General control IP core scheduling matrix Spine-Leaf architecture routing convergence optimization method and system, electronic equipment and storage medium

Abstract

The invention provides a general control IP core scheduling matrix Spine-Leaf architecture routing convergence optimization method and system, electronic equipment and a storage medium, and the general control IP core scheduling matrix Spine-Leaf architecture routing convergence optimization method comprises the following steps: when an IP core scheduling matrix general control Spine-Leaf architecture changes, calculating and reconstructing a networking topological structure of the Spine-Leaf architecture; performing optimization multicast routing convergence calculation based on the reconstructed Spine-Leaf architecture networking topological structure to obtain a routing forwarding path, and obtaining a multicast stream switching result according to the routing forwarding path; performing data forwarding according to a multicast stream switching result; the routing convergence optimization system comprises a topological structure reconstruction module, a convergence calculation module and a transmission module. The routing convergence optimization method has the beneficial effects that the routing convergence optimization method is simple, and by optimizing routing convergence, when a general control IP core scheduling matrix Spine-Leaf architecture is changed, a response can be quickly made, multicast streams can be switched, and the continuity of services is ensured; the method is suitable for the routing convergence field.

Description

Technical Field

[0001] The present application relates to the technical field of routing convergence, and specifically to a routing convergence optimization method, system, electronic device, and storage medium for a master control IP core scheduling matrix Spine-Leaf architecture. Background Art

[0002] In the digital age, data centers have become the core infrastructure for enterprises to store, process and transmit data. However, with the rapid development of technologies such as cloud computing, big data, and the Internet of Things, data centers are facing unprecedented challenges. The traditional master control IP core scheduling matrix is ​​a single device, with bandwidth bottlenecks, limited scalability and other problems. It cannot meet the current requirements for the richness of master control services, and requires the use of a hybrid spine-leaf architecture with multiple devices and port boards.

[0003] The Spine-Leaf architecture has many advantages, such as: High scalability: The Spine-Leaf architecture adopts a modular design, which allows you to easily add new Leaf routers and Spine routers to expand the network scale. This flexibility allows data centers to quickly adjust the network structure according to business needs to meet the growing data transmission needs. High performance: Since the Spine-Leaf architecture adopts a fully connected mesh topology, data can choose the shortest path for forwarding during transmission, thereby reducing network latency and congestion. In addition, the high-bandwidth connection between Spine routers and Leaf routers also ensures high-speed data transmission. High availability: Since each Leaf switch is connected to all Spine switches, when a Spine router or Leaf router fails, other switches can continue to work, ensuring the stability and availability of the network. Simplified management: The flat design of the Spine-Leaf architecture makes the network hierarchy clearer and management simpler. Administrators can manage the entire network through a unified configuration and management interface, reducing management costs and complexity. With the expansion of data center scale and the widespread application of virtualization technology, the Spine-Leaf architecture has been widely used in data center networks. It can be combined with virtualization technology to realize functions such as virtual machine migration and load balancing. In addition, the Spine-Leaf architecture can also be combined with SDN technology to realize the programmability and automated management of the network.

[0004] However, in the Spine-Leaf architecture, if a node or link fails, the stream link needs to be switched to ensure service availability. Currently, the convergence performance is limited by the switching interface of Huawei routers, and fast batch switching cannot be achieved, resulting in a long switching time, affecting service continuity. Therefore, routing convergence needs to be optimized. Summary of the invention

[0005] In order to solve one of the above-mentioned technical defects, the present application provides a routing convergence optimization method, system, electronic device, and storage medium for a master control IP core scheduling matrix Spine-Leaf architecture.

[0006] According to a first aspect of the present application, a method for optimizing routing convergence of a master control IP core scheduling matrix Spine-Leaf architecture is provided, including: When the Spine-Leaf architecture of the master control IP core scheduling matrix changes, calculate and rebuild the network topology of the Spine-Leaf architecture; Based on the reconstructed Spine-Leaf architecture networking topology, the multicast routing convergence calculation is optimized to obtain the routing forwarding path, and the multicast stream switching result is obtained according to the routing forwarding path; Data is forwarded based on the multicast stream switching result.

[0007] Preferably, the optimizing multicast routing convergence calculation based on the reconstructed Spine-Leaf architecture networking topology structure to obtain a routing forwarding path, and obtaining a multicast stream switching result according to the routing forwarding path specifically includes: Based on the reconstructed Spine-Leaf architecture networking topology, the routing forwarding path is calculated; Build a routing table based on the routing forwarding path; Divide the multicast stream into high-priority multicast stream and low-priority multicast stream according to the task characteristics; Switching forwarding table entries of all multicast streams based on the division result of the multicast streams and the routing table specifically includes: preferentially switching high-priority multicast streams, and then switching low-priority multicast streams.

[0008] More preferably, the dividing of the multicast stream into high priority multicast stream and low priority multicast stream according to the task characteristics specifically includes: Preliminarily classify and sort the multicast streams according to the task characteristics; Prioritize the multicast streams according to the preliminary classification and sorting results, and divide them into preliminary high-priority multicast streams and preliminary low-priority multicast streams; Determine whether the proportion of the preliminary high priority multicast stream in all multicast streams does not exceed a preset threshold, and if so, use the preliminary high priority multicast stream as the final high priority multicast stream, and use the preliminary low priority multicast stream as the final low priority multicast stream; Otherwise, the multicast streams are prioritized again according to the preliminary classification and sorting results until the proportion of the preliminary high-priority multicast streams in all multicast streams does not exceed a preset threshold.

[0009] Preferably, changes occur in the Spine-Leaf architecture of the master control IP core scheduling matrix, including at least one of the following situations: failure of a node or link in the Spine-Leaf architecture, disconnection or deletion of a Spine router or Leaf router in the Spine-Leaf architecture.

[0010] According to the second aspect of the present application, a master control IP core scheduling matrix Spine-Leaf architecture routing convergence optimization system is provided, including: a module for implementing the master control IP core scheduling matrix Spine-Leaf architecture routing convergence optimization method as described in any of the above items.

[0011] Preferably, the routing convergence optimization system includes: The topology reconstruction module is used to calculate and rebuild the network topology of the Spine-Leaf architecture when the Spine-Leaf architecture of the master control IP core scheduling matrix changes; The convergence calculation module is used to optimize the multicast routing convergence calculation based on the reconstructed Spine-Leaf architecture networking topology, obtain the routing forwarding path, and obtain the multicast flow switching result according to the routing forwarding path; The transmission module is used to forward data according to the multicast stream switching result.

[0012] More preferably, the convergence calculation module comprises: The path calculation unit is used to calculate the routing forwarding path based on the network topology of the reconstructed Spine-Leaf architecture; A routing table building unit, used to build a routing table according to a routing forwarding path; A division unit, used for dividing the multicast stream into: a high priority multicast stream and a low priority multicast stream according to task characteristics; The switching unit is used to switch the forwarding table items of all multicast streams based on the division result of the multicast streams and the routing table, specifically including: preferentially switching the high priority multicast streams, and then switching the low priority multicast streams.

[0013] More preferably, the division unit comprises: A preliminary classification and sorting unit, used for preliminary classification and sorting of multicast streams according to task characteristics; A preliminary classification unit, used to classify the multicast stream into a preliminary high priority multicast stream and a preliminary low priority multicast stream according to the preliminary classification and sorting result; A determination unit, configured to determine whether the proportion of the preliminary high priority multicast stream in all multicast streams does not exceed a preset threshold; a priority determination unit, configured to use the preliminary high priority multicast stream as the final high priority multicast stream and the preliminary low priority multicast stream as the final low priority multicast stream when the proportion of the preliminary high priority multicast stream in all multicast streams does not exceed a preset threshold; The re-dividing unit is used to re-divide the priority of the multicast streams according to the preliminary classification and sorting result when the proportion of the preliminary high priority multicast streams in all multicast streams exceeds a preset threshold, until the proportion of the preliminary high priority multicast streams in all multicast streams does not exceed the preset threshold.

[0014] According to a third aspect of the present application, an electronic device is provided, including: Memory; Processor; and Computer programs; Wherein, the computer program is stored in the memory and is configured to be executed by the processor to implement the routing convergence optimization method of the master control IP core scheduling matrix Spine-Leaf architecture as described in any one of the above items.

[0015] According to the fourth aspect of the present application, a computer-readable storage medium is provided, on which a computer program is stored; the computer program is executed by a processor to implement the routing convergence optimization method of the master control IP core scheduling matrix Spine-Leaf architecture as described in any of the above items.

[0016] In the present application, when the Spine-Leaf architecture of the master control IP core scheduling matrix changes, the corresponding topology structure will also change. By rebuilding the topology structure and then optimizing the routing convergence for calculation, the routing forwarding path is obtained, and then the multicast stream switching result is obtained based on the routing forwarding path, and data is forwarded based on the multicast stream switching result. The provided routing convergence optimization method is simple. By optimizing the routing convergence, when the Spine-Leaf architecture of the master control IP core scheduling matrix changes, it can respond quickly and switch the multicast stream, thereby ensuring business continuity.

[0017] As a new type of network architecture, Spine-Leaf architecture has significant advantages and broad application prospects in data center networks. In the field of radio and television, Spine-Leaf architecture has also fully demonstrated its value, and has done a good job in routing convergence optimization, making it play a more important role in the master control IP system, promoting the reform of television production and transmission, and contributing to the better completion of large-scale live broadcast tasks in the future.

[0018] Other features and advantages of the present application will be described in the following description, and partly become apparent from the description, or understood by practicing the present application. The purpose and other advantages of the present application can be realized and obtained by the contents indicated in the written description and the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings: Figure 1 A flow chart of the master control IP core scheduling matrix Spine-Leaf architecture routing convergence optimization method provided in this application; Figure 2 for Figure 1 A flow chart of optimizing multicast routing convergence calculation in; Figure 3 Schematic diagram of optimized multicast routing convergence for single cut and group cut provided in this application; Figure 4 for Figure 3 Comparison diagram of optimized multicast routing convergence between single cut and group cut; Figure 5 for Figure 2 Schematic diagram of the process of dividing multicast streams according to task characteristics; Figure 6 A functional structural diagram of the master control IP core scheduling matrix Spine-Leaf architecture routing convergence optimization system provided for this application; Figure 7 for Figure 6 Schematic diagram of the functional structure of the convergence calculation module; Figure 8 for Figure 7 Schematic diagram of the functional structure of the division unit.

[0020] In the figure: 1 is single-cut convergence, 2 is group-cut convergence, 10 is a topology reconstruction module, 20 is a convergence calculation module, 30 is a transmission module, 201 is a path calculation unit, 202 is a routing table construction unit, 203 is a division unit, 204 is a switching unit, 2031 is a preliminary classification and sorting unit, 2032 is a preliminary division unit, 2033 is a judgment unit, 2034 is a priority determination unit, and 2035 is a re-division unit. DETAILED DESCRIPTION

[0021] In order to make the technical solutions and advantages in the embodiments of the present application more clearly understood, the exemplary embodiments of the present application are further described in detail below in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present application, rather than an exhaustive list of all the embodiments. It should be noted that the embodiments in the present application and the features in the embodiments can be combined with each other without conflict.

[0022] like Figure 1 As shown, in order to solve the above problems, the present application provides a method for optimizing routing convergence of a master control IP core scheduling matrix Spine-Leaf architecture, including: When the Spine-Leaf architecture of the master control IP core scheduling matrix changes, calculate and rebuild the network topology of the Spine-Leaf architecture of the master control; Based on the reconstructed Spine-Leaf architecture networking topology, the multicast routing convergence calculation is optimized to obtain the routing forwarding path, and the multicast stream switching result is obtained according to the routing forwarding path; Data is forwarded based on the multicast stream switching result.

[0023] Specifically, before the Spine-Leaf architecture of the master control IP core scheduling matrix changes, the topology structure of the original Spine-Leaf architecture of the master control IP core scheduling matrix is ​​stored; the topology structure of the reconstructed Spine-Leaf architecture of the master control IP core scheduling matrix has changed relative to the topology structure of the original Spine-Leaf architecture of the master control IP core scheduling matrix, so routing convergence and multicast stream switching can be performed according to the corresponding changes. The modification amount is relatively controllable and has little impact on stability.

[0024] In the present application, when the Spine-Leaf architecture of the master control IP core scheduling matrix changes, the corresponding topology structure will also change. By rebuilding the topology structure and then optimizing the routing convergence for calculation, the routing forwarding path is obtained, and then the multicast stream switching result is obtained based on the routing forwarding path, and data is forwarded based on the multicast stream switching result. The provided routing convergence optimization method is simple. By optimizing the routing convergence, when the Spine-Leaf architecture of the master control IP core scheduling matrix changes, it can respond quickly and switch the multicast stream, thereby ensuring business continuity.

[0025] As a new type of network architecture, Spine-Leaf architecture has significant advantages and broad application prospects in data center networks. In the field of radio and television, Spine-Leaf architecture has also fully demonstrated its value, and has done a good job in routing convergence optimization, making it play a more important role in the master control IP system, promoting the reform of television production and transmission, and contributing to the better completion of large-scale live broadcast tasks in the future.

[0026] like Figure 2 As shown, further, based on the reconstructed Spine-Leaf architecture networking topology, the multicast routing convergence calculation is optimized to obtain the routing forwarding path, and the multicast stream switching result is obtained according to the routing forwarding path, which specifically includes: Based on the reconstructed Spine-Leaf architecture networking topology, the routing forwarding path is calculated; Build a routing table based on the routing forwarding path; Divide the multicast stream into high-priority multicast stream and low-priority multicast stream according to the task characteristics; Switching forwarding table entries of all multicast streams based on the division result of the multicast streams and the routing table specifically includes: preferentially switching high-priority multicast streams, and then switching low-priority multicast streams.

[0027] When the optimized multicast routing convergence method provided by the present application is adopted, the average switching time for each multicast stream is 20-30 ms, and the switching time for 100 high-priority multicast streams is about 2-3 s; when the traditional routing convergence method is adopted, the average switching time for each multicast stream is 70-100 ms, and the switching time for 1000 high-priority multicast streams is about 70-100 s; therefore, the switching efficiency of the optimized multicast routing convergence method provided by the present application is higher.

[0028] In this application, the importance of target tasks is divided by dividing high-priority multicast streams and low-priority multicast streams, and the important target tasks are switched first as high-priority multicast streams, and the less important target tasks are switched later, which can quickly restore the important target tasks, enhance the protection of important target tasks, greatly shorten the average switching time of each multicast stream, and further ensure business continuity. Figure 3 As shown, Figure 3 T1 indicates that the Spine-Leaf architecture of the master control IP core scheduling matrix changes, that is, the main interface is offline (down), T2 indicates that the single-cut convergence is completed, and T3 indicates that the group-cut convergence is completed. Figure 3(a) When switching multicast streams, a single cut method is used. Although the total execution time is long, each multicast stream interacts once, and the preparation time before switching is short. One stream is calculated and sent for switching, and the switching is performed in sequence. When there are multiple devices, if one device fails to execute, it only affects the current multicast stream and has no impact on other multicast streams; this avoids Figure 3 (b) The group switching method used results in a long preparation time before switching, the overall sending and switching after the calculation is completed, the inability to ensure transaction consistency among the devices in the topology when there are multiple devices, the switching order of multiple devices corresponding to the business flow will be temporarily disordered, and when one device fails to execute, even if other devices have executed successfully, the entire task traffic will be affected.

[0029] In order to prove the beneficial effect of single cutting in this application, the single cutting and group cutting methods are compared. The comparison results are as follows: Figure 4 As shown, 1 is single-cut convergence, 2 is group-cut convergence, .

[0030] like Figure 5 As shown, further, the multicast stream is divided into high priority multicast stream and low priority multicast stream according to the task characteristics, specifically including: Preliminarily classify and sort the multicast streams according to the task characteristics; Prioritize the multicast streams according to the preliminary classification and sorting results, and divide them into preliminary high-priority multicast streams and preliminary low-priority multicast streams; Determine whether the proportion of the preliminary high priority multicast stream in all multicast streams does not exceed a preset threshold, and if so, use the preliminary high priority multicast stream as the final high priority multicast stream, and use the preliminary low priority multicast stream as the final low priority multicast stream; Otherwise, the multicast streams are prioritized again according to the preliminary classification and sorting results until the proportion of the preliminary high-priority multicast streams in all multicast streams does not exceed a preset threshold.

[0031] When dividing multicast streams in this application, the priority can be adjusted according to the actual situation to adapt to the characteristics of the actual target task. It is necessary to consider the proportion of high-priority multicast streams in all multicast streams. If it exceeds the preset threshold, that is, the proportion of high-priority multicast streams is too large, the effect of optimizing routing convergence will be discounted, resulting in unsatisfactory switching effects.

[0032] Furthermore, changes occur in the Spine-Leaf architecture of the master control IP core scheduling matrix, including at least one of the following situations: failure of a node or link in the Spine-Leaf architecture, disconnection or deletion of a Spine router or Leaf router in the Spine-Leaf architecture.

[0033] In the present application, the reasons for the change in the Spine-Leaf architecture of the master control IP core scheduling matrix, that is, the change in the topological structure of the Spine-Leaf architecture of the master control IP core scheduling matrix, may be one or more of the above situations.

[0034] It should be understood that, although the various steps in the flow chart are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear description in this article, the execution of these steps is not strictly limited in order, and these steps can be executed in other orders. Moreover, at least a portion of the steps in the figure may include multiple sub-steps or multiple stages, and these sub-steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these sub-steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a portion of the sub-steps or stages of other steps.

[0035] The present application also provides a master control IP core scheduling matrix Spine-Leaf architecture routing convergence optimization system, including: a module for implementing the master control IP core scheduling matrix Spine-Leaf architecture routing convergence optimization method as described in any of the above items.

[0036] like Figure 6 As shown, further, the routing convergence optimization system includes: The topology reconstruction module 10 is used to calculate and reconstruct the network topology of the Spine-Leaf architecture when the Spine-Leaf architecture of the master control IP core scheduling matrix changes; The convergence calculation module 20 is used to optimize the multicast routing convergence calculation based on the reconstructed Spine-Leaf architecture networking topology structure, obtain the routing forwarding path, and obtain the multicast flow switching result according to the routing forwarding path; The transmission module 30 is used to forward data according to the multicast stream switching result.

[0037] like Figure 7 As shown, further, the convergence calculation module 20 includes: A path calculation unit 201 is used to calculate a routing forwarding path based on the reconstructed Spine-Leaf architecture networking topology; A routing table building unit 202, used to build a routing table according to the routing forwarding path; A division unit 203, configured to divide the multicast stream into a high priority multicast stream and a low priority multicast stream according to the task characteristics; The switching unit 204 is used to switch forwarding table entries of all multicast streams based on the division result of the multicast streams and the routing table, specifically including: preferentially switching high priority multicast streams, and then switching low priority multicast streams.

[0038] like Figure 8 As shown, further, the division unit 203 includes: A preliminary classification and sorting unit 2031 is used to perform preliminary classification and sorting of the multicast stream according to the task characteristics; A preliminary classification unit 2032 is used to classify the multicast stream into a preliminary high priority multicast stream and a preliminary low priority multicast stream according to the preliminary classification and sorting result; A determination unit 2033, configured to determine whether the proportion of the preliminary high priority multicast stream in all multicast streams does not exceed a preset threshold; The priority determination unit 2034 is configured to use the preliminary high priority multicast stream as the final high priority multicast stream and the preliminary low priority multicast stream as the final low priority multicast stream when the proportion of the preliminary high priority multicast stream in all multicast streams does not exceed a preset threshold; The re-dividing unit 2035 is used to re-prioritize the multicast streams according to the preliminary classification and sorting result when the proportion of the preliminary high priority multicast streams in all multicast streams exceeds the preset threshold, until the proportion of the preliminary high priority multicast streams in all multicast streams does not exceed the preset threshold.

[0039] The present application also provides an electronic device, including: Memory; Processor; and Computer programs; Wherein, the computer program is stored in the memory and is configured to be executed by the processor to implement the routing convergence optimization method of the master control IP core scheduling matrix Spine-Leaf architecture as described in any one of the above items.

[0040] The present application also provides a computer-readable storage medium having a computer program stored thereon; the computer program is executed by a processor to implement the routing convergence optimization method of the master control IP core scheduling matrix Spine-Leaf architecture as described in any of the above items.

[0041] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program codes. The scheme in the embodiments of the present application may be implemented in various computer languages, for example, C language, VHDL language, Verilog language, object-oriented programming language Java, and literal scripting language JavaScript, etc.

[0042] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0043] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0044] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0045] In addition, in the description of the present application, “plurality” means at least two, for example, two, three, etc., unless otherwise clearly and specifically defined.

[0046] Although the preferred embodiments of the present application have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications falling within the scope of the present application.

[0047] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is also intended to include these modifications and variations.

Claims

1. A method for optimizing routing convergence in a master control IP core scheduling matrix Spine-Leaf architecture, characterized in that: include: When the Spine-Leaf architecture of the master control IP core scheduling matrix changes, calculate and rebuild the network topology of the Spine-Leaf architecture; Based on the reconstructed Spine-Leaf architecture networking topology, the multicast routing convergence calculation is optimized to obtain the routing forwarding path, and the multicast stream switching result is obtained according to the routing forwarding path; Data is forwarded based on the multicast stream switching result.

2. The method for optimizing routing convergence of the master control IP core scheduling matrix Spine-Leaf architecture according to claim 1 is characterized in that: The method of performing optimized multicast routing convergence calculation based on the reconstructed Spine-Leaf architecture networking topology to obtain a routing forwarding path, and obtaining a multicast stream switching result according to the routing forwarding path, specifically includes: Based on the reconstructed Spine-Leaf architecture networking topology, the routing forwarding path is calculated; Build a routing table based on the routing forwarding path; Divide the multicast stream into high-priority multicast stream and low-priority multicast stream according to the task characteristics; Switching forwarding table entries of all multicast streams based on the division result of the multicast streams and the routing table specifically includes: preferentially switching high-priority multicast streams, and then switching low-priority multicast streams.

3. The method for optimizing routing convergence of the master control IP core scheduling matrix Spine-Leaf architecture according to claim 2 is characterized in that: The multicast stream is divided into high priority multicast stream and low priority multicast stream according to the task characteristics, specifically including: Preliminarily classify and sort the multicast streams according to the task characteristics; Prioritize the multicast streams according to the preliminary classification and sorting results, and divide them into preliminary high-priority multicast streams and preliminary low-priority multicast streams; Determine whether the proportion of the preliminary high priority multicast stream in all multicast streams does not exceed a preset threshold, and if so, use the preliminary high priority multicast stream as the final high priority multicast stream, and use the preliminary low priority multicast stream as the final low priority multicast stream; Otherwise, the multicast streams are prioritized again according to the preliminary classification and sorting results until the proportion of the preliminary high-priority multicast streams in all multicast streams does not exceed a preset threshold.

4. The method for optimizing routing convergence of the master control IP core scheduling matrix Spine-Leaf architecture according to claim 1 is characterized in that: The changes in the master control IP core scheduling matrix Spine-Leaf architecture include at least one of the following situations: failure of a node or link in the Spine-Leaf architecture, disconnection or deletion of a Spine router or Leaf router in the Spine-Leaf architecture.

5. Master control IP core scheduling matrix Spine-Leaf architecture routing convergence optimization system, characterized by: include: A module for implementing the routing convergence optimization method of the master control IP core scheduling matrix Spine-Leaf architecture as described in any one of claims 1 to 4.

6. The master control IP core scheduling matrix Spine-Leaf architecture routing convergence optimization system according to claim 5 is characterized in that: The routing convergence optimization system comprises: A topology reconstruction module (10) is used to calculate and reconstruct the network topology of the Spine-Leaf architecture when the Spine-Leaf architecture of the master control IP core scheduling matrix changes; A convergence calculation module (20) is used to optimize multicast routing convergence calculation based on the reconstructed Spine-Leaf architecture networking topology structure, obtain a routing forwarding path, and obtain a multicast flow switching result according to the routing forwarding path; The transmission module (30) is used to forward data according to the multicast stream switching result.

7. The master control IP core scheduling matrix Spine-Leaf architecture routing convergence optimization system according to claim 6 is characterized in that: The convergence calculation module (20) comprises: A path calculation unit (201) is used to calculate a routing forwarding path based on the reconstructed Spine-Leaf architecture networking topology structure; A routing table construction unit (202), configured to construct a routing table according to a routing forwarding path; A division unit (203) is used to divide the multicast stream into: a high priority multicast stream and a low priority multicast stream according to task characteristics; The switching unit (204) is used to switch the forwarding table entries of all multicast streams based on the division result of the multicast streams and the routing table, specifically including: preferentially switching the high priority multicast streams, and then switching the low priority multicast streams.

8. The master control IP core scheduling matrix Spine-Leaf architecture routing convergence optimization system according to claim 7 is characterized in that: The dividing unit (203) comprises: A preliminary classification and sorting unit (2031) is used to perform preliminary classification and sorting of the multicast stream according to the task characteristics; A preliminary classification unit (2032) is used to classify the multicast stream into a preliminary high priority multicast stream and a preliminary low priority multicast stream according to the preliminary classification and sorting result; A judging unit (2033) is used to judge whether the proportion of the preliminary high priority multicast stream in all multicast streams does not exceed a preset threshold; The priority determination unit (2034) is used to use the preliminary high priority multicast stream as the final high priority multicast stream and the preliminary low priority multicast stream as the final low priority multicast stream when the proportion of the preliminary high priority multicast stream in all multicast streams does not exceed a preset threshold; The re-dividing unit (2035) is used to re-prioritize the multicast streams according to the preliminary classification and sorting results when the proportion of the preliminary high priority multicast streams in all multicast streams exceeds a preset threshold, until the proportion of the preliminary high priority multicast streams in all multicast streams does not exceed the preset threshold.

9. An electronic device, characterized in that: include: Memory; processor; as well as Computer programs; The computer program is stored in the memory and is configured to be executed by the processor to implement the routing convergence optimization method of the master control IP core scheduling matrix Spine-Leaf architecture as described in any one of claims 1 to 4.

10. A computer-readable storage medium, characterized in that: A computer program is stored thereon; the computer program is executed by a processor to implement the routing convergence optimization method of the master control IP core scheduling matrix Spine-Leaf architecture as described in any one of claims 1 to 4.

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