adjacent nodes as current nodes, updating i to be i+1, and returning to execute the adjacent nodes for acquiring the current nodes; wherein M is a positive integer less than the number of adjacent nodes;
when i is greater than or equal to the sampling step length, inputting at least two groups of Node sequences obtained by sampling into a preset Node2Vec model for training, and obtaining the trained Node2Vec model.
In the above method, the network performance parameter of the current service includes one or more of the following:
The average occupied bandwidth of the current service, the average message delay of the current service and the average packet loss rate of the current service. .
In the method, the current node to the adjacent node is calculated by adopting the following formulaBias walk probability pi of point vx ：
π vx ＝α pq (t,x)·w vx
Wherein v represents the current node, t represents the neighboring node of the current node, W 0 Representing the average occupied bandwidth of the current service at the current moment, T 0 Average message delay representing current business at current moment E 0 Represents the average packet loss rate, k of the current service at the current moment W Is W 0 Is (k) the estimated level of (k) T Is T 0 Is (k) the estimated level of (k) E Is E 0 Is the estimated level of d tx Representing the shortest number of hops that node t needs to jump to node x.
In the above method, the determining whether to trigger the reconstruction of the slice instance according to the vector of the node includes:
according to the vector of the nodes, calculating the Euclidean distance between any two nodes in the slice example;
and triggering the reconstruction of the slice instance when the Euclidean distance between any two nodes meets a preset condition.
In the above method, when the euclidean distance between any two nodes meets a preset condition, triggering the reconstruction of the slice instance includes:
And triggering the reconstruction of the slice instance when the Euclidean distance between any two nodes is smaller than the preset minimum Euclidean distance between the nodes.
In the above method, when the euclidean distance between any two nodes meets a preset condition, triggering the reconstruction of the slice instance includes:
and triggering the reconstruction of the slice instance when the Euclidean distance between any two nodes is larger than the preset maximum Euclidean distance between the nodes.
In the above method, the method further comprises:
and when no distance smaller than the preset minimum Euclidean distance between the nodes exists in the Euclidean distance between any two nodes, and no distance larger than the preset maximum Euclidean distance between the nodes exists in the Euclidean distance between any two nodes, triggering the reconstruction of the slice instance is forbidden.
In a second aspect, the present invention provides a triggering device, the device comprising:
the acquisition module is used for acquiring a slice instance of the network slice;
the processing module is used for inputting the slice instance into a trained Node2Vec model and outputting a vector of a Node in the slice instance; wherein the vector elements of the node include: an amount representing the structure of the node and an amount representing the load of the node;
And the triggering module is used for determining whether to trigger the reconstruction of the slice instance according to the vector of the node.
In the above apparatus, the apparatus is further configured to:
the trained Node2Vec model is obtained by the following method:
acquiring a starting node and a next-hop node of the starting node from a slice example to be trained, determining the next-hop node as a current node, and setting an initial value of sampling times i to be 1;
acquiring adjacent nodes of the current node;
when i is smaller than the sampling step length, calculating the offset migration probability of the current node and the adjacent node according to a preset offset migration probability algorithm; the preset bias migration probability is positively correlated with network performance parameters of the current service when the slice instance is adopted at the current moment;
sequencing the calculated offset walk probability according to the order from large to small, selecting the adjacent nodes ranked in the previous M, determining the selected adjacent nodes as current nodes, updating i to be i+1, and returning to execute the adjacent nodes for acquiring the current nodes; wherein M is a positive integer less than the number of adjacent nodes;
when i is greater than or equal to the sampling step length, inputting at least two groups of Node sequences obtained by sampling into a preset Node2Vec model for training, and obtaining the trained Node2Vec model.
In the above apparatus, the network performance parameters of the current service include one or more of the following:
the average occupied bandwidth of the current service, the average message delay of the current service and the average packet loss rate of the current service.
In the above apparatus, the apparatus is further configured to: calculating the offset migration probability pi from the current node to the adjacent node by adopting the following formula vx ：
π vx ＝α pq (t,x)·w vx
Wherein v represents the current node, t represents the neighboring node of the current node, W 0 Representing the average occupied bandwidth of the current service at the current moment, T 0 Average message delay representing current business at current moment E 0 Represents the average packet loss rate, k of the current service at the current moment W Is W 0 Is (k) the estimated level of (k) T Is T 0 Is (k) the estimated level of (k) E Is E 0 Is the estimated level of d tx Representing the shortest number of hops that node t needs to jump to node x.
In the above device, the triggering module is specifically configured to:
according to the vector of the nodes, calculating the Euclidean distance between any two nodes in the slice example;
and triggering the reconstruction of the slice instance when the Euclidean distance between any two nodes meets a preset condition.
In the above apparatus, when the euclidean distance between any two nodes satisfies a preset condition, the triggering module triggers the reconstruction of the slice instance, including:
And triggering the reconstruction of the slice instance when the Euclidean distance between any two nodes is smaller than the preset minimum Euclidean distance between the nodes.
In the above apparatus, when the euclidean distance between any two nodes satisfies a preset condition, the triggering module triggers the reconstruction of the slice instance, including:
and triggering the reconstruction of the slice instance when the Euclidean distance between any two nodes is larger than the preset maximum Euclidean distance between the nodes.
In the above apparatus, the apparatus is further configured to:
and when no distance smaller than the preset minimum Euclidean distance between the nodes exists in the Euclidean distance between any two nodes, and no distance larger than the preset maximum Euclidean distance between the nodes exists in the Euclidean distance between any two nodes, triggering the reconstruction of the slice instance is forbidden.
In a third aspect, an embodiment of the present invention further provides an apparatus, including: a processor and a storage medium storing instructions executable by the processor, the storage medium performing operations in dependence upon the processor through a communication bus, the instructions, when executed by the processor, performing the triggering method of one or more embodiments described above.
Embodiments of the present invention provide a computer storage medium storing executable instructions that, when executed by one or more processors, perform the triggering method of one or more of the embodiments described above.
The invention provides a triggering method, a device, equipment and a computer storage medium, wherein the method comprises the following steps: obtaining a slice instance of a network slice, inputting the slice instance into a trained Node2Vec model, and outputting a vector of a Node in the obtained slice instance, wherein vector elements of the Node comprise: determining to trigger the reconstruction of the slice instance according to the vector of the node; that is, in the present invention, after a slice instance of a network slice is obtained, the slice instance is input into a trained Node2Vec model, so that a vector of a Node of the slice instance can be obtained, and since the obtained vector includes an amount for representing a Node structure and a Node load, and the Node-based vector can reflect the structure of the slice and the load condition of the slice, in determining whether to trigger the reconstruction of the slice instance according to the vector of the Node, the complexity of determining whether to trigger the reconstruction of the slice instance is reduced by using the vector of the Node with two dimensions, thereby improving the decision efficiency of whether to reconstruct the network slice, and further improving the reconstruction efficiency of the network slice.
Drawings
FIG. 1 is a flow chart of an alternative triggering method according to an embodiment of the present invention;
FIG. 2 is a network topology of an example of a slice labeled with bias weights in the related art;
FIG. 3 is a flowchart illustrating an example of an alternative triggering method according to an embodiment of the present invention;
FIG. 4 is a schematic diagram of an alternative trigger device according to an embodiment of the present invention;
fig. 5 is a schematic structural diagram of an alternative device according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention.
Example 1
An embodiment of the present invention provides a triggering method, and fig. 1 is a schematic flow diagram of an optional triggering in the embodiment of the present invention, as shown in fig. 1, where the triggering method may include:
s101: acquiring a slice instance of a network slice;
at present, for network slicing, after slicing service begins, a slicing template matched with current service requirements is selected from a slicing template library, then a slicing instance is created according to the selected slicing template, then an online slicing instance bears the service requirements, after the slicing instance is online, the slicing instance is represented based on a matrix, and whether to trigger reconstruction of the slicing instance is determined; then, whether to trigger the reconstruction of the slice instance is determined based on the matrix, and the dimension of the matrix is higher, so that the matrix is adopted to represent the slice information, so that the computational complexity in determining whether to trigger the reconstruction of the slice instance is increased, and the cost of monitoring the slice instance is increased.
In order to reduce the complexity of determining whether to trigger the reconstruction of a slice instance to reduce the cost of monitoring the slice instance, embodiments of the present invention provide a triggering method for determining whether to trigger the reconstruction method of a slice instance, first, a slice instance of a network slice is acquired.
It should be noted that, after the trigger method provided by the embodiment of the present invention is deployed after the slice instance is online, that is, in the above-mentioned runtime stage, that is, after the start of the slice service, the slice template matching with the current service requirement is selected from the slice template library, then the slice instance is created according to the selected slice template, then the online slice instance carries the service requirement, and after the slice instance is online, the slice instance is obtained.
The current service of the slice example may be a shopping application program, a mobile phone service, or a communication application program, which is not limited in particular in the embodiment of the present invention.
S102: inputting the slice instance into a trained Node2Vec model, and outputting a vector of a Node in the slice instance;
in the step of obtaining the slice example, a topological graph of the slice example is obtained, wherein the topological graph of the slice example comprises nodes in the slice example and connection relations between the nodes, and then the slice example is input into a trained Node2Vec model, so that vectors of the nodes in the slice example can be output.
The trained Node2Vec model is obtained by sampling to obtain sample data, and then training the Node2Vec model by using the sample data, wherein the Node2Vec model is a model for generating Node vectors in a network, the input of the model is a network structure, and the output of the model is the vector of each Node.
Here, by introducing the processing of the slicing problem into the Node2Vec model, the slicing information is reduced from the N dimension to the 2 dimension, so that the space complexity of slice information representation and storage is effectively reduced, the management and monitoring cost of slicing is reduced, and the network operation and maintenance benefits are improved.
Wherein the vector elements of the node include: an amount representing the structure of the node and an amount representing the load of the node; that is, the obtained node vector can reflect the structure of the node and the load condition of the node, so that whether to trigger the reconstruction of the slice instance is determined according to the node vector, mainly according to the structure of the node and the load condition of the node, and the obtained node vector is favorable for accurately determining whether to trigger the reconstruction of the slice instance by combining the structure of the node and the current load condition under the condition of ensuring lower calculation amount, thereby increasing the accuracy of decision.
Further, in order to obtain a trained Node2Vec model, in an alternative embodiment, the trained Node2Vec model is obtained by:
acquiring a starting node and a next-hop node of the starting node from a slice example to be trained, determining the next-hop node as a current node, and setting an initial value of sampling times i as 1;
acquiring adjacent nodes of the current node;
when i is smaller than the sampling step length, calculating the offset migration probability of the current node and the adjacent node according to a preset offset migration probability algorithm;
sequencing the calculated offset walk probability from large to small, selecting the adjacent nodes ranked in the previous M, determining the selected adjacent nodes as current nodes, updating i to be i+1, and returning to execute the adjacent nodes for acquiring the current nodes;
when i is greater than or equal to the sampling step length, at least two groups of Node sequences obtained by sampling are input into a preset Node2Vec model for training, and a trained Node2Vec model is obtained.
Specifically, the number of slice instances to be trained may be one or more, and embodiments of the present invention are not limited in this regard.
In order to obtain sample data for model training, sampling is performed in the following manner: for the slicing example to be trained, a starting node is randomly determined, then the next-hop node of the starting node is randomly determined from adjacent nodes of the starting node, and the next-hop node is determined as the current node, and because the sampling step length is preset, the initial value of the sampling frequency i is set to be 1 for sampling according to the sampling step length.
And then acquiring adjacent nodes of the current node, judging the relation between i and the sampling step length, and calculating the offset migration probability of the current node and the adjacent nodes according to a preset offset migration probability algorithm when i is smaller than the sampling step length.
FIG. 2 is a network topology diagram of a related art example of slicing labeled with bias weights, as shown in FIG. 2, t represents a start Node, v represents a next-hop Node of the start Node, t, x1, x2 and x3 represent neighboring nodes of the v Node, and in the related art, the walk-in manner in the Node2Vec model calculates the bias walk probability α using the following formula pq (t,x)：
Wherein, in connection with fig. 2, parameters p and q are used to control the tendency of the sampling process to breadth first search (BFS, breadth First Search) and depth first search (DFS, depth First Search), respectively, p is the return parameter, and the larger p is, the same node is obtained in the sampling process Q is the entry and exit parameter the smaller the probability of (1), if q>1, the sampling process will favor BFS, if q<1, the sampling process is prone to DFS, d tx Representing the shortest number of hops that node t needs to jump to node v.
In the embodiment of the present invention, in order to implement triggering of slice instance reconstruction, a preset offset migration probability is provided herein, where the preset offset migration probability is positively related to a network performance parameter of a current service when a slice instance is adopted at a current time, that is, a value of the offset migration probability increases with an increase of the network performance parameter of the current service of the slice instance, and decreases with a decrease of the network performance parameter of the current service of the slice instance, so that the offset migration probability is related to the network performance parameter of the current service.
The calculated bias migration probability is ranked according to the size from large to small, and the adjacent nodes ranked in the front M are selected as current nodes, wherein M is a positive integer smaller than the number of the adjacent nodes; therefore, the sampled training sample is a Node sequence with better network performance parameters of the current service, and the quality of the training sample is improved, so that a Node2Vec model meeting the requirements can be trained.
After the current node is updated by selecting the adjacent nodes, i is updated to i+1, the next sampling is carried out, and the adjacent nodes of the current node are returned to be acquired.
In addition, when i is greater than or equal to the sampling step length, the sampling is completed, and then at least two groups of Node sequences which jump in the sampling are used as training samples and input into a preset Node2Vec model for training, so that a trained Node2Vec model is obtained.
In the embodiment of the invention, the bias parameter w is introduced in the sampling stage of the trained Node2Vec model vx The node sequence sample obtained by sampling can train a mapping model fusing the slice structure and the load state, the fusion mapping of the slice structure and the load information is completed, and the reconstruction decision of multi-factor guidance can be realized.
To more accurately determine whether to trigger reconstruction of a slice instance, in an alternative embodiment, network performance parameters of the current service include one or more of:
the average occupied bandwidth of the current service, the average message delay of the current service and the average packet loss rate of the current service.
In particular, the network performance parameter of the current service may include one or more of an average occupied bandwidth of the current service, an average message delay of the current service, and an average packet loss rate of the current service, which is not particularly limited herein by the embodiment of the present invention.
Further, to determine whether to trigger reconstruction of the slice instance more accurately, the network performance parameters of the current service may include an average occupied bandwidth of the current service, an average message delay of the current service, and an average packet loss rate of the current service, in an alternative embodiment, the offset migration probability pi from the current node to the neighboring node is calculated using the following formula vx ：
π vx ＝α pq (t,x)·w vx (2)
Wherein v represents the current node, t represents the neighboring node of the current node, W 0 Representing the average occupied bandwidth of the current service at the current moment, T 0 Average message delay representing current business at current moment E 0 Represents the average packet loss rate, k of the current service at the current moment W Is W 0 Is (k) the estimated level of (k) T Is T 0 Is (k) the estimated level of (k) E Is E 0 Is a function of the estimated rank of (2).
Wherein, in practical application, k is as described above W ，k T And k E The sum is 1, and the importance degree of occupied bandwidth, message delay and packet loss rate in sampling decision can be adjusted by adjusting the three estimated level parameters.
Here, on the basis of the original offset walk probability using the above formula (1), the formula (3) is introducedI.e. multiplying w by the original bias walk probability vx So that pi obtained by adopting a preset offset walk probability algorithm vx The structure of the nodes and the load condition of the nodes are integrated, so that the obtained bias migration probability considers the network performance of the current service, the selected adjacent nodes are network nodes with better network performance, the quality of training samples is improved, and more accurate Node2Vec models are trained.
S103: based on the vector of nodes, it is determined whether to trigger reconstruction of the slice instance.
After determining the node vector of the slice instance, to determine whether to trigger the reconstruction of the slice instance, whether to trigger the reconstruction of the slice instance may be determined according to the vector of the node, for example, whether to trigger the reconstruction of the slice instance may be determined by using one component in the vector of the node, whether to trigger the reconstruction of the slice instance may be determined by using another component in the vector of the node, whether to trigger the reconstruction of the slice instance may be determined by using two components of the vector of the node, and embodiments of the present invention are not limited in this regard.
To determine whether to trigger reconstruction of the slice instance, in an alternative embodiment, S102 may include:
According to the vector of the nodes, calculating the Euclidean distance between any two nodes in the slice example;
and triggering the reconstruction of the slice instance when the Euclidean distance between any two nodes meets the preset condition.
Specifically, according to the vector of the node, the Euclidean distance between any two nodes in the slice example is calculated by using a distance formula between the two points, then whether the Euclidean distance between any two nodes meets a preset condition is judged, the reconstruction of the slice example is triggered only when the preset condition is met, and the reconstruction of the slice example is forbidden to be triggered when the preset condition is not met.
Further, in order to trigger the reconstruction of the slice instance, in an alternative embodiment, when the euclidean distance between any two nodes satisfies a preset condition, the triggering the reconstruction of the slice instance includes:
and triggering the reconstruction of the slice instance when the Euclidean distance between any two nodes is smaller than the preset minimum Euclidean distance between the nodes.
Specifically, a preset minimum euclidean distance between nodes is preset in the triggering method, the minimum euclidean distance represents an acceptable minimum vector distance in a vector space, the practical significance of the minimum euclidean distance is that the load upper limit borne by the corresponding two nodes in the current slice example, and when the distance between any two nodes is smaller than the preset minimum euclidean distance between the nodes, the load between the two nodes in the slice example is lower than the load upper limit borne by the corresponding two nodes in the current slice example, so that the current slice example can not guarantee the stable operation of the current service, and the reconstruction of the slice example is triggered.
Further, in order to trigger the reconstruction of the slice instance, in an alternative embodiment, when the euclidean distance between any two nodes satisfies a preset condition, the triggering the reconstruction of the slice instance includes:
and triggering the reconstruction of the slice instance when the Euclidean distance between any two nodes is larger than the preset maximum Euclidean distance between the nodes.
Specifically, a preset maximum euclidean distance between nodes is preset in the triggering method, the maximum euclidean distance represents an acceptable maximum vector distance in a vector space, the practical significance of the triggering method is that the lower limit of the acceptable resource occupancy rate between two corresponding nodes in the current slice example is reached, and when the euclidean distance between any two nodes is larger than the preset maximum euclidean distance between the nodes, the fact that the resource occupancy rate between the two nodes in the slice example is larger than the lower limit of the acceptable resource occupancy rate between the two corresponding nodes in the current slice example is shown, and therefore, the current slice example cannot guarantee the stable operation of the current service is triggered.
To achieve accurate triggering of the reconstruction of a slice instance, in an alternative embodiment, the method further comprises:
And when no distance smaller than the preset minimum Euclidean distance between the nodes exists in the Euclidean distance between any two nodes, and no distance larger than the preset maximum Euclidean distance between the nodes exists in the Euclidean distance between any two nodes, triggering to reconstruct the slice instance is forbidden.
When the Euclidean distance between any two nodes is not smaller than the preset minimum Euclidean distance between the nodes, the fact that the load between the two nodes in the slice example is not lower than the upper limit of the load which can be borne by the corresponding two nodes in the current slice example is indicated, when the Euclidean distance between any two nodes is not greater than the preset maximum Euclidean distance between the nodes, the fact that the resource occupancy rate between the two nodes in the slice example is not greater than the lower limit of the acceptable resource occupancy rate between the corresponding two nodes in the current slice example is indicated, and therefore, the current slice example can ensure the stable operation of the current service, and therefore the reconstruction of the slice example is forbidden to be triggered.
The triggering method described in one or more embodiments above is described below by way of example.
Fig. 3 is a schematic flow chart of an example of an alternative triggering method provided by the embodiment of the present invention, as shown in fig. 3, the slice reconstruction triggering method of the present example is deployed after the slice example is online and operated, and before the slice example is offline, the evolution from a static slice to an elastic slice under the standard slice lifecycle is realized; the triggering method can comprise the following steps:
S301: after the slicing service is started, a slicing template is selected from a slicing template library; wherein the selected slicing template is matched with the current business requirement;
s302: creating a slice instance according to the selected slice template;
s303: the slice instance runs online, carrying the business requirements.
Specifically, after the slice instance is online, the slice reconstruction triggering strategy proposed by the invention is started.
S304: judging whether slicing service is finished; if yes, executing S305, if no, executing S306;
s305: cutting the slice example off line; s311 is performed;
s306: sampling by adopting an S strategy, namely adopting a slice example to be trained to obtain a training sample;
s307: training the Node2Vec model by using the training sample to obtain a trained Node2Vec model;
s308: inputting the slice instance into a trained Node2Vec model, and mapping the slice instance into a vector space to obtain a vector of a Node in the slice instance;
specifically, the slice reconstruction triggering strategy is to firstly sample a current slice based on an S sampling strategy (equivalent to the sampling manner described in one or more embodiments above), train a Node2Vec model by using the collected samples, and then input the slice into the trained model to obtain a mapping result of the slice structure and the load state in a low-dimensional vector space.
It should be noted that the Node2Vec model is trained using the training data obtained by the S strategy, and the model can map the slice and the slice load from the high-dimensional information space to the two-dimensional vector space, and each virtual Node obtains its corresponding two-dimensional vector representation. Because the offset walk strategy sampled by the Node2Vec fuses the information of the virtual link load state, the Euclidean distance between vectors can represent not only the connection similarity of the topological structure, but also the load state between two virtual nodes.
S309: calculating Euclidean distance between nodes, and judging whether the Euclidean distance has a distance exceeding a threshold value; if yes, executing S310, if not, executing S304;
s310: reconstructing slices, namely triggering the reconstruction of slice instances;
s311: and (5) recycling resources and ending the service.
Specifically, a threshold check is started after the mapping is completed to see if there are vector pairs that are outside the threshold range. If the current slice exists, reconstructing the current slice, replacing the current running slice with the reconstructed slice, and continuously executing the monitoring of the reconstruction triggering strategy on the reconstructed slice; if not, the monitoring of the reconstruction trigger strategy is continued for the current slice. And if the slicing service is finished, the slicing instance is disconnected, all resources allocated to the slicing are recovered, and the service is finished.
For threshold checking, in particular, if the existence of a virtual link connection between two nodes or the heavy traffic load between them may cause the euclidean distance of their corresponding vector pairs in vector space to become closer, based on this changing relationship, the slice state monitoring algorithm of this example will set up two thresholds, threshold T min Representing the minimum acceptable vector distance in the vector space, the practical meaning is that the upper limit of the load which can be borne between the corresponding two nodes in the current slice example is detected that the minimum acceptable vector distance in the vector space is less than T min When euclidean distance of (c) will trigger slice reconstruction. Threshold T max Representing the maximum acceptable vector distance in the vector space, the practical meaning is that the lower limit of the acceptable resource occupancy rate between the corresponding two nodes in the slice example is detected that more than T appears in the vector space max The euclidean distance of (c) will trigger slice reconstruction. The difference between triggering slice reconstruction in the two cases is that the former would allocate more bearer resources for the new slice instance than the existing slice instance when reconstructed, and the latter would allocate less bearer resources than the existing slice instance.
That is, this example proposes a slice state monitoring method based on Node2Vec, by mapping slice structure and slice load from high-dimensional information space to two-dimensional vector space, space complexity required for storing slice structure and slice state is reduced, space cost for monitoring slice structure and slice state is reduced, efficiency of managing slice structure and slice state is improved, slice reconstruction trigger decision based on two-dimensional vector space is adopted, in two-dimensional vector diagram output by Node2Vec algorithm, there is one-to-one correspondence between vector and Node in slice topology, vector relationship reflects topology structure and load state between nodes in slice Chile, and multidimensional information fusion of slice structure and load is realized by taking the result of relation operation between vectors as judgment standard of reconstruction trigger Decision making is performed, the accuracy of the decision making is improved, a sampling strategy S is adopted, and a bias parameter w is introduced in a Node2Vec model sampling stage vx The Node sequence sample obtained by sampling can train a mapping model fusing the slice structure and the load state, and an implementation scheme is provided for the application of the Node2Vec model in the slice representation and monitoring field.
The invention provides a triggering method, which comprises the following steps: obtaining a slice instance of a network slice, inputting the slice instance into a trained Node2Vec model, and outputting a vector of a Node in the obtained slice instance, wherein vector elements of the Node comprise: determining to trigger the reconstruction of the slice instance according to the vector of the node; that is, in the present invention, after a slice instance of a network slice is obtained, the slice instance is input into a trained Node2Vec model, so that a vector of a Node of the slice instance can be obtained, and since the obtained vector includes an amount for representing a Node structure and a Node load, and the Node-based vector can reflect the structure of the slice and the load condition of the slice, in determining whether to trigger the reconstruction of the slice instance according to the vector of the Node, the complexity of determining whether to trigger the reconstruction of the slice instance is reduced by using the vector of the Node with two dimensions, thereby improving the decision efficiency of whether to reconstruct the network slice, and further improving the reconstruction efficiency of the network slice.
Example two
Based on the same inventive concept, an embodiment of the present invention provides a triggering device, and fig. 4 is a schematic structural diagram of an alternative triggering device in the embodiment of the present invention, as shown in fig. 4, where the triggering device includes: an acquisition module 41, a processing module 42 and a triggering module 43;
wherein, the obtaining module 41 is configured to obtain a slice instance of a network slice;
the processing module 42 is configured to input the slice instance into the trained Node2Vec model, and output a vector of a Node in the slice instance; wherein the vector elements of the node include: an amount representing the structure of the node and an amount representing the load of the node;
the triggering module 43 is configured to determine whether to trigger reconstruction of the slice instance according to the vector of the node.
In an alternative embodiment, the apparatus is further configured to:
the trained Node2Vec model is obtained by the following method:
acquiring a starting node and a next-hop node of the starting node from a slice example to be trained, determining the next-hop node as a current node, and setting an initial value of sampling times i as 1;
acquiring adjacent nodes of the current node;
when i is smaller than the sampling step length, calculating the offset migration probability of the current node and the adjacent node according to a preset offset migration probability algorithm; the preset bias migration probability is positively correlated with the network performance parameter of the current service when the slice instance is adopted at the current moment;
Sequencing the calculated offset walk probability from large to small, selecting the adjacent nodes ranked in the previous M, determining the selected adjacent nodes as current nodes, updating i to be i+1, and returning to execute the adjacent nodes for acquiring the current nodes; wherein M is a positive integer less than the number of adjacent nodes;
when i is greater than or equal to the sampling step length, at least two groups of Node sequences obtained by sampling are input into a preset Node2Vec model for training, and a trained Node2Vec model is obtained.
In an alternative embodiment, the network performance parameters of the current service include one or more of the following:
the average occupied bandwidth of the current service, the average message delay of the current service and the average packet loss rate of the current service.
In an alternative embodiment, the apparatus is further configured to:
calculating to obtain the bias migration probability pi from the current node to the adjacent node by adopting the formula (1) -formula (3) vx 。
In an alternative embodiment, the triggering module 43 is specifically configured to:
according to the vector of the nodes, calculating the Euclidean distance between any two nodes in the slice example;
and triggering the reconstruction of the slice instance when the Euclidean distance between any two nodes meets the preset condition.
In an alternative embodiment, when the euclidean distance between any two nodes meets a preset condition, the triggering module 43 triggers the reconstruction of the slice instance, which includes:
and triggering the reconstruction of the slice instance when the Euclidean distance between any two nodes is smaller than the preset minimum Euclidean distance between the nodes.
In an alternative embodiment, when the euclidean distance between any two nodes meets a preset condition, the triggering module 43 triggers the reconstruction of the slice instance, which includes:
and triggering the reconstruction of the slice instance when the Euclidean distance between any two nodes is larger than the preset maximum Euclidean distance between the nodes.
In an alternative embodiment, the apparatus is further configured to:
and when no distance smaller than the preset minimum Euclidean distance between the nodes exists in the Euclidean distance between any two nodes, and no distance larger than the preset maximum Euclidean distance between the nodes exists in the Euclidean distance between any two nodes, triggering to reconstruct the slice instance is forbidden.
In practical applications, the acquiring module 41, the processing module 42 and the triggering module 43 may be implemented by a processor located on the device, specifically, a central processing unit (CPU, central Processing Unit), a microprocessor (MPU, microprocessor Unit), a digital signal processor (DSP, digital Signal Processing) or a field programmable gate array (FPGA, field Programmable Gate Array), etc.
Fig. 5 is a schematic structural diagram of an alternative device provided in an embodiment of the present invention, and as shown in fig. 5, an embodiment of the present invention provides a device 500, including:
a processor 51 and a storage medium 52 storing instructions executable by the processor 51, the storage medium 52 performing operations in dependence on the processor 51 through a communication bus 53, the triggering method according to the above-mentioned embodiment being performed when the instructions are executed by the processor 51.
In practical use, the components in the terminal are coupled together via the communication bus 53. It will be appreciated that the communication bus 53 is used to enable connected communication between these components. The communication bus 53 includes a power bus, a control bus, and a status signal bus in addition to the data bus. But for clarity of illustration the various buses are labeled as communication bus 53 in fig. 5.
An embodiment of the present invention provides a computer storage medium storing executable instructions that, when executed by one or more processors, perform the triggering method of embodiment one.
The computer readable storage medium may be a magnetic random access Memory (ferromagnetic random access Memory, FRAM), read Only Memory (ROM), programmable Read Only Memory (Programmable Read-Only Memory, PROM), erasable programmable Read Only Memory (Erasable Programmable Read-Only Memory, EPROM), electrically erasable programmable Read Only Memory (Electrically Erasable Programmable Read-Only Memory, EEPROM), flash Memory (Flash Memory), magnetic surface Memory, optical disk, or compact disk Read Only Memory (Compact Disc Read-Only Memory, CD-ROM).
It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of a hardware embodiment, a software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, magnetic disk storage, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations 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.
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.
The foregoing description is only of the preferred embodiments of the present invention, and is not intended to limit the scope of the present invention.
Claims (9)
1. A method of triggering, comprising:
after the slice instance is online, acquiring a slice instance of the network slice; the slice instance is created according to a slice template matched with the current service requirement;
Inputting the topological graph of the slice example into a trained Node2Vec model, and outputting a vector of a Node in the slice example; wherein the vector elements of the node include: an amount representing the structure of the node and an amount representing the load of the node;
determining whether to trigger reconstruction of the slice instance according to the vector of the node; the trained Node2Vec model is obtained by the following steps:
acquiring a starting node and a next-hop node of the starting node from a slice example to be trained, determining the next-hop node as a current node, and setting an initial value of sampling times i to be 1;
acquiring adjacent nodes of the current node;
when i is smaller than the sampling step length, calculating the offset migration probability of the current node and the adjacent node according to a preset offset migration probability algorithm; the preset bias migration probability is positively correlated with network performance parameters of the current service when the slice instance is adopted at the current moment;
sequencing the calculated offset walk probability according to the order from large to small, selecting the adjacent nodes ranked in the previous M, determining the selected adjacent nodes as current nodes, updating i to be i+1, and returning to execute the adjacent nodes for acquiring the current nodes; wherein M is a positive integer less than the number of adjacent nodes;
When i is greater than or equal to the sampling step length, inputting at least two groups of Node sequences obtained by sampling into a preset Node2Vec model for training to obtain the trained Node2Vec model;
the determining whether to trigger the reconstruction of the slice instance according to the vector of the node comprises:
according to the vector of the nodes, calculating the Euclidean distance between any two nodes in the slice example;
and triggering the reconstruction of the slice instance when the Euclidean distance between any two nodes meets a preset condition.
2. The method of claim 1, wherein the network performance parameters of the current service include one or more of:
the average occupied bandwidth of the current service, the average message delay of the current service and the average packet loss rate of the current service.
3. A method according to claim 1 or 2, wherein the bias walk probability of the current node to the adjacent node is calculated using the formula：
；
；
；
Wherein v represents the current node, t represents the neighboring node of the current node, W 0 Representing the average occupied bandwidth of the current service at the current moment, T 0 Average message delay representing current business at current moment E 0 Represents the average packet loss rate, k of the current service at the current moment W Is W 0 Is (k) the estimated level of (k) T Is T 0 Is (k) the estimated level of (k) E Is E 0 Is used to determine the estimated level of (1),representing the shortest number of hops that node t needs to jump to node x.
4. The method of claim 1, wherein triggering the reconstruction of the slice instance when the euclidean distance between any two nodes satisfies a preset condition comprises:
and triggering the reconstruction of the slice instance when the Euclidean distance between any two nodes is smaller than the preset minimum Euclidean distance between the nodes.
5. The method of claim 1, wherein triggering the reconstruction of the slice instance when the euclidean distance between any two nodes satisfies a preset condition comprises:
and triggering the reconstruction of the slice instance when the Euclidean distance between any two nodes is larger than the preset maximum Euclidean distance between the nodes.
6. The method according to claim 1, wherein the method further comprises:
and when no distance smaller than the preset minimum Euclidean distance between the nodes exists in the Euclidean distance between any two nodes, and no distance larger than the preset maximum Euclidean distance between the nodes exists in the Euclidean distance between any two nodes, triggering the reconstruction of the slice instance is forbidden.
7. A triggering device, the device comprising:
the acquisition module is used for acquiring the slice instance of the network slice after the slice instance is online; the slice instance is created according to a slice template matched with the current service requirement; the processing module is used for inputting the topological graph of the slice example into a trained Node2Vec model and outputting a vector of a Node in the slice example; wherein the vector elements of the node include: an amount representing the structure of the node and an amount representing the load of the node;
the triggering module is used for determining whether to trigger the reconstruction of the slice instance according to the vector of the node; triggering the reconstruction of the slice instance when the Euclidean distance between any two nodes meets a preset condition;
the trained Node2Vec model is obtained by adopting the following modes:
acquiring a starting node and a next-hop node of the starting node from a slice example to be trained, determining the next-hop node as a current node, and setting an initial value of sampling times i to be 1;
acquiring adjacent nodes of the current node;
when i is smaller than the sampling step length, calculating the offset migration probability of the current node and the adjacent node according to a preset offset migration probability algorithm; the preset bias migration probability is positively correlated with network performance parameters of the current service when the slice instance is adopted at the current moment;
Sequencing the calculated offset walk probability according to the order from large to small, selecting the adjacent nodes ranked in the previous M, determining the selected adjacent nodes as current nodes, updating i to be i+1, and returning to execute the adjacent nodes for acquiring the current nodes; wherein M is a positive integer less than the number of adjacent nodes;
when i is greater than or equal to the sampling step length, inputting at least two groups of Node sequences obtained by sampling into a preset Node2Vec model for training, and obtaining the trained Node2Vec model.
8. An apparatus, the apparatus comprising:
a processor and a storage medium storing instructions executable by the processor, the storage medium performing operations in dependence on the processor through a communication bus, the instructions, when executed by the processor, performing the triggering method of any one of claims 1 to 6.
9. A computer storage medium storing executable instructions which, when executed by one or more processors, perform the triggering method of any one of claims 1 to 6.
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Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2018184666A1 (en) * | 2017-04-04 | 2018-10-11 | Telefonaktiebolaget Lm Ericsson (Publ) | Training a software agent to control a communication network |
| CN108965024A (en) * | 2018-08-01 | 2018-12-07 | 重庆邮电大学 | A kind of virtual network function dispatching method of the 5G network slice based on prediction |
| CN110213780A (en) * | 2018-02-28 | 2019-09-06 | 中兴通讯股份有限公司 | Management method, management and the layout entity and storage medium of network slice |
| CN110881207A (en) * | 2019-10-31 | 2020-03-13 | 华为技术有限公司 | Network slice selection method and related product |
| WO2020057261A1 (en) * | 2018-09-17 | 2020-03-26 | 华为技术有限公司 | Communication method and apparatus |
| CN110958666A (en) * | 2019-11-20 | 2020-04-03 | 无锡北邮感知技术产业研究院有限公司 | Network slice resource mapping method based on reinforcement learning |
| CN111083744A (en) * | 2019-12-31 | 2020-04-28 | 北京思特奇信息技术股份有限公司 | Network slicing method, device, storage medium and equipment |
| CN111866953A (en) * | 2019-04-26 | 2020-10-30 | ***通信有限公司研究院 | Network resource allocation method, device and storage medium |
| WO2020258920A1 (en) * | 2019-06-26 | 2020-12-30 | 华为技术有限公司 | Network slice resource management method and apparatus |
| CN112613230A (en) * | 2020-12-15 | 2021-04-06 | 云南电网有限责任公司 | Network slice resource dynamic partitioning method and device based on neural network |
-
2021
- 2021-04-13 CN CN202110397103.6A patent/CN115209431B/en active Active
Patent Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2018184666A1 (en) * | 2017-04-04 | 2018-10-11 | Telefonaktiebolaget Lm Ericsson (Publ) | Training a software agent to control a communication network |
| CN110213780A (en) * | 2018-02-28 | 2019-09-06 | 中兴通讯股份有限公司 | Management method, management and the layout entity and storage medium of network slice |
| CN108965024A (en) * | 2018-08-01 | 2018-12-07 | 重庆邮电大学 | A kind of virtual network function dispatching method of the 5G network slice based on prediction |
| WO2020057261A1 (en) * | 2018-09-17 | 2020-03-26 | 华为技术有限公司 | Communication method and apparatus |
| CN111866953A (en) * | 2019-04-26 | 2020-10-30 | ***通信有限公司研究院 | Network resource allocation method, device and storage medium |
| WO2020258920A1 (en) * | 2019-06-26 | 2020-12-30 | 华为技术有限公司 | Network slice resource management method and apparatus |
| CN110881207A (en) * | 2019-10-31 | 2020-03-13 | 华为技术有限公司 | Network slice selection method and related product |
| CN110958666A (en) * | 2019-11-20 | 2020-04-03 | 无锡北邮感知技术产业研究院有限公司 | Network slice resource mapping method based on reinforcement learning |
| CN111083744A (en) * | 2019-12-31 | 2020-04-28 | 北京思特奇信息技术股份有限公司 | Network slicing method, device, storage medium and equipment |
| CN112613230A (en) * | 2020-12-15 | 2021-04-06 | 云南电网有限责任公司 | Network slice resource dynamic partitioning method and device based on neural network |
Non-Patent Citations (3)
| Title |
|---|
| 基于AI的5G网络切片管理技术研究;徐丹;王海宁;袁祥枫;朱雪田;;电子技术应用(01);全文 * |
| 基于多智体强化学习的接入网络切片动态切换;秦爽;赵冠群;冯钢;;电子科技大学学报(02);全文 * |
| 基于强化学习的5G网络切片虚拟网络功能迁移算法;唐伦;周钰;谭颀;魏延南;陈前斌;;电子与信息学报(03);全文 * |
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