CN115190025B - Dynamic balance-based network digital twin body resource optimization method - Google Patents
Dynamic balance-based network digital twin body resource optimization method Download PDFInfo
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
- H04L41/12—Discovery or management of network topologies
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L45/00—Routing or path finding of packets in data switching networks
- H04L45/12—Shortest path evaluation
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L45/00—Routing or path finding of packets in data switching networks
- H04L45/74—Address processing for routing
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L47/00—Traffic control in data switching networks
- H04L47/10—Flow control; Congestion control
- H04L47/12—Avoiding congestion; Recovering from congestion
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- Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
- Y02D30/00—Reducing energy consumption in communication networks
Abstract
The invention relates to a network digital twin body resource optimization method based on dynamic balance, which belongs to the technical field of digital twin body network resource optimization, and solves the problem of weak self-adaptive adjustment capability of the existing network digital twin body resource optimization method based on a software definition method, wherein the method comprises the following steps: acquiring a network digital twin; the network digital twin comprises a controller, network nodes, links and application terminal twin; the controller twin generates a network topology structure serving the application terminal twin based on the self state information reported by the network node twin and the link twin; performing resource information request detection on the network topology structure to acquire resource parameter information of a network node twin and a link twin; and determining a routing decision based on a network topology structure and the resource parameter information according to the service transmission requirement of the application terminal twin, and executing the routing decision in the network entity.
Description
Technical Field
The invention relates to the technical field of digital twin network resource optimization, in particular to a network digital twin resource optimization method based on dynamic balance.
Background
The metauniverse concept and the framework thereof based on the core technologies of digital twinning, artificial intelligence, blockchain, big data and the like are considered as the development direction of social and entertainment for decades and the next revolution direction of industry. The meta universe is the final form connecting the physical world and the virtual world, and becomes a life style of human beings after decades, and the digital economic system is remodeled. The meta universe aggregates a large number of discrete single-point innovations to form a complete application scene and an implementation framework, brings new business model innovations for a long time, and drives related technologies to realize breakthrough.
Under the introduction of the development trend of the metauniverse in the future, it is expected that application scenes and architectures will be oriented to the evolution of applications and technologies in the virtual world based on digital twin, and iterating continuously and repeatedly until a final form which meets the requirements of all parties and matches the technological capability is formed. In the process, if the digital twin is required to be redesigned and built every time the scene and technology are updated, the construction efficiency of the virtual world is greatly limited, and the requirements of various applications are met due to the fact that various resources are rapidly utilized in the meta space are not met.
Therefore, there is a need to achieve this goal through optimization of digital resources in a virtual world formed based on computer software and network technology. In recent years, a software-defined simulation method is a novel simulation method for simulating a real physical world by a digital twin body. The Clean State project, which was most originally in us Stanford University in 2006, has been widely used in the field of simulation over the last decade of development. The system resource management and control are defined through programming, the data and the control plane are separated, a larger flexible implementation and adjustment space is provided for digital twin body simulation, a platform is provided for various resource optimization, and a new solution idea is provided for the architecture evolution of the real physical world.
The simulation method based on software definition can realize the programmable control of the twin network. This approach architecture has three main features: centralized control, control separation, programmability. The aim of the centralized control is to improve the utilization efficiency of resources and realize the improvement of the maintainability of a control plane; the control separation is used as a basic characteristic, a control plane realizes a decision function, and according to a flow table matching result, a data plane can directly process and forward data, so that development and maintenance of physical world equipment are convenient; the programmability can enable developers and maintainers of the digital twin to independently develop the resource management system by depending on application programs so as to adapt to the requirements of various resources in the physical world, meet different types of business and achieve a real simulation effect.
The prior art mostly adopts distributed control of network nodes, and has weak capability of grasping service-oriented integrity and self-adapting adjustment according to dynamic changes of services in the real physical world.
Disclosure of Invention
In view of the above analysis, the embodiment of the invention aims to provide a network digital twin-body resource optimization method based on dynamic balance, which is used for solving the problem that the self-adaptive adjustment capability of the existing network digital twin-body resource optimization method based on a software definition method is not strong.
The invention discloses a network digital twin body resource optimization method based on dynamic balance, which comprises the following steps:
acquiring a network digital twin body obtained by mapping a network entity; the network digital twin comprises a controller twin, a network node twin, a link twin and an application terminal twin;
the controller twin body generates a network topological structure serving the application terminal twin body based on the self state information reported by the network node twin body and the link twin body;
performing resource information request detection on the network topology structure to acquire resource parameter information of the network node twin and the link twin;
and determining a routing decision based on the network topology structure and the resource parameter information according to the service transmission requirement of the application terminal twin, and executing the routing decision in the network entity.
Based on the scheme, the invention also makes the following improvements:
further, the generating a network topology includes:
the controller twins send out topology discovery signaling;
the network node twin and the link twin respond to the topology discovery signaling and report own state information to the controller twin;
the controller twins perform validity judgment on the state information of the network node twins and the link twins, and if the validity judgment is passed, a network topology structure is constructed or updated based on the set of the network node twins and the link twins which are passed by the validity judgment.
Further, the validity judgment includes: judging whether the network element serves the application terminal twin according to the self state information of the network element, and if so, judging the validity of the network element; otherwise, the validity judgment is not passed;
the network element is a network node twine or a link twine.
Further, the self state information of the network node twin at least comprises a port address, an ID number, a source MAC address and a destination MAC address;
the self state information of the link twins comprises port addresses of two network node twins forming the current link.
Further, the resource information request detection comprises network loop delay parameter detection and network element information transceiving parameter detection.
Further, the resource parameter information includes:
delay parameters, operating state information of the network node twins and the link twins, and remaining available resources of the network topology.
Further, the obtaining the resource parameter information of the network node twin and the link twin includes:
the controller twins initiate network loop delay parameter detection to the network topology structure, and delay parameters of network node twins and link twins in the network topology structure are obtained;
the controller twins initiate network element information receiving and transmitting parameter detection to the network topological structure, and working state information of network node twins and link twins in the network topological structure is obtained;
based on the detected working state information, evaluating the flow rate of the network topology structure due to network service; summarizing the detected working state information, the time delay parameter and the flow rate to be used as the resource occupation condition of network service in the network topology structure; and determining the remaining available resources of the network topology structure based on the resource occupation condition of the network service and the whole network resource prediction result of the network topology structure.
Further, the delay parameters of the network node twins include a transmission delay parameter and a processing delay parameter, and the delay parameters of the link twins include a transmission delay parameter.
Further, the working state information of the network node twin body comprises port interaction data quantity and port starting or stopping states; the working state information of the link twins comprises the survival or failure state of the link.
Further, the executing the routing decision according to the service transmission requirement of the application terminal twin body includes:
based on the network topology structure and the resource parameter information, a feasible transmission path is preliminarily determined according to the service transmission requirement of the application terminal twin;
performing routing decision on the feasible transmission paths which are preliminarily determined, and determining an optimal transmission path;
and transmitting the route information of the optimal transmission path to a corresponding network node twin and a link twin in the network topology structure as a route reference for forwarding the service through the optimal transmission path.
Compared with the prior art, the invention has at least one of the following beneficial effects:
according to the network digital twin body resource optimization method based on dynamic balance, on the premise that the flexibility of a software definition architecture is fully utilized, the simulation of the digital twin body is configured more efficiently, meanwhile, the flexible configuration mode of the network digital twin body resource optimization method can be used as various technical routes before physical world real realization to try to provide rich possibility, and the network digital twin body resource optimization method is used as an effective reference for the physical world future real realization, so that the self-adaptive adjustment capability of the network digital twin body resource optimization is effectively enhanced.
In the invention, the technical schemes can be mutually combined to realize more preferable combination schemes. Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention may be realized and attained by the structure particularly pointed out in the written description and drawings.
Drawings
The drawings are only for purposes of illustrating particular embodiments and are not to be construed as limiting the invention, like reference numerals being used to refer to like parts throughout the several views.
FIG. 1 is a dynamic balance-based network digital twin body resource optimization method provided in embodiment 1 of the present invention;
fig. 2 is a schematic diagram of a network hierarchy structure provided in embodiment 1 of the present invention.
Fig. 3 is an average resource utilization curve of the routing decision algorithm provided in embodiment 1 of the present invention.
Detailed Description
Preferred embodiments of the present invention will now be described in detail with reference to the accompanying drawings, which form a part hereof, and together with the description serve to explain the principles of the invention, and are not intended to limit the scope of the invention.
The invention discloses a network digital twin body resource optimization method based on dynamic balance, wherein a flow chart is shown in figure 1, and the method comprises the following steps:
step S1: acquiring a network digital twin body obtained by mapping a network entity; the network digital twin comprises a controller twin, a network node twin, a link twin and an application terminal twin;
step S2: the controller twin body generates a network topological structure serving the application terminal twin body based on the self state information reported by the network node twin body and the link twin body;
step S3: performing resource information request detection on the network topology structure to acquire resource parameter information of the network node twin and the link twin;
step S4: and determining a routing decision based on the network topology structure and the resource parameter information according to the service transmission requirement of the application terminal twin, and executing the routing decision in the network entity.
Specifically, in step S1, the network entity is mapped to obtain a network digital twin. Specifically, a controller, a network node, a link and an application terminal in the network entity are mapped correspondingly to obtain a controller twin, a network node twin, a link twin and an application terminal twin in the network digital twin respectively.
Specifically, in step S2, the following operations are performed:
step S21: the controller twins send out topology discovery signaling;
by way of example, topology discovery signaling may be implemented by: using LLDP (Link Layer Discovery Protocol ), a link layer discovery scheme is specified so that devices accessing the network can broadcast their various information in the form of an LLDP information frame structure to let other network devices obtain link and topology information. Illustratively, an information frame structure LLDP is shown in table 1.
TABLE 1 LLDP information frame structure
Wherein, each field has the following meaning:
lldp_ Multicast address: destination node address.
MAC address: the node address is sent.
88-CC: the frame types are arranged according to some specific rule, here exemplified as 0x88CC.
LLDPDU: information actually transmitted by the LLDP information frame.
FCS: a frame check sequence.
After the sending node and the destination node are determined, the link between the sending node and the destination node can be determined. Accordingly, after the network digital twin serving as the transmitting node and the destination node is determined, the corresponding link twin can be determined.
Step S22: the network node twin and the link twin respond to the topology discovery signaling and report own state information to the controller twin;
specifically, the controller twins issue topology discovery signaling to the network node twins and broadcast by the network node twins at each port, so that the topology discovery signaling can be propagated to other parts in the network; finally, the feedback information received by the controller twins comprises the self state information of the network node twins and the self state information of the link twins among the network node twins; therefore, each network node twin and link twin receives and responds to topology discovery signaling, and reports own state information to the controller twin. Specifically, the self state information of the network node twin at least comprises a port address, an ID number, a source MAC address and a destination MAC address; the own state information of the link twins includes the port addresses of the two network node twins forming the current link. Then, the controller twins record the received own state information of the network node twins. In this way all online network node twins of all network digital twins can be obtained, as well as the link twins of the process on state.
In addition, the controller twin monitors the states of the network digital twin and the link twin, and when the network state changes, such as the network node twin is online or offline and the link twin is connected or disconnected, the state information of the network node twin and the link stored at the controller twin is updated;
step S23: the controller twins respectively perform validity judgment on the state information of the network node twins and the link twins, and construct or update a network topological structure based on the set of the network node twins and the link twins through which the validity judgment passes.
Both the network node twins and the link twins may be described as network elements, and thus, illustratively, the validity determination includes: judging whether the network element serves the application terminal twin according to the self state information of the network element, and if so, judging the validity of the network element; otherwise, the validity judgment is not passed.
And judging the passing set of the network node twins and the link twins based on the validity, and constructing or updating a network topology structure under the form of the network architecture. Specifically, a network topology structure can be constructed after a set of a network node twin and a link twin through which validity judgment passes is obtained for the first time; when the network state changes, the network topology structure needs to be updated according to the set of the network node twins and the link twins which are judged to pass through by the validity judgment.
In step S3, the resource information request detection includes network loop delay parameter detection and network element information transceiving parameter detection. The resource parameter information includes: delay parameters, operating state information of the network node twins and the link twins, and remaining available resources of the network topology. Specifically, step S3 performs:
step S31: the controller twins initiate network loop delay parameter detection to the network topology structure, and delay parameters of network node twins and link twins in the network topology structure are obtained;
preferably, the network loop delay parameter detection includes unidirectional loop delay detection and bidirectional loop delay detection. Wherein, the liquid crystal display device comprises a liquid crystal display device,
unidirectional loop time delay detection refers to the process of sending unidirectional detection flow to each network element in the network topology structure, sending unidirectional detection flow in the process of passing through each network element, and finally returning to the controller twin body and calculating time delay; the bidirectional loop time delay detection refers to a process of sending bidirectional detection flow to network elements in a network topology structure, sending the bidirectional detection flow in the process of passing through each network element, and finally returning to a controller twin body and calculating time delay.
The specific process of unidirectional loop time delay detection is as follows:
step S311: acquiring a network element list (network elements comprise the network node twins and the link twins) supporting unidirectional loop detection flow in a network from the network topology by utilizing a communication mechanism between the network node twins and the link twins in the network topology, wherein the network element list is used as an alternative element set for unidirectional loop detection;
step S312: the controller twins send a trigger event to the network topology structure to carry out a unidirectional time delay detection process;
step S313: the detection triggering event transmitted by the network records unidirectional time information of the network digital twin and the link twin which flow through in the transmission process of the detection triggering event and returns the unidirectional time information to the controller twin;
step S314: the controller twins collect unidirectional time information, and feedback of each network node twins and link twins is synthesized to perform unidirectional loop time delay calculation.
The unidirectional time information collected by the controller twin body comprises unidirectional time information of each network element in the network element list supporting unidirectional loop detection flow, and the time delay information of each network element in the network element list supporting unidirectional loop detection flow can be obtained through unidirectional loop time delay calculation; specifically, the delay parameters of the network node twins include a transmission delay parameter and a processing delay parameter, and the delay parameters of the link twins include a transmission delay parameter.
The specific process of the bidirectional loop delay detection is as follows:
step S315: acquiring a network element list supporting a bidirectional loop detection flow in a network from a network topological structure by utilizing a communication mechanism between a network node twin and a link twin in the network topological structure, wherein the network element list is used as an alternative element set for bidirectional loop detection;
step S316: the controller twins send and execute the trigger event to the network topology structure, carry on the process of the two-way time delay detection;
step S317: the network-transmitted detection trigger event records the bidirectional time information of the network digital twin and the link twin which flow through in the transmission process of the network-transmitted detection trigger event and returns the bidirectional time information to the controller twin;
step S318: and the controller twins collect bidirectional time information, integrate feedback of each network node twins and link twins, and calculate bidirectional loop delay.
It should be noted that, the bidirectional time information collected by the controller twin body includes bidirectional time information of each network element in the network element list supporting the bidirectional loop detection flow, and the time delay information of each network element in the network element list supporting the bidirectional loop detection flow can be obtained through bidirectional loop time delay calculation; specifically, the delay parameters of the network node twins include a transmission delay parameter and a processing delay parameter, and the delay parameters of the link twins include a transmission delay parameter.
Step S32: the controller twins initiate network element information receiving and transmitting parameter detection to the network topological structure, and working state information of network node twins and link twins in the network topological structure is obtained; the working state information of the network node twins comprises port interaction data quantity and port starting or stopping states; the working state information of the link twins comprises the survival or failure state of the link; illustratively, the port interaction data volume includes: the number of received pkts packets, the number of transmitted pkts packets, the number of received bytes packets, the number of transmitted bytes packets, the number of reception errors, the number of transmission errors.
Step S33: based on the detected working state information, evaluating the flow rate of the network topology structure due to network service; summarizing the detected working state information, the time delay parameter and the flow rate to be used as the resource occupation condition of network service in the network topology structure;
in this step, for each port, the traffic rate of the corresponding port during the evolution over time is obtained based on the ratio of the difference in the number of transmitted bytes packets at two adjacent times to the time interval.
Step S34: and determining the remaining available resources of the network topology structure based on the resource occupation condition of the network service and the whole network resource prediction result of the network topology structure.
Specifically, in this embodiment, after the controller obtains relevant parameters of all network node twins and link twins in the network topology, the controller may integrate to obtain resource parameter information of all network node twins and link twins, which is used as a whole network resource prediction result of the network topology.
In step S4, specifically:
step S41: based on the network topology structure and the resource parameter information, a feasible transmission path is preliminarily determined according to the service transmission requirement of the application terminal twin;
specifically, according to the service transmission requirement of an application terminal twin, network node twin and link twin which do not meet the service transmission requirement such as residual available resources, delay parameters and the like in a network topology structure can be removed, and a feasible transmission path is preliminarily determined based on the residual network node twin and link twin which meet the service transmission requirement;
step S42: performing routing decision on the feasible transmission paths which are preliminarily determined, and determining an optimal transmission path;
illustratively, this embodiment presents two routing decision methods:
first, shortest path algorithm
In the shortest path algorithm, the shortest path between two points or the shortest path from a designated point to any point is calculated. G= (V, E) for a directed or undirected graph with positive link weights given, where V is the set of nodes in the graph and E is the set of links; if the node set V is divided into two sets S and T, where let s= { S } be the source node set, T contains all nodes except the set S, i.e. t=v-s= { T } be the destination node set, define d s,t Is the distance from the source node to the destination node. The basic idea is as follows:
(1) Setting an initial node s and a destination node t, if a link e exists between the initial node and the destination node s,t D is then s,t ＝e s,t Otherwise d s,t ＝∞。
(2) Definition of the definitionInitial value d s,t For all nodes { T } in T, =0, the node T' with the smallest distance from the starting node s is found by comparison, i.e.:
d s,t ＝d s,t +min{e s,t |t∈T}
d is then s,t That is, all nodes in the set T are closest to the starting node s. The vertex T' is deleted from T and added to set S as a new starting node S in set S. I.e.
s＝t′
(3) Continuing the step (2) until the destination node T in T is added to the set S, at which point d s,t Is the shortest path between the originating node s and the destination node t.
However, the shortest path routing algorithm has the application disadvantage that the algorithm may not fully utilize the path resources of the network, and the network utilization is low, for example, when other available data paths exist, data forwarding is still performed according to the original path, and a new data forwarding path is not considered to be reselected until the path is congested.
Second, dynamic equalization path algorithm
Dynamic balancing takes the flow as the minimum unit, and a depth priority method is used, so that all traffic from a source node is firstly transmitted to the highest-level node to be accessed, and then is continuously forwarded downwards until a destination node. For a newly generated flow in a network, the source address and destination address are first determined, from which the highest level node that the flow needs to access is determined. The task node is a node of the service access network, and after the service access, the hierarchy which the task node needs to access in the network is determined according to the relative position of the task node in the network. Specifically, each network node digital twin is divided into edge layer, convergence layer and core layer nodes from bottom to top according to the hierarchy, the controller twin is used as the top-layer controller, and the application terminal digital twin is used as the task node in the network. A schematic diagram of the network hierarchy is shown in fig. 2. In particular the number of the elements,
(1) The traffic between task nodes connected to the same edge layer node only needs to access the edge layer node;
illustratively, as shown in fig. 2, the task nodes h1 and h2 are connected to the same edge layer node e13, so that only the edge layer node e13 needs to be accessed in the interaction process of the task nodes h1 and h 2;
(2) Traffic between task nodes in the same array (shown in phantom in fig. 2) but connected to different edge layer nodes needs to access the sink layer nodes;
illustratively, as shown in FIG. 2, the sink layer nodes a5 and a6, and the edge layer nodes e13 and e14 are in the same array; in the interaction process of the task nodes h1 and h4, the sink layer nodes a5 and a6 and the edge layer nodes e13 and e14 need to be accessed.
(3) Traffic between task nodes located in different arrays requires access to core layer nodes.
Illustratively, as shown in fig. 2, in the interaction process of the task nodes h1 and h5, the core layer node c1 needs to be accessed in addition to the adjacent edge layer node and the convergence layer node.
The route is selected by using a dynamic balancing algorithm, only the route from the source node to the highest-level node to be accessed is needed to be selected, and once the highest-level node is determined, the route from the highest-level node to the destination node is also determined. The algorithm schedules traffic based on current network traffic statistics. The controller firstly receives information from the routing node and gives the information to the dynamic balancing module for processing. The dynamic balancing module extracts the matching field of the information, determines the source node and the destination node of the stream according to the source and destination address of the information and the network topology structure, calculates the highest level to be accessed, and then selects a complete transmission path for the information stream by a recursion calling method. Since the path from the highest node to the destination host is determined after the highest node is determined, only the best link is actually selected for the upstream direction.
This strategy takes into account the distribution of the overall resources in the network, and the overall path selected from the source to the destination may not be the shortest distance, but is an algorithm that achieves overall digital twin network optimization after balancing the overall network resources.
Step S43: and transmitting the route information of the optimal transmission path to a corresponding network node twin and a link twin in a network topology structure as a route reference for forwarding the service through the optimal transmission path, and executing the route decision in the network entity. In particular, the method comprises the steps of,
step S431: encapsulating the routing information determined in the routing decision according to a general standard protocol in the network to adapt to a general interface;
step S432: and distributing the routing information to a network node twin and a link twin which participate in the service transmission.
In this embodiment, the effects of the two routing decisions are also simulated, and the simulation mainly compares the average resource utilization of the network digital twin. The average resource utilization rate is defined as the ratio of the resources occupied by the actual transmission of the data stream to the sending rate, namely, the average value of the ratio of the obtained stream rate to the sending stream rate of the user side is counted at the control side. The higher the average resource utilization, the lighter the network congestion situation.
Wherein B is tx For the transmission resource of a certain stream, B rx And n is the number of counted streams, which is the actual occupied resources of the streams.
The average resource utilization simulation results are shown in fig. 3.
When the network congestion degree is low, the average resource utilization rate of the two algorithms is 100%, the shortest path algorithm starts to decline when the load is 0.4 along with the increase of the network congestion, the decline speed is the fastest, and the resource utilization rate is only about 73% when the network congestion degree is 0.7. The dynamic balancing algorithm starts to decline when the congestion degree is 0.45, and the decline speed is slower than that of the shortest path algorithm, so that the dynamic balancing algorithm represents overall resource planning of the whole digital twin network, and compared with the shortest path algorithm, the dynamic balancing algorithm has better unified resource optimizing capability.
Those skilled in the art will appreciate that all or part of the flow of the methods of the embodiments described above may be accomplished by way of a computer program to instruct associated hardware, where the program may be stored on a computer readable storage medium. Wherein the computer readable storage medium is a magnetic disk, an optical disk, a read-only memory or a random access memory, etc.
The present invention is not limited to the above-mentioned embodiments, and any changes or substitutions that can be easily understood by those skilled in the art within the technical scope of the present invention are intended to be included in the scope of the present invention.
Claims (10)
1. The network digital twin body resource optimization method based on dynamic balance is characterized by comprising the following steps of:
acquiring a network digital twin body obtained by mapping a network entity; the network digital twin comprises a controller twin, a network node twin, a link twin and an application terminal twin;
the controller twin body generates a network topological structure serving the application terminal twin body based on the self state information reported by the network node twin body and the link twin body;
performing resource information request detection on the network topology structure to acquire resource parameter information of the network node twin and the link twin;
and determining a routing decision based on the network topology structure and the resource parameter information according to the service transmission requirement of the application terminal twin, and executing the routing decision in the network entity.
2. The dynamic equalization-based network digital twin body resource optimization method of claim 1, wherein generating a network topology comprises:
the controller twins send out topology discovery signaling;
the network node twin and the link twin respond to the topology discovery signaling and report own state information to the controller twin;
the controller twins perform validity judgment on the state information of the network node twins and the link twins, and if the validity judgment is passed, a network topology structure is constructed or updated based on the set of the network node twins and the link twins which are passed by the validity judgment.
3. The dynamic equalization-based network digital twin body resource optimization method of claim 2, wherein the legitimacy determination comprises: judging whether the network element serves the application terminal twin according to the self state information of the network element, and if so, judging the validity of the network element; otherwise, the validity judgment is not passed;
the network element is a network node twine or a link twine.
4. A dynamic equalization based network digital twin body resource optimization method as defined in claim 2 or 3,
the self state information of the network node twin at least comprises a port address, an ID number, a source MAC address and a destination MAC address;
the self state information of the link twins comprises port addresses of two network node twins forming the current link.
5. The dynamic equalization-based network digital twin body resource optimization method of claim 4, wherein the resource information request probe comprises a network loop delay parameter probe and a network element information transceiving parameter probe.
6. The dynamic equalization-based network digital twin body resource optimization method of claim 5, wherein the resource parameter information comprises:
delay parameters, operating state information of the network node twins and the link twins, and remaining available resources of the network topology.
7. The dynamic balancing-based network digital twin resource optimization method according to claim 6, wherein the obtaining the resource parameter information of the network node twin and the link twin comprises:
the controller twins initiate network loop delay parameter detection to the network topology structure, and delay parameters of network node twins and link twins in the network topology structure are obtained;
the controller twins initiate network element information receiving and transmitting parameter detection to the network topological structure, and working state information of network node twins and link twins in the network topological structure is obtained;
based on the detected working state information, evaluating the flow rate of the network topology structure due to network service; summarizing the detected working state information, the time delay parameter and the flow rate to be used as the resource occupation condition of network service in the network topology structure; and determining the remaining available resources of the network topology structure based on the resource occupation condition of the network service and the whole network resource prediction result of the network topology structure.
8. The dynamic balance-based network digital twin body resource optimization method of claim 7,
the delay parameters of the network node twins comprise transmission delay parameters and processing delay parameters, and the delay parameters of the link twins comprise transmission delay parameters.
9. The dynamic balance-based network digital twin body resource optimization method of claim 8,
the working state information of the network node twins comprises port interaction data volume and port starting or stopping states; the working state information of the link twins comprises the survival or failure state of the link.
10. The dynamic equalization-based network digital twin resource optimization method of claim 9, wherein determining a routing decision according to the traffic transmission requirements of the application terminal twin comprises:
based on the network topology structure and the resource parameter information, a feasible transmission path is preliminarily determined according to the service transmission requirement of the application terminal twin;
performing routing decision on the feasible transmission paths which are preliminarily determined, and determining an optimal transmission path;
and transmitting the route information of the optimal transmission path to a corresponding network node twin and a link twin in a network topology structure as route references for forwarding the service through the optimal transmission path, and executing the route decision in the network entity.
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