CN112399483A - Constraint route control system based on GNs graph predictor - Google Patents

Constraint route control system based on GNs graph predictor Download PDF

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CN112399483A
CN112399483A CN202011211827.9A CN202011211827A CN112399483A CN 112399483 A CN112399483 A CN 112399483A CN 202011211827 A CN202011211827 A CN 202011211827A CN 112399483 A CN112399483 A CN 112399483A
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control system
gns
predictor
graph
network
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CN112399483B (en
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秦卫忠
杨晨
韩保华
王善忠
胥晓冬
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Nanjing Jiahuan Technology Co Ltd
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W28/00—Network traffic management; Network resource management
    • H04W28/02—Traffic management, e.g. flow control or congestion control
    • H04W28/08—Load balancing or load distribution
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W28/00—Network traffic management; Network resource management
    • H04W28/16—Central resource management; Negotiation of resources or communication parameters, e.g. negotiating bandwidth or QoS [Quality of Service]
    • H04W28/24—Negotiating SLA [Service Level Agreement]; Negotiating QoS [Quality of Service]
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W40/00—Communication routing or communication path finding
    • H04W40/02—Communication route or path selection, e.g. power-based or shortest path routing
    • H04W40/12—Communication route or path selection, e.g. power-based or shortest path routing based on transmission quality or channel quality
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W40/00—Communication routing or communication path finding
    • H04W40/24—Connectivity information management, e.g. connectivity discovery or connectivity update
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W40/00—Communication routing or communication path finding
    • H04W40/34—Modification of an existing route

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Abstract

The invention provides a constraint route control system based on a GNs graph predictor, which comprises a QoS route control system and a GNs graph predictor, wherein the QoS route control system comprises a QoS route control system and a GNs graph predictor; the GNs graph predictor is used for simulating the dynamic change of the communication flow of the whole network when different network nodes have instantaneous peak loads and providing a prejudgment standard for the QoS route control system according to different communication flow changes; the GNs diagram predictor performs offline calculation of network communication flow, feeds off the offline calculation result back to the QoS route control system, and improves the implementation efficiency of the current QoS route; the QoS routing control system regularly feeds back historical data of network communication flow to the GNs diagram predictor, and the GNs diagram predictor carries out off-line calculation again according to the received feedback data. According to the method, the node data transmission condition under the condition of local node instantaneous peak load is simulated and calculated through the GNs network, and effective local isolation and route change are constructed according to the condition, so that the instantaneous peak load is controlled in a small range, and the impact and the damage of the instantaneous peak load on the overall communication network are avoided.

Description

Constraint route control system based on GNs graph predictor
Technical Field
The invention relates to the technical field of network routing control, in particular to a network load balancing technology, and specifically relates to a constraint routing control system based on a GNs graph predictor.
Background
With the advent of the age of 5G, Internet multimedia applications, such as video chat, live web broadcast, web talk, and distance education, became endless, the Internet began to move from single data transmission to comprehensive transmission of data, voice, image, and video multimedia information. Data transmission needs to meet different quality of service (QoS) requirements, and multi-QoS constrained routing becomes an effective way to solve QoS problems. Meanwhile, under the condition of a 5G network, if the communication flow of part of nodes is overhigh instantly, the large-scale data is diffused in the surrounding 5G nodes, so that the local network is blocked, other normal flows passing through the nodes are blocked, and the stability of the whole network is further influenced. In addition, in actual network maintenance and operation, node debugging and the like are often required to adjust the current network structure, and the original QoS requirements are also ensured, which requires higher requirements on network QoS constraint routing, especially QoS constraint routing control during local peak data transmission.
The QoS routing protocol comprises a multi-path QoS routing protocol and a node-controlled single-hop path QoS routing protocol. The algorithm can improve the network throughput and the network performance degradation, optimize the resource configuration, balance the network load, realize the optimization of the network global resource utilization rate and maximize the requirement of the network for accepting other QoS parameters no matter the multi-path Qos routing protocol or the QoS path of the single-hop path. Under normal network carrying, considering end-to-end reliability, time delay and energy consumption, the common QoS routing protocol can realize the functions of preprocessing the original data of the node, accelerating convergence of pruning rules and reducing the whole energy consumption of the network by a node energy perception awakening mechanism. However, the QoS protocol has not been specifically studied for the local peak load situation, and there are the following technical blind points or difficulties:
1) route optimization for local peak load. Under normal network operating conditions, it is generally difficult to encounter peak loads at local nodes. If some special conditions occur, for example, some nodes are hacked, the local area nodes have particularly high network traffic, and if the local area nodes are not controlled, the traffic data can quickly affect the surrounding network nodes. Currently, there is a lack of an in-depth and efficient solution for route optimization for such local peak loads;
2) multiple and different variations of network structure. In an actual engineering environment, a network structure is often changed due to multiple complex factors such as service expansion and technical maintenance. In some cases, when the network structure is changed, the peak traffic occurs for some reasons, and then the existing routing control system cannot adjust in time for the changed network structure;
3) the large-scale occlusion model has high calculation difficulty. In a real environment, due to the adoption of multiple technical means, generally, the situation that large-scale network blocking occurs is less. As such, people have less knowledge about the actual situation of network congestion on a large scale, and the related research is insufficient. Especially, in a multi-node network environment, how to calculate the large-scale network blocking condition is still an engineering problem.
Therefore, the main purpose of network load scheduling and provisioning is to avoid network congestion, but there are many times when particularly high peak traffic occurs at some nodes of the network. The peak traffic is often sudden and irregular, and the peak traffic can bring a large load impact to the network, resulting in overloading of a large area of network nodes. Existing route management systems are difficult to handle in the face of such peak traffic.
Disclosure of Invention
The invention aims to provide a constraint route control system based on a GNs graph predictor, aiming at the network transmission problem of sudden excess communication reception of local nodes under a wireless network, which adopts an independent GNs predictor and an offline calculation mode to simulate peak traffic propagation modes under multiple nodes of different routing strategies, selects a route control strategy combination with optimal QoS constraint from the peak traffic propagation modes, and integrates corresponding peak traffic characteristics and a strategy combination scheme into the existing network system, so that the route control strategies can be automatically switched when similar network peak modes are met, the optimal route control effect under the peak traffic of the local nodes is achieved, the problem of large-area network overload caused by local instantaneous peak traffic is avoided, and the overall network stability is improved.
In order to achieve the above object, a first aspect of the present invention provides a GNs graph predictor-based constrained route control system, which includes a QoS route control system and a GNs graph predictor;
the QoS route control system is used for carrying out route forwarding control on each node in the network;
the GNs graph predictor is used for simulating the dynamic change of the communication flow of the whole network when the single node or the multiple nodes in different network nodes have instantaneous peak load and providing a prejudgment standard for the QoS route control system according to different communication flow changes;
the GNs diagram predictor is independent of the QoS route control system, offline calculation of network communication flow is carried out, offline calculation results are fed back to the QoS route control system, and implementation efficiency of a current QoS route is improved;
and the QoS routing control system feeds the current network communication flow historical data back to the GNs diagram predictor in a set period, and the GNs diagram predictor carries out off-line calculation again according to the received feedback data.
Preferably, the QoS route control system is configured to deploy a routing protocol policy algorithm to each node so that each node performs route processing according to configuration parameters of the node;
the QoS route control system is set to record the original communication data of the nodes when the network is normal, the original communication data comprises the transmission time and the transmission energy consumption of effective data, and the original communication data is summarized and output to the GNs graph predictor periodically.
Preferably, the GNs graph predictor comprises an encoder, a processing unit, and a decoder;
the encoder is used for encoding the received original communication data from the QoS routing control system and carrying out normalization operation;
the processing unit comprises a plurality of serially connected graph neural network computing units, and each graph neural network computing unit is stored as an independent layer;
and the decoder is used for decoding the prediction result output by the processing unit and generating configuration strategy data used by the QoS route control system.
Preferably, the encoder is configured to perform normalization processing on the acquired raw communication data, and construct an input matrix for input to a subsequent processing unit, which is expressed as:
Figure BDA0002758992600000031
wherein G represents an input matrix generated after encoding;
v denotes the original communication data actually received, Vi∈V；
E denotes a learning prediction vector, Ei,j∈E；
viRepresenting iteratively loaded learning functions, vi＝εv(ei)；
εvAnd εeThe vectors of the multilayer perceptrons with the same input matrix order;
thus, the input original communication data is normalized, and the original numerical value is replaced by the mean value and the variance to generate the input matrix.
Preferably, the graph neural network computing unit in the arithmetic processor is configured to process graph structure data input, that is, the input matrix, wherein the graph neural network computing unit employs a space-based graph neural network computing unit, and the construction process includes:
firstly, a node set V and a link edge combination E in an encoding output result G are constructed, wherein:
Figure BDA0002758992600000032
Figure BDA0002758992600000033
rk，sk∈{1：Nv}；
then, the weight between each node is:
Figure BDA0002758992600000034
obtaining the accumulated value of each node as follows:
Figure BDA0002758992600000035
then, the node prediction output is:
Figure BDA0002758992600000036
when a graph neural network computing unit is used for carrying out simulation prediction on a known system, namely a Qos routing control system, an effective iteration updating function needs to be constructed, and a model is converted into a model according to iteration training:
Figure BDA0002758992600000041
finally, the next state is updated from the predicted acceleration using a semi-implicit Euler integral function, namely:
Figure BDA0002758992600000042
Figure BDA0002758992600000043
wherein Δ t is 1.
Preferably, the decoder is an inverse operation of the encoder, and is configured to convert a parameter matrix output by the GNs graph predictor into a form that can be read and configured by the Qos routing control system, which is specifically expressed as follows:
Figure BDA0002758992600000044
wherein, deltavRepresenting the real control variables in the y system, namely the Qos route control system.
Therefore, the GNs graph predictor-based constraint routing control system according to the foregoing embodiment has significant advantages over the prior art in that:
1) the constraint routing control system based on the GNS graph predictor provided by the invention introduces the GNN artificial neural network, starts from a small-scale blocking mode, gradually expands the calculation level of the GNN artificial neural network, and realizes model research on multi-node large-scale blocking. Finding a propagation mode of local peak load in a theoretical infinite capacity infinite node network from a data simulation level, and further constructing an optimal QoS constraint route calculation model based on the theoretical network propagation form;
2) a predictive computational model based on GNs plot simulator is proposed. In the face of an infinite-capacity infinite node network, if a traditional SPH (smoothed Particle dynamics) algorithm is adopted, the calculation amount is very large, so that the invention introduces a GNs diagram predictor, the original simulation calculation process is simplified by utilizing the predictor model, the calculation efficiency is improved, and the data transmitted by different types of local peak loads in the network can be quickly simulated;
the peak load propagation condition of a multi-element heterogeneous network is further simulated on an ideal network, information transmission characteristics of different network structures in different load modes are obtained, and data basis is provided for predicting future flow change according to the current flow mode condition detected by an actual network node;
3) based on the simulation data, a prediction control flow is constructed, when network flow similar to the simulation prediction characteristic points appears on some nodes in the network, the original network routing control algorithm is immediately changed to a new network routing, so that the local peak load is controlled in a limited range, and the normal operation of a new-structure network formed by other nodes can be ensured.
Therefore, the invention simulates and calculates the node data transmission condition under the condition of local node instantaneous peak load through the GNs network, and constructs effective local isolation and route change according to the condition so as to control the instantaneous peak load in a small range and avoid the impact and the harm to the overall communication network.
It should be understood that all combinations of the foregoing concepts and additional concepts described in greater detail below can be considered as part of the inventive subject matter of this disclosure unless such concepts are mutually inconsistent. In addition, all combinations of claimed subject matter are considered a part of the presently disclosed subject matter.
The foregoing and other aspects, embodiments and features of the present teachings can be more fully understood from the following description taken in conjunction with the accompanying drawings. Additional aspects of the present invention, such as features and/or advantages of exemplary embodiments, will be apparent from the description which follows, or may be learned by practice of specific embodiments in accordance with the teachings of the present invention.
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The drawings are not intended to be drawn to scale. In the drawings, each identical or nearly identical component that is illustrated in various figures may be represented by a like numeral. For purposes of clarity, not every component may be labeled in every drawing. Embodiments of various aspects of the present invention will now be described, by way of example, with reference to the accompanying drawings, in which:
FIG. 1 is a functional block diagram of a GNs graph predictor based constrained routing control system of the present invention.
FIG. 2 is a schematic diagram of the GNs graph predictor of the present invention.
Detailed Description
In order to better understand the technical content of the present invention, specific embodiments are described below with reference to the accompanying drawings.
In this disclosure, aspects of the present invention are described with reference to the accompanying drawings, in which a number of illustrative embodiments are shown. Embodiments of the present disclosure are not necessarily intended to include all aspects of the invention. It should be appreciated that the various concepts and embodiments described above, as well as those described in greater detail below, may be implemented in any of numerous ways, as the disclosed concepts and embodiments are not limited to any one implementation. In addition, some aspects of the present disclosure may be used alone, or in any suitable combination with other aspects of the present disclosure.
In the constraint routing control system based on the GNs graph predictor according to the exemplary embodiment of the invention, on the basis of the existing network routing system architecture, a GNs graph predictor is added, the GNs graph predictor adopts a GNN artificial neural network to construct a GNs graph prediction framework, peak traffic propagation modes under multiple nodes of different routing strategies are simulated through the GNs graph predictor, a routing control strategy combination with optimal QoS constraint is selected from the peak traffic propagation modes, and corresponding peak traffic characteristics and the strategy combination scheme are integrated into the network routing system, so that the routing control strategies can be automatically switched when similar network peak modes are met, network routing is dynamically adjusted, network load balance is realized, and the optimal routing control effect under local node peak traffic is achieved.
The exemplary GNs graph predictor based constrained routing control system shown in connection with fig. 1 and 2 includes a QoS routing control system and GNs graph predictors.
And the QoS route control system is used for carrying out route forwarding control on each node in the network. In a specific embodiment, the QoS route control system is a QoS route control system for each node in the network, which performs overall management on the route of each node.
The GNs graph predictor is used for simulating the dynamic change of the communication flow of the whole network when the single node or the multiple nodes in different network nodes have instantaneous peak load, and providing a prejudgment standard for the QoS route control system according to the change of different communication flows.
Preferably, the GNs prediction model may be calculated offline, an optimal policy is processed in the calculation result set in cooperation with the prediction of each grid node, and the policy is issued to each node of the network.
The GNs graph predictor is independent of the QoS route control system, offline calculation of network communication flow is carried out, offline calculation results are fed back to the QoS route control system, and implementation efficiency of current QoS routes is improved.
And the QoS routing control system feeds the current network communication flow historical data back to the GNs diagram predictor in a set period, and the GNs diagram predictor carries out off-line calculation again according to the received feedback data.
Preferably, the QoS route control system is configured to deploy a routing protocol policy algorithm to each node so that each node performs route processing according to configuration parameters of the node;
the QoS route control system is set to record the original communication data of the nodes when the network is normal, the original communication data comprises the transmission time and the transmission energy consumption of effective data, and the original communication data is summarized and output to the GNs graph predictor periodically.
At present, aiming at the fluctuating temperature caused by local information impact in the network, a local simulation mode is mainly adopted for adjustment in the prior art, but a global calculation mode is not adopted, because the influence of the global calculation is large. Therefore, there are limitations on the influence of the propagation attribute and the channel bandwidth of each node in the calculation process, and these limitations cause a large deviation between the simulation data and the real data, which is determined by the simulation process and the result and cannot play a predictive effect in the actual operation effort.
In the implementation process, the graph network predictor units are introduced, a plurality of graph network predictor units are connected in series and grouped to construct, the larger calculation problem can be quickly adapted only by structural expansion, the calculation efficiency bottleneck is solved, and the structure of each single graph network predictor unit is simpler; and the network mode can be fully utilized, the flow is parallelized, and only one encoding process and one prediction result decoding process are needed to be carried out on the samples of the Qos routing control system.
It should be understood that GNs used in the present invention are abbreviations of Graph Network-based architectures (GNs), different Graph Network models can be constructed based on different Graph theories, and the present invention is directed to the Network transmission problem that a local node in the same wireless Network generates burst super traffic communication, and the node and connectivity are only traffic limited without other constraint requirements, so that the GNs are abbreviated as combining with the serial connection of a plurality of GN small units.
With reference to the prediction process examples shown in fig. 1 and 2, after iterative learning, the GNs graph predictor can quickly learn characteristics of network congestion in different modes, so as to make quick prediction according to the communication condition of the existing network, and select an effective policy to configure to the Qos routing control system.
As shown in fig. 2, the GNs graph predictor includes an encoder, a processing unit, and a decoder; the encoder is used for encoding the received original communication data from the QoS routing control system and carrying out normalization operation; the processing unit comprises a plurality of serially connected graph neural network computing units, and each graph neural network computing unit is stored as an independent layer; and the decoder is used for decoding the prediction result output by the processing unit and generating configuration strategy data used by the QoS route control system.
Preferably, the encoder is configured to perform normalization processing on the acquired raw communication data, and construct an input matrix for input to a subsequent processing unit, which is expressed as:
Figure BDA0002758992600000071
wherein G represents an input matrix generated after encoding;
v denotes the original communication data actually received, Vi∈V；
E denotes a learning prediction vector, Ei,j∈E；
viRepresenting iteratively loaded learning functions, vi＝εv(ei)；
εvAnd εeThe vectors of the multilayer perceptrons with the same input matrix order;
thus, the input original communication data is normalized, and the original numerical value is replaced by the mean value and the variance to generate the input matrix.
Preferably, the graph neural network computing unit in the arithmetic processor is configured to process graph structure data input, that is, the input matrix, wherein the graph neural network computing unit employs a space-based graph neural network computing unit, and the construction process includes:
firstly, a node set V and a link edge combination E in an encoding output result G are constructed, wherein:
Figure BDA0002758992600000072
Figure BDA0002758992600000073
rk，sk∈{1：Nv}；
then, the weight between each node is:
Figure BDA0002758992600000074
obtaining the accumulated value of each node as follows:
Figure BDA0002758992600000075
then, the node prediction output is:
Figure BDA0002758992600000081
when a graph neural network computing unit is used for carrying out simulation prediction on a known system, namely a Qos routing control system, an effective iteration updating function needs to be constructed, and a model is converted into a model according to iteration training:
Figure BDA0002758992600000082
finally, the next state is updated from the predicted acceleration using a semi-implicit Euler integral function, namely:
Figure BDA0002758992600000083
Figure BDA0002758992600000084
wherein Δ t is 1.
Preferably, the decoder is an inverse operation of the encoder, and is configured to convert a parameter matrix output by the GNs graph predictor into a form that can be read and configured by the Qos routing control system, which is specifically expressed as follows:
Figure BDA0002758992600000085
wherein, deltavRepresenting the real control variables in the y system, namely the Qos route control system.
In the process of implementing the invention, as shown in the combination of figures 1 and 2, the invention
Figure BDA00027589926000000811
Locating traffic in nodes during actual routing of network trafficStatus. If K is defined as a time parameter, then
Figure BDA00027589926000000812
Defined as a sequence of undulating state trajectories.
Figure BDA0002758992600000086
Thus, a simulator can be constructed
Figure BDA0002758992600000087
Prediction of future states is achieved by modeling the dynamics of past states.
It can give a calculation that can simulate a future trajectory model, expressed as:
Figure BDA0002758992600000088
the trajectory sequences of these dynamics are obtained by iterative operations, and
Figure BDA0002758992600000089
the result of the iterative operation of one of the times is represented.
Therefore, we use the change of the system state
Figure BDA00027589926000000810
This dynamic differential dust prevention is described. The graph predictor of the invention utilizes the self-learning capability of the graph neural network to continuously approach an ideal equation set, thereby realizing the simulation of the actual situation. The self-learning process of the graph predictor can be expressed as:
Figure BDA0002758992600000091
wherein s isθIs a trained self-learning simulator, namely a small unit of a graph predictor in the invention;
Figure BDA0002758992600000092
is the kth training iteration update function;
dθthe system is an ideal equation set to be learned, and is an actual network communication parameter in a normal communication state in the invention;
theta is an internal parameter that can be adjusted by numerical training;
and ← represents one useful training update mechanism, in the present invention an euler integrator is employed.
Based on the method, the spatial method proposed by Gori is adopted to construct a spatial-based graph neural network computing unit. Defining the Node Velocity of the input network as the average Velocity between the current time step and the last time step, and the flow difference is
Figure BDA0002758992600000093
(constant Δ t omitted for simplicity), also drop, the average acceleration calculated between the next and current time step is targeted:
Figure BDA0002758992600000094
thus, the target acceleration is obtained as:
Figure BDA0002758992600000095
with the above embodiments, the present invention adopts the structures of the input encoder and the output decoder, and the data in the neural network computing unit is normalized, so that the learning process is evaluated by using a dimensionless learning gradient in the present case, and a priority gradient computing formula is constructed:
Figure BDA0002758992600000096
wherein η is the learning rate of the network;
Figure BDA0002758992600000097
is the deviation between the predicted data and the actual data after the network training, and the invention uses the vector variance mean value to calculate.
The network learning rate in the model is an important factor for improving the learning efficiency of the network unit. If the learning rate is low, the convergence time of the objective function needs longer time, and if the learning rate is too low, the parameter change may oscillate, and the objective function may miss some minimum values and be less likely to converge. We therefore use the following formula for the processing:
Figure BDA0002758992600000098
wherein j is the number of learning iterations;
η (j) is the learning rate of the optimization target model parameter θ;
thus, the learning rate of the surface convergence condition ranges from 0.001 to 0.1.
In a specific implementation process, supplementary sample noise is added into training to improve the network learning level and the generalization level of an intensifier. During training, random walk noise is used
Figure BDA0002758992600000101
To disturb the original number of samples.
To maintain data consistency, guarantees are required
Figure BDA0002758992600000102
From which we randomly sampled different sets and added random noise
Figure BDA0002758992600000103
Calculating node flow acceleration by interfering with training track
Figure BDA0002758992600000104
And calculating the predicted acceleration L of the flow of each node after the impact2Dynamic functions of, i.e.
Figure BDA0002758992600000105
In the following, we set $ V $ as the source node of a certain peak load in a network of $ N \ cross N $ nodes in combination with the actual operation of the engineering network, and the $ V $ node is added to the active SET 'ACTIV SET' when the routing policy is first refreshed, broadcasting a data packet carrying address and synchronization information to its single-hop neighbor nodes (B, D, E, F) within the communication range 'R', returning a data packet containing own address and synchronization information to the 'V', returning a 'I' confirmation character packet after the 'V' receives the data packet of 'B, D, E, F', informing that the 'B, D, E, F' is not in the peak load at present, the nodes can normally communicate with each other, and the time delay between each node is fixed.
The above-described confirmation operation is performed every'm' seconds.
When a traffic flow exceeding a preset peak appears in the 'V' node, the 'V' node rapidly compares the traffic flow with the prediction model from the local node, selects an action from the processing strategy, and sends the action as a first forwarding command to the 'B, D, E and F' nodes.
When the node B 'gets the command and enters the emergency processing mode immediately, the current flow model is compared, and the data packet transmitted from the node V' is shielded, so that the information of the node V 'is prevented from impacting the node V'.
And meanwhile, according to the prior prediction model, a threshold value limit is made on the flow packets of D, E and F' so as to avoid the influence of excessive data sets transmitted from other nodes in the local area on the transmission of the local area.
This is also true for the other nodes in the ACTIVE SET ACTIVE _ SET'. And simultaneously, early warning report is carried out on the nodes around the node. Then the nodes V, B, C, D, E and F' are isolated from the large network, and the data packets in the nodes are quickly stored and encapsulated in the local nodes. After the nodes of the external network receive the early warning signal, the original QoS routing table is refreshed again according to the original prediction model, and the original route passing through the nodes with the problems is transferred to other nodes.
When the data flow on the nodes V, B, C, D, E and F' is reduced to a preset value, the load of the nodes falls back to the credible interval. At this time, a recovery instruction is still sent by the node 'V', and transmission is confirmed twice by nodes one by one according to 'V- > B- > C- > D- > E- > F- > V- > B- > C- > D- > E- > F'. After all the nodes of the instruction are confirmed twice, the current load overload phenomenon can be confirmed to be finished. At this time, each node of the network sends a cancel command outwards again. The shielded nodes are connected back to the original network by the external network, and the QoS routing system is recovered to be normal.
When the load is abnormal, the 'V' node is down directly. At this time, if any node in 'B, C, D, E, F' fails to receive the normal polling message of 'V' within the specified time, the peak overload mode is entered directly, and the above operations are repeated until the accident situation is resolved.
The system of the invention can be effectively applied to the Internet, in particular to a 5G wireless network. Because the independent GNs predictor and the offline computing mode are adopted, the method is easier to integrate into the existing network routing control system. The invention can provide more intelligent guarantee for the actual operation network system, avoid the problem of large-area network overload caused by local instantaneous peak flow and improve the overall network stability.
For example, in a large-scale online activity, a large number of users are rushed to send information to one or more wireless network nodes, and if the information is not blocked in time, the operation of the nodes in the area is affected. Simply disconnecting the local area network from the outside completely affects both network transmission at the active site and other network transmissions that would otherwise be required through the active site nodes. The system of the invention can effectively predict the influence range of the node peak flow, predict the influence effect of the flow on the external network, and automatically distinguish the active local area network from the external network in advance. The node routing in the external network is automatically adjusted, and the node of the local network is skipped, so that the overall network carrying is not influenced. The peak load in the local network of the activity runs in the local network of the activity, and the network requirement of the activity is not influenced. When the activity is finished, the system automatically connects the local network nodes back to the backbone network according to the load condition, and the original node routing of the network is recovered.
When a certain node in the network is attacked by a hacker, a large amount of information is generated, and an external instantaneous peak flow is formed. The system can determine a diffusion pattern to the instantaneous peak traffic based on GNs prediction models to quickly isolate its associated nodes from the backbone network. Meanwhile, a routing system of the network is refreshed, the node routing in the external network is automatically adjusted, the active local node is skipped, and the overall network carrying is not influenced. After the information load of the hacker attack is eliminated, the system automatically replies to the nodes of the hacker attack and refreshes the route.
Although the present invention has been described with reference to the preferred embodiments, it is not intended to be limited thereto. Those skilled in the art can make various changes and modifications without departing from the spirit and scope of the invention. Therefore, the protection scope of the present invention should be determined by the appended claims.

Claims (6)

1. A constraint route control system based on a GNs graph predictor is characterized by comprising a QoS route control system and a GNs graph predictor;
the QoS route control system is used for carrying out route forwarding control on each node in the network;
the GNs graph predictor is used for simulating the dynamic change of the communication flow of the whole network when the single node or the multiple nodes in different network nodes have instantaneous peak load and providing a prejudgment standard for the QoS route control system according to different communication flow changes;
the GNs diagram predictor is independent of the QoS route control system, offline calculation of network communication flow is carried out, offline calculation results are fed back to the QoS route control system, and implementation efficiency of a current QoS route is improved;
and the QoS routing control system feeds the current network communication flow historical data back to the GNs diagram predictor in a set period, and the GNs diagram predictor carries out off-line calculation again according to the received feedback data.
2. A GNs graph predictor-based constraint routing control system according to claim 1, wherein the QoS routing control system is configured to deploy routing protocol policy algorithms to the respective nodes to cause each node to route according to the node's configuration parameters;
the QoS route control system is set to record the original communication data of the nodes when the network is normal, the original communication data comprises the transmission time and the transmission energy consumption of effective data, and the original communication data is summarized and output to the GNs graph predictor periodically.
3. The GNs-graph-predictor-based constrained routing control system of claim 1 or 2, wherein the GNs-graph predictor comprises an encoder, a processing unit, and a decoder;
the encoder is used for encoding the received original communication data from the QoS routing control system and carrying out normalization operation;
the processing unit comprises a plurality of serially connected graph neural network computing units, and each graph neural network computing unit is stored as an independent layer;
and the decoder is used for decoding the prediction result output by the processing unit and generating configuration strategy data used by the QoS route control system.
4. A GNs-graph-predictor-based constraint routing control system according to claim 3, wherein said encoder is configured to normalize the raw communication data obtained, constructing an input matrix for input to a subsequent processing unit, expressed as:
Figure FDA0002758992590000011
wherein G represents an input matrix generated after encoding;
v denotes the original communication data actually received, Vi∈V；
E denotes a learning prediction vector, Ei,j∈E；
viRepresenting iteratively loaded learning functions, vi＝εv(ei)；
εvAnd εeThe vectors of the multilayer perceptrons with the same input matrix order;
thus, the input original communication data is normalized, and the original numerical value is replaced by the mean value and the variance to generate the input matrix.
5. A constraint routing control system based on GNs graph predictor according to claim 4, characterized in that the graph neural network computing unit in the operation processor is used to process graph structure data input, i.e. the input matrix, wherein the graph neural network computing unit employs a space-based graph neural network computing unit, and its construction process includes:
firstly, a node set V and a link edge combination E in an encoding output result G are constructed, wherein:
Figure FDA0002758992590000021
Figure FDA0002758992590000022
then, the weight between each node is:
Figure FDA0002758992590000023
obtaining the accumulated value of each node as follows:
Figure FDA0002758992590000024
then, the node prediction output is:
Figure FDA0002758992590000025
when a graph neural network computing unit is used for carrying out simulation prediction on a known system, namely a Qos routing control system, an effective iteration updating function needs to be constructed, and a model is converted into a model according to iteration training:
Figure FDA0002758992590000026
finally, the next state is updated from the predicted acceleration using a semi-implicit Euler integral function, namely:
Figure FDA0002758992590000027
Figure FDA0002758992590000028
wherein Δ t is 1.
6. The GNs-graph-predictor-based constrained routing control system of claim 5, wherein the decoder is an inverse operation of an encoder for transforming the parameter matrix output by the GNs-graph predictor into a form that can be read and configured for the Qos routing control system, as expressed in the following:
Figure FDA0002758992590000031
wherein, deltavRepresenting the real control variables in the y system, namely the Qos route control system.
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