Multicast routing and load balancing method applied to low earth orbit satellite network

Abstract

The invention discloses a multicast routing and load balancing method applied to a low earth orbit satellite network, which belongs to the field of low earth orbit satellite network communication, and comprises the following steps: acquiring topology and link state information of the low earth orbit satellite network, modeling a multicast tree construction problem into a joint path optimization game, and solving through a cooperative game routing algorithm; by introducing an incentive link cost allocation mechanism and combining with a dynamically adjusted cost weight, multicast path selection is optimized, link cost is effectively allocated, and load balancing is realized; and designing a composite cost function fusing link load and delay to calculate the total cost of the path. According to the method provided by the invention, an innovative and efficient solution is provided for the multicast routing and load balancing problems in the low earth orbit satellite network, and the transmission efficiency, the service quality, the network stability and the overall throughput of the multicast service in the low earth orbit satellite network can be enhanced. And obvious advantages are shown in the aspects of improving the network performance, guaranteeing the service quality and enhancing the network robustness.

Description

TECHNICAL FIELD

[0001] The present application belongs to the field of low earth orbit (LEO) satellite network communication, and more particularly relates to a multicast routing and load balancing method applied to a low earth orbit satellite network. BACKGROUND

[0002] Low earth orbit satellite networks have become a key technology path to support global communication, Internet of Things, remote sensing monitoring, and especially real-time video transmission and other applications, due to their inherent advantages of low delay, high bandwidth, and seamless global coverage. With the continuous expansion of the number of satellites in orbit and the explosive growth of emerging applications such as 4K / 8K ultra-high-definition video and virtual reality live streaming, the effective management of multicast traffic and the optimization of bandwidth utilization in satellite networks have become a higher requirement, and have become a core challenge to achieve efficient video distribution and other services.

[0003] Currently, there are many challenges in implementing efficient multicast services in LEO satellite networks. First, LEO satellite networks and ground networks have inherent differences in architecture and operating environment, and their topology structure frequently changes dynamically due to the high-speed movement of satellites, making it difficult for mature multicast technologies on the ground to be directly applicable to this network environment. For example, traditional IP multicast technologies, such as using a shortest path tree (SPT) to build a multicast routing, need to be frequently reconstructed under a dynamic topology, making it difficult to optimize bandwidth consumption. At the same time, the distributed nature of IP multicast limits the optimal perception of global network state and centralized resource scheduling, making it difficult to provide reliable QoS guarantees.

[0004] To address the above challenges, software-defined multicast (SDM) technology has been introduced into LEO satellite networks. SDM draws on the centralized control idea of software-defined networks (SDN), and collects global network information (such as topology, link load, delay, etc.) through a controller to dynamically optimize multicast tree construction, thereby improving bandwidth utilization and traffic scheduling accuracy. Existing research has explored the application of SDM to LEO satellite networks, such as using a Steiner tree algorithm to optimize the length of a multicast tree or to avoid obstacles in multicast routing.

[0005] Although SDM technology shows potential, current SDM research for LEO satellites still has deficiencies. First, many studies still mainly focus on the node positioning and construction optimization of the Steiner tree. However, this often leads to excessive utilization of part of the Inter-Satellite Link (ISL), thereby causing hotspot link congestion and poor network load balancing. Second, the Steiner tree has inherent limitations in multi-objective optimization, making it difficult to effectively balance bandwidth utilization, transmission delay, reliability and other key QoS indicators at the same time. Third, in the face of dynamic changes in satellite network topology, especially satellite node failure or link congestion, the Steiner tree-based method often lacks sufficient dynamic adaptability and rapid self-healing ability.

[0006] Therefore, how to realize efficient multicast routing and resource management in the case of dynamic changes in LEO satellite network topology and frequent link failures, while taking into account bandwidth, delay and load balancing and other multi-objective optimization, is a problem to be solved. SUMMARY

[0007] In view of the above defects or improvement needs of the prior art, the present application provides a multicast routing and load balancing method applied to a low-orbit satellite network, thereby solving the problems of low routing efficiency, unbalanced load, insufficient QoS guarantee and poor adaptability to network dynamics existing in the prior art multicast routing scheme.

[0008] To achieve the above-mentioned purpose, according to the first aspect of the present application, a multicast routing and load balancing method applied to a low-orbit satellite network is provided, comprising: S1, determining the network topology structure and link state information of the satellite constellation under the current time slice, mapping each satellite to a virtual node one by one, and constructing an undirected graph ; wherein, is a virtual node set, is a communication link set between virtual nodes; S2, according to the user request of the ground station, determining a source virtual node and a target virtual node set , regarding each target virtual node as a game participant to build a joint path optimization game model for cooperative game; Wherein, the goal of each game participant is to minimize the total cost of the path from to itself by selecting and adjusting the path from to The total cost of a path is the sum of the costs of all links in that path; the total cost of a link between any two adjacent virtual nodes is the weighted sum of the load rate and the link delay of that link, and this total cost is shared by all target virtual nodes that transmit data through that link; j=1,2,…,n; n is the number of target virtual nodes; S3, obtained by solving the joint path optimization game model. arrive The optimal path is determined and a multicast path routing table is generated, thereby obtaining a multicast tree that satisfies the user's request. The solution process includes: initialization arrive The path, total path cost, participating nodes and link load of each link; traversal Search from arrive If the total cost of the new path is lower than the total cost of the current path, then update... arrive The path, total path cost, participating nodes and link load of each link are not updated unless the path is found; the search continues from... arrive The game continues until no new path that reduces the total cost of the path can be found. If no new path can be found that reduces the total cost of the path, the current path is the optimal path, and the game ends.

[0009] According to a second aspect of the present invention, an electronic device is provided, comprising: a computer-readable storage medium and a processor; The computer-readable storage medium is used to store executable instructions; The processor is configured to read executable instructions stored in the computer-readable storage medium and execute the method as described in the first aspect.

[0010] According to a third aspect of the invention, a computer-readable storage medium is provided, the computer-readable storage medium storing computer instructions for causing a processor to perform the method as described in the first aspect.

[0011] According to a fourth aspect of the invention, a computer program product is provided, comprising a computer program or instructions that, when executed by a processor, implement the method described in the first aspect.

[0012] In summary, compared with the prior art, the above-described technical solutions conceived by this invention can achieve the following beneficial effects: 1. The multicast routing and load balancing method for low-orbit satellite networks provided by the application models the multicast tree construction problem as a joint path optimization game (JPOG). In the JPOG, the calculation of link cost not only considers the transmission delay and load of the link, but also considers the resource consumption and network topology of the link. This multi-dimensional link cost model enables the algorithm to make more accurate optimization decisions in complex network environments. In addition, the JPOG has a load balancing mechanism that can effectively balance the resource demands between different multicast groups by reasonably allocating resources, avoiding performance bottlenecks caused by overloading on some links. In this way, the algorithm optimizes the allocation of network resources while ensuring the performance of each multicast group is effectively guaranteed. In actual network application scenarios, due to the dynamic changes in the number of multicast groups, network traffic inevitably presents a characteristic of stage-wise uneven distribution. To address this issue, the JPOG can dynamically adjust resource allocation strategies in real time, continuously optimizing link load distribution to ensure fairness between different multicast groups and maximize network resource utilization. In this game model, each multicast target virtual node is considered a game participant whose goal is to minimize its total path cost by selecting the optimal path.

[0013] Furthermore, to promote cooperation and achieve global optimization, a link cost sharing mechanism is adopted: when multiple target virtual nodes' paths share a link, the cost of that link is shared by all target virtual nodes that pass through it. This mechanism encourages target virtual nodes to share link resources, thereby reducing their own costs and reducing overall network resource consumption.

[0014] In addition, the method uses a composite cost function that combines link load and delay to evaluate the quality of a link; link cost is positively related to the current load rate and transmission delay of the link, and low-cost links mean low load and low delay, which helps to improve network transmission efficiency and ensure quality of service (QoS). The link load rate reflects the degree of link congestion, while the link delay takes into account the transmission delay of data packets and the queuing delay on satellite nodes. By adjusting the weight coefficients of load and delay in the cost function, optimization can be performed according to different application requirements.

[0015] 2、As a further preferred scheme, the method provided by the application adopts a two-stage A* path generation algorithm based on Manhattan distance as a stage switching condition in path search, which dynamically switches the search strategy according to the Manhattan distance between the current node and the target virtual node: in the initial stage of path search (when the distance to the target virtual node is far), a breadth-first strategy is adopted to explore multiple directions of the adjacent nodes in order to quickly find a potential low-cost path; when approaching the target virtual node, the search is focused on the direction towards the target virtual node in order to reduce redundant path expansion and improve path generation efficiency.

[0016] 3、As a further preferred scheme, the method provided by the application adopts a lightweight member management mechanism and a fault recovery mechanism. The member management mechanism can handle the dynamic joining and exiting of multicast group members, independently execute the CGMR algorithm to access the existing session when a new member joins, and orderly release resources when a member exits to avoid affecting other members; through the member management mechanism, the dynamic joining and exiting of multicast group members are managed to ensure the stability of the session and the effective use of resources. When a link fault is detected, the fault recovery mechanism sets the cost of the fault link to infinity through the SDM controller, forces the path generation algorithm to avoid the fault link, and allows the affected nodes to perform local path re-planning, thereby quickly recovering communication, enhancing the stability and throughput of the network, realizing the fast self-healing of communication services and guaranteeing the overall stability of the network. BRIEF DESCRIPTION OF DRAWINGS

[0017] Figure 1 A flowchart of a multicast routing and load balancing method applied to a low-orbit satellite network provided by an embodiment of the application.

[0018] Figure 2 A mapping relationship diagram of a network topology and a virtual node provided by an embodiment of the application.

[0019] Figure 3 A flowchart of a CGMR algorithm provided by an embodiment of the application.

[0020] Figure 4 A schematic diagram of a two-stage A* path generation algorithm provided by an embodiment of the application.

[0021] Figure 5 A multicast routing schematic diagram. DETAILED DESCRIPTION

[0022] In order to make the objects, technical solutions and advantages of the present application clearer, the present application will be further described in detail below with reference to the drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and should not be used to limit the present application. In addition, the technical features involved in the various embodiments of the present application described below can be combined with each other as long as they do not conflict with each other.

[0023] The embodiment of the present application provides a multicast routing and load balancing method applied to a low-orbit satellite network, as shown in the formula (I), comprising the steps of: Figure 1 S1, determining the network topology structure and link state information of a satellite constellation under a current time slice, mapping each satellite to a virtual node one by one, and constructing an undirected graph ; wherein, is a virtual node set, is a communication link set between virtual nodes; S2, determining a source virtual node and a target virtual node set according to a user request of a ground station, regarding each target virtual node as a game participant to construct a joint path optimization game model for cooperative game; wherein, the target of each game participant is to minimize the total path cost of the path from to itself by selecting and adjusting the path from to itself; the total path cost of the path from to is the sum of the costs of all links in the path; the total link cost of the link between any two adjacent virtual nodes is the weighted sum of the load rate and the link delay of the link, and the total link cost is shared by all target virtual nodes that transmit data through the link; j=1,2,…,n; n is the number of target virtual nodes; S3, obtaining the optimal path from to by solving the joint path optimization game model and generating a multicast path routing table, so as to obtain a multicast tree satisfying the user request; wherein, the solving process comprises: initializing the path from to , the total path cost, and the participating nodes and link load of each link; traversing , searching for a new path from to , if the total path cost of the new path is lower than the total path cost of the current path, updating the path from to , the total path cost, and the participating nodes and link load of each link, otherwise, not updating; continuously searching for a new path from to A new path is found until no new path can be found to reduce the total cost of the path, then the current path is the optimal path, and the game ends.

[0024] The method provided by the embodiment of the application constructs a cooperative game-theoretic multicast routing (CGMR) algorithm, which is applied to multicast tree construction in a low earth orbit satellite network of a software defined multicast architecture. The typical architecture is divided into three levels of architecture of an application data layer, a control layer and an application service layer: the data layer is composed of a LEO satellite constellation and inter-satellite links (ISLs) and satellite-ground links, and undertakes an actual data packet forwarding task; the control layer is deployed in a ground control station, has global network topology, link state and traffic information, and the CGMR algorithm for calculating the cooperative game-theoretic multicast routing CGMR algorithm proposed in the application runs in the layer and issues control instructions to the data layer satellite; and the application service layer is an interface of a ground terminal user or a base station accessing the satellite network, and provides multicast service access.

[0025] One is a Walker constellation composed of satellites. The satellites are connected with each other through in-orbit plane inter-satellite links and cross-orbit plane inter-satellite links (for example, X-type links), and form a mesh-shaped topological structure. In view of the topological dynamic problem caused by high-speed movement of the satellites, the embodiment of the application introduces the concept of time slicing (Time Slice), and in each time slicing period, the network topology is regarded as relatively stable. Meanwhile, a virtual node mapping technology is adopted, and a virtual rectangular network is constructed above the physical satellite layer , as a virtual network dimension parameter. As shown in Figure 2 , each physical satellite is mapped to a virtual node , and the dynamic binding relationship between the satellite and the virtual node is described through an indication function . In a single time slice, the network topology is modeled as an undirected graph , where is a virtual node set, and is a virtual node intercommunication link set. With the passage of time, the virtual topological structure maintains logical static characteristics, and when the physical satellites move, they will be re-bound with the virtual nodes, and link state information such as topology configuration, satellite load and energy consumption is synchronously updated.

[0026] The method provided by the embodiment of the application aims at efficiently generating multicast routing in each time slice, and realizing minimization of inter-satellite link (ISL) cost.

[0027] The link between two nodes is defined​ The cost of a link is the weighted sum of the load ratio and the link delay of the link. The link delay is mainly composed of the transmission delay and the queuing delay .

[0028] Preferably, the queuing delay of a link in a satellite network is modeled by using the M / M / 1 queuing model, i.e., .

[0029] The total cost of a link is calculated by the following formula:

[0030]

[0031]

[0032] wherein denotes the average packet size, denotes the average load of the link, denotes the capacity of the link, denotes the bandwidth of the link, denotes the average arrival rate of the packets, denotes the service rate of the link, denotes the load ratio of the link, is a preset weight coefficient.

[0033] In the traditional unicast path problem, the path selection of each target virtual node is usually only aimed at minimizing its own cost. However, in the scenario of satellite multicast, the situation becomes more complex because multiple paths often need to share common physical links. This resource sharing characteristic causes the path decision of any node to not only affect its own cost, but also directly affect the costs of other nodes sharing the link. Therefore, in order to solve this optimization problem of mutual influence, the multicast tree construction problem can be modeled as a joint path optimization game model (JPOG).

[0034] In a specific multicast service, the source virtual node is defined as , and the set of target virtual nodes is (wherein n is the number of target virtual nodes), wherein each target virtual node is regarded as a game player, j = 1, 2, …, n, and the goal of each player is to minimize its own total path cost by independently deciding and adjusting its path from the source node to itself.

[0035] ​​Therefore, for the target virtual node The path selection optimization problem can be described as follows:

[0036] in, Indicates from the source node To the target virtual node The full path, Indicates the formation of a path One of the links, This is the cost of the link.

[0037] To promote global path optimization, the method provided in this embodiment of the invention introduces a link cost sharing mechanism: when the path of a target virtual node contains a link, the target virtual node is added to the set of participating nodes of the link, and the cost allocated to the target virtual node on the link is the total cost of the link divided by the total number of target virtual nodes passing through the link.

[0038] Under this mechanism, a physical link Total cost The cost will be shared by all target virtual nodes sharing the link, rather than being borne independently by a single user. Specifically, a "participant" list is maintained for each link to track all target virtual nodes sharing the link. Therefore, from the source node... To the target virtual node full path Total cost It is the sum of the shared costs of all links along its path:

[0039] in, Indicates link The list of "participants", i.e., through the link The target set of virtual nodes for data transmission Representing a path The total cost.

[0040] This cost-sharing mechanism aims to incentivize each target virtual node to choose shared link resources. The more nodes that share, the lower the cost per node, thus reducing not only its own total path cost but also the cost for other nodes sharing the link, creating a "cooperative game" situation. When a target virtual node... To minimize its total path cost, its current path is... Change to a new path This path change process typically involves one or more new links. The addition (i.e.) and ), and one or more old links Removal (i.e.) and Accordingly, the target virtual node It will be added to all newly joined links The set of participating nodes In, and from all the old links that were removed The set of participating nodes Removed from the middle.

[0041] To mitigate the risk of frequent path changes (i.e., path oscillations), as a further optimization, after the path is updated, the target virtual node... The method for calculating the total path cost will be adjusted. Its new total path cost... Given by the following formula:

[0042]

[0043] in, Indicates the updated path Links in The weighting ratio Indicates the updated path The total cost.

[0044] Weighting ratio The design allows nodes with higher total path costs to receive relatively higher weight on shared links, while reducing the risk of frequent path selection changes (i.e., path oscillations).

[0045] For multicast services JPOG achieves Pure Strategy Nash Equilibrium (PSNE) through an iterative method. The overall framework of the Cooperative Game Multicast Routing (CGMR) algorithm provided in this embodiment is shown in Table 1.

[0046]

[0047] The specific process of the algorithm is as follows: Figure 3 As shown, the CGMR process includes the following details: Initialization: The ground control station initializes the satellite network as an undirected graph. Each target virtual node As a participant in the game, and to initialize the path list. Initialize the list of participant nodes in the link. and link load Each target virtual node randomly selects a shortest path from available paths as an initial path, and calculates total cost of links and exists in the cost list .

[0048] Path selection and list update: when the target virtual node selects a path, if the path contains a link , the participant node list is updated according to , and the link load is updated according to .

[0049] Optimal path search and replacement: each target virtual node is traversed, the optimal path is searched through a two-stage A* algorithm, and the total cost of links is calculated . If , the path is replaced and the participant node list is updated according to the rules :

[0050] The participant nodes in the old path participant node list are moved to the new path participant node list , and the updated path is stored in the path list p, which is dynamically updated with the change of the path, and is not updated if the cost is the same.

[0051] Load and cost update: at the end of each round of path selection, the related link load is updated, and the total cost of paths of each target virtual node is recalculated . The CGMR process continues until each target virtual node cannot find a better path that can reduce its own cost, at which time the path is the optimal path, and the game ends.

[0052] To improve the path search efficiency, preferably, when performing path search, a two-stage A* algorithm is used to perform optimal path search. In the initial stage of path search, when the Manhattan distance between the current virtual node and the target virtual node is greater than or equal to a preset threshold, it is in the first stage, a breadth-first strategy is used to explore multiple directions of adjacent nodes; when the Manhattan distance is less than the preset threshold, it is in the second stage, and a search is performed in a direction focusing on the target virtual node.

[0053] Specifically, the two-stage A* algorithm improves the path generation efficiency by dynamically adjusting the neighbor node traversal strategy and using different search strategies in different stages. The path search process is divided into two stages: a first stage phase1 and a second stage phase2, and the stage switching condition is defined as follows:

[0054] wherein,​​ is the Manhattan distance from the current neighbor node to the target virtual node, is the adjustment factor that controls the entering of the later stage.

[0055] The two-stage A* algorithm determines whether to switch to the later stage according to the distance between the current node and the target virtual node, and then adjusts the search focus. Figure 4 As shown in the figure, according to this rule: when the current node searched is still far away from the final target virtual node, it is in the phase1 stage, at this time, all the neighbor nodes in the current node are explored, and extensive search is performed. The purpose of this is to avoid locking a direction too early, so that the global and possible low-cost path can be found faster; when the current node searched is close to the target virtual node, it is switched to the phase2 stage, and the search strategy becomes more purposeful, and the exploration is concentrated in the direction towards the target virtual node. This reduces the redundant calculation and path expansion in the irrelevant direction, thereby significantly speeding up the accurate search near the end point.

[0056] After obtaining the optimized path of each target virtual node through the CGMR algorithm, the ground control station is configured to perform a path integration operation to generate a multicast path routing table. The system adopts a distributed routing mechanism, as shown in the figure, a communication connection is established between the ground control station and the related satellite, and the corresponding next-hop forwarding information is sent to each satellite node, without obtaining the global routing topology, thereby realizing the technical effects of reducing satellite signaling overhead and enhancing system scalability. Figure 5

[0057] The method provided by the application collects satellite network topology structure, link load and ground station user request data in real time; according to the above data, the satellite network topology structure is converted into a standardized network model and a game scenario, according to the constructed model, the path selection game between each target virtual node is simulated through the cooperative game routing (CGMR) algorithm, and a global optimal multicast routing scheme is finally calculated by using a cost allocation mechanism. The scheme is converted into a specific next-hop forwarding table and distributed to each satellite node for execution.

[0058] As a further preferred scheme, when a new member node dynamically joins a stable multicast session, there is no need to rebuild the complete multicast tree structure. Since rebuilding the multicast tree may cause a dramatic fluctuation in link state, increase the path reconstruction calculation overhead, and even cause a temporary decrease in network performance, the method provided by the embodiment of the application adopts the following new member management mechanism: The new member node independently executes the CGMR algorithm based on the real-time obtained network link load and delay state parameters, selects the optimal access link through path total cost optimization calculation, and accesses the existing multicast session in a way of minimizing the total path cost, thereby effectively avoiding excessive disturbance to the overall multicast tree structure;​ When a member node needs to exit the multicast session due to device failure, unstable link or network environment change, the control layer is configured to manage the link corresponding to the node to maintain the overall stability of the network. The specific implementation is: when the member node exits, the control layer triggers the link disconnection process, which will recursively disconnect the related links on the path of the member node to release the corresponding network resources. It needs to be particularly limited that if it is detected that a certain link is still used by other member nodes, the disconnection operation of the link is inhibited to avoid interference with the normal communication of other multicast members. This mechanism aims to maintain the stability of the multicast tree while minimizing the negative impact caused by node exit. During the link removal process, the control layer queries the update of the participating node list of each link to determine whether the corresponding link needs to be disconnected, ensuring the accuracy and efficiency of the management measures.

[0059] The link failure recovery mechanism is a key technical means to ensure the stable operation of the LEO satellite network. Influenced by factors such as solar activity, satellite maintenance and network environment change, link failure is difficult to completely avoid in actual operation. When a link fails, the multicast service path that depends on the link needs to be reconstructed in a timely manner to ensure the continuity of communication services and the overall stability of the network. As a further preferred solution, the method provided by the embodiment of the application adopts the following link failure recovery mechanism: When a link fails, the software-defined multicast (SDM) controller sets the cost or load parameter of the failed link to infinity to force the path generation algorithm to avoid the failed link; Allow the affected nodes to perform local path re-planning (the paths of unaffected nodes remain unchanged), and during path reconstruction, the search range is expanded to all adjacent nodes, rather than using only the two-stage A* search strategy.

[0060] Specifically, to minimize the impact of link failure on the overall network performance, the path reconstruction operation is limited to multicast target virtual nodes directly affected by the failure, and large-scale adjustment of the overall network path layout is not required. Under the unified management architecture of the SDM controller, the controller is configured to set the load parameter of the failed link to infinity, and the parameter configuration forces the path generation algorithm to automatically avoid the failed link, effectively preventing communication interruption caused by link failure.

[0061] To improve the efficiency of path reconstruction, the path generation algorithm under the link failure scenario usually disables the shortest path search strategy. At this time, instead of using the two-stage A* search strategy, the search range is directly expanded to all adjacent nodes. Since the shortest path search algorithm relies on complete and stable network state information, if the search path contains a failed link, it may cause the path calculation to fail to converge or even fail to generate, therefore, by expanding the search range, the failed link can be effectively avoided, ensuring the reliability of path generation and improving the success rate and efficiency of path reconstruction.

[0062] The method provided by the embodiment of the present application models the multicast tree construction problem as a joint path optimization game (JPOG), and innovatively proposes a cooperative game multicast routing (CGMR) algorithm. The algorithm effectively encourages link sharing between target virtual nodes by introducing a link cost allocation mechanism, thereby optimizing multicast path selection and achieving effective utilization and load balancing of network resources. Moreover, the complex cost function designed by the present application fuses link load and delay, and combines an efficient two-stage A* path generation algorithm, which significantly improves network transmission efficiency and guarantees quality of service (QoS). In addition, the method provided by the embodiment of the present application adopts a lightweight member management mechanism and a failure recovery mechanism, so that the system can quickly adapt to the dynamic changes of the low-orbit satellite network topology and link failures, greatly enhancing the stability, reliability and overall throughput of the network.

[0063] To sum up, the method provided by the embodiment of the present application provides an innovative and efficient solution to the multicast routing and load balancing problem in a low-orbit satellite network, which has significant advantages in improving network performance, guaranteeing quality of service and enhancing network robustness.

[0064] The embodiment of the present application provides a multicast routing and load balancing system applied to a low-orbit satellite network, which can be deployed on an SDM architecture, and the system comprises a computer readable storage medium and a processor. The computer readable storage medium is used to store executable instructions. The processor is used to read the executable instructions stored in the computer readable storage medium, and execute the method according to any one of the above embodiments.

[0065] The embodiment of the present application provides a computer readable storage medium, which stores computer instructions, and the computer instructions are used to make the processor execute the method according to any one of the above embodiments.

[0066] The embodiment of the present application provides a computer program product, which comprises a computer program or instructions, and the computer program or instructions are executed by the processor to realize the method according to any one of the above embodiments.

[0067] Those skilled in the art can easily understand that the above description is only a preferred embodiment of the present application, and is not used to limit the present application, and any modification, equivalent replacement and improvement made within the spirit and principle of the present application should be included in the protection scope of the present application.

Claims

1. A multicast routing and load balancing method applied to low-Earth orbit satellite networks, characterized in that, include: S1: Determine the network topology and link status information of the satellite constellation under the current time slice, map each satellite to a virtual node, and construct an undirected graph. ;in, A set of virtual nodes A set of communication links between virtual nodes; S2, based on the user request from the ground station, determines the source virtual node. and the target virtual node set Each target virtual node is regarded as a game participant, and a joint path optimization game model is constructed for cooperative game. In this game, the goal of each participant is to autonomously choose and adjust their strategy from... The path to itself, in order to minimize its total path cost; from arrive The total cost of a path is the sum of the costs of all links in that path; the total cost of a link between any two adjacent virtual nodes is the weighted sum of the load rate and the link delay of that link, and this total cost is shared by all target virtual nodes that transmit data through that link; j=1,2,…,n; n is the number of target virtual nodes; S3, obtained by solving the joint path optimization game model. arrive The optimal path is determined and a multicast path routing table is generated, thereby obtaining a multicast tree that satisfies the user's request. The solution process includes: initialization arrive The path, total path cost, participating nodes and link load of each link; traversal Search from arrive If the total cost of the new path is lower than the total cost of the current path, then update... arrive The path, total path cost, participating nodes and link load of each link are not updated unless the path is found; the search continues from... arrive The game continues until no new path that reduces the total cost of the path can be found. If no new path can be found that reduces the total cost of the path, the current path is the optimal path, and the game ends.

2. The method as described in claim 1, characterized in that, from arrive path Total path cost The calculation formula is: in, To pass through the link The target set of virtual nodes for data transmission To pass through the link The number of target virtual nodes for data transmission. for One of the links in the chain.

3. The method as described in claim 2, characterized in that, from arrive path The path is Updated to back, arrive Total path cost The calculation formula is: in, , for Total path cost, for Total path cost, From arrive The path, for The target virtual node in To pass through the link The target set of virtual nodes for data transmission for One of the links in, For link The total cost.

4. The method according to any one of claims 1-3, characterized in that, In step S3, the path search process is divided into a first stage and a second stage; the first stage uses a breadth-first search strategy to search for paths, and the second stage explores the direction towards the target virtual node. Wherein, if the Manhattan distance from the current neighbor node to the target virtual node is Satisfying the relation If the path search is successful, the path search process is in the first stage; otherwise, it is in the second stage. As a regulating factor, , The dimension parameters are used to map each satellite to a virtual node to form a virtual network.

5. The method as described in claim 1, characterized in that, Also includes: When a new satellite virtual node joins, it connects to the existing multicast session by minimizing its total path cost, based on real-time link status information, without changing the optimal path from the source node to other virtual nodes. When a satellite virtual node exits, the links on its path are recursively disconnected; however, if a link on the path is still being used by another satellite virtual node, it is not disconnected.

6. The method as described in claim 1, characterized in that, Also includes: When a link fails, the total cost of the failed link is set to infinity, and the affected satellite virtual nodes expand the path search range to all adjacent nodes to replan the path.

7. The method as described in claim 1, characterized in that, The link delay between any two virtual nodes includes transmission delay and queuing delay.

8. A multicast routing and load balancing system for low-Earth orbit satellite networks, characterized in that, include: Computer-readable storage media and processors; The computer-readable storage medium is used to store executable instructions; The processor is configured to read executable instructions stored in the computer-readable storage medium and execute the method as described in any one of claims 1-7.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions for causing a processor to perform the method as described in any one of claims 1-7.

10. A computer program product, comprising a computer program or instructions, characterized in that, When the computer program or instructions are executed by a processor, they implement the method as described in any one of claims 1-7.

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