A 5G gateway resource optimization system and method

Abstract

The present invention discloses a 5G gateway resource optimization system and method. The system includes an acquisition module, a calculation module I, a judgment module, a calculation module II, a calculation module III, a processing module, a feedback module, etc. The present invention relates to the technical field of resource optimization. Specifically, it relates to a 5G gateway resource optimization system and method. The technical problem to be solved by the present invention is to provide a 5G gateway resource optimization system and method that can perform dynamic task allocation, improve the utilization rate of resource allocation, and improve the management efficiency of gateway resources.

Description

Technical Field

[0001] The present invention relates to the technical field of resource optimization, and in particular, to a 5G gateway resource optimization system and method. Background Art

[0002] With the development of the Internet of Things and 5G communication technology, edge gateways, as an important carrier of edge computing, have become a core component in the data processing and decision-making process. Edge computing can perform pre-analysis and local decision-making at the data source and communicate with the cloud, thereby reducing dependence on cloud resources and effectively reducing response time. Its data resource optimization plays an important role in improving the efficiency of the overall system.

[0003] At present, 5G edge gateways usually perform resource optimization and task management according to the system's pre-set rules and fixed thresholds. By setting the constraints for the edge gateway's resource optimization and allocation, a resource allocation model is obtained. When it is obtained that there is a certain amount of resources to be allocated at a certain node, the task computing load data is obtained and loaded into the resource allocation model, thereby scheduling the idle computing power of the edge gateway and front-end equipment to share the burden of computing resources.

[0004] Regarding the above technical solutions, these strategies are difficult to cope with dynamically changing task loads and changing network conditions. For example, when a node is under high concurrency pressure or overload risk, dynamic task allocation cannot be performed in a timely manner, resulting in a lot of resource waste and reducing the management efficiency of gateway resources. Summary of the invention

[0005] The technical problem to be solved by the present invention is to provide a 5G gateway resource optimization system and method, so as to enable dynamic task allocation, improve the utilization rate of resource allocation, and improve the management efficiency of gateway resources.

[0006] In a first aspect, the present invention provides a 5G gateway resource optimization system, which adopts the following technical solutions to achieve the purpose of the invention:

[0007] A 5G gateway resource optimization system includes the following modules:

[0008] Acquisition module: The output end is connected to the input end of calculation module I, and is used to obtain the task data to be processed in I gateway nodes. The tasks to be processed include real-time tasks and N non-real-time tasks. The calculation amount of the real-time task of the i-th node is , the computational amount of the nth non-real-time task of the ith node is , the maximum single-task computation amount of the i-th node is , the average task calculation rate of the i-th node is ;

[0009] Calculation Module I: Its input end is connected to the output end of the acquisition module, and its output end is connected to the input end of the judgment module, and it is used to calculate the processing time-consuming of the task to be processed at the i-th node , The calculation model of

[0010] ;

[0011] Judgment Module: Its input end is connected to the output end of Calculation Module I, and its output end is respectively connected to the input end of Calculation Module II and the input end of the feedback module, and it is used to obtain the deadline of the task to be processed at the i-th node as , the system real-time time is , if , then it is judged that the resources are sufficient, and the feedback module is executed. Otherwise, it is judged that the resources are insufficient, and Calculation Module II is executed;

[0012] Calculation Module II: Its input end is connected to the output end of the judgment module, and its output end is connected to the input end of Calculation Module III, and it is used to calculate the non-real-time task allocation weight of the i-th node , The calculation model of

[0013] ;

[0014] Among them, is the priority weight of the i-th node, ;

[0015] Calculation Module III: Its input end is connected to the output end of Calculation Module II, and its output end is connected to the input end of the processing module, and it is used to calculate that the task resource allocation ratio of the i-th node is , and the task resource allocation amount of the i-th node is ;

[0016] Processing Module: Its input end is connected to the output end of Calculation Module III, and its output end is connected to the input end of , and it is used to sequentially allocate the non-real-time tasks in descending order according to the task resource allocation amount to other nodes for calculation;

[0017] Feedback Module: Its input end is respectively connected to the output end of the judgment module and the output end of the processing module, and it is used to upload the allocation result of the task data to be processed to the management end.

[0018] As a further limitation of this technical solution, in Calculation Module II, The calculation model of

[0019] ;

[0020] Among them, α is the first weight coefficient, β is the second weight coefficient, and γ is the third weight coefficient.

[0021] As a further limitation of this technical solution, in calculation module II, the calculation model of

[0022] ;

[0023] Among them, is the real-time task processing coefficient of the i-th node, the calculation model of

[0024] .

[0025] As a further limitation of this technical solution, in calculation module I, the calculation model of

[0026] ;

[0027] Among them, is the node load factor, and Y is the maximum concurrent task carrying capacity.

[0028] As a further limitation of this technical solution, in the processing module, the minimum task resource allocation amount is also set to , the node numbers arranged in descending order are 1, 2, 3,..., m,..., M in turn, where M = I. If according to the task resource allocation amount of the m-th node allocated in descending order is less than or equal to , then after allocating to the (m + 1)-th node, stop allocating to the next node, and the (m + 1)-th node completes the remaining non-real-time tasks to be allocated.

[0029] In the second aspect, the present invention provides a 5G gateway resource optimization method, and the invention purpose is achieved by adopting the following technical solution:

[0030] A 5G gateway resource optimization method includes the following steps:

[0031] Obtain data: Obtain the to-be-processed task data in I gateway nodes. The to-be-processed tasks include real-time tasks and N non-real-time tasks. Obtain the real-time task calculation amount of the i-th node as , the calculation amount of the n-th non-real-time task of the i-th node is , the maximum single-task calculation amount of the i-th node is , and the average task calculation rate of the i-th node is ;

[0032] Calculation I: Calculate the processing time of the to-be-processed tasks of the i-th node , The calculation model of

[0033] ;

[0034] Judgment: Obtain the deadline of the task to be processed at the i-th node as , and the system real-time time is . If , it is determined that the resources are sufficient, and the feedback step is executed. Otherwise, it is determined that the resources are insufficient, and the calculation II step is executed;

[0035] Calculation II: Calculate the non-real-time task allocation weight of the i-th node , The calculation model of

[0036] ;

[0037] Among them, is the priority weight of the i-th node, ;

[0038] Calculation III: Calculate that the task resource allocation ratio of the i-th node is , and the task resource allocation amount of the i-th node is ;

[0039] Processing: According to the task resource allocation ratio of the i-th node, the non-real-time tasks are sorted in descending order according to the task resource allocation amount and allocated to other nodes for calculation;

[0040] Feedback: Upload the allocation result of the task data to be processed to the management end.

[0041] As a further limitation of this technical solution, in the calculation II step, the calculation model of

[0042] ;

[0043] Among them, α is the first weight coefficient, β is the second weight coefficient, and γ is the third weight coefficient.

[0044] As a further limitation of this technical solution, in the calculation II step, the calculation model of

[0045] ;

[0046] Among them, is the task processing real-time coefficient of the i-th node, the calculation model of

[0047] 。

[0048] As a further limitation of this technical solution, in step I of the calculation, the calculation model of

[0049] ;

[0050] wherein, is the node load factor, and Y is the maximum concurrent task capacity.

[0051] As a further limitation of this technical solution, in the processing step, the minimum task resource allocation amount is also set to , the node numbers in descending order are 1, 2, 3,..., m,..., M, where M = I. If according to the task resource allocation amount of the mth node allocated in descending order is less than or equal to , then after allocating to the (m + 1)th node, stop allocating to the next node, and the (m + 1)th node completes the remaining unallocated non-real-time tasks.

[0052] Compared with the prior art, the advantages and positive effects of the present invention are:

[0053] 1. By calculating the processing time of the tasks to be processed by the ith node, it is judged whether the computing resources of this node are sufficient. When it is judged that the resources of this node are insufficient, according to the real-time task calculation amount, non-real-time task calculation amount and task type of this node, calculate the non-real-time task allocation weight of the ith node, so as to achieve multi-objective optimization. By introducing the priority weight, the scheduling priority of high-value service nodes is significantly higher than that of ordinary task nodes. Calculate the task resource allocation ratio of the ith node, and then calculate the task resource allocation amount of the ith node. According to the task resource allocation ratio of the ith node, allocate the non-real-time tasks to other nodes in descending order for calculation, so as to perform dynamic task allocation, improve the utilization rate of resource allocation, and improve the management efficiency of gateway resources.

[0054] 2. By designing the first weight coefficient, the second weight coefficient, and the third weight coefficient, it is possible to accurately adjust the node priority, resource utilization rate, and real-time performance in different environments, further adapt to different business requirements in different differentiated scenarios, and thus improve the practicability and reliability.

[0055] 3. By calculating the real-time coefficient of task processing at this node, it is beneficial to ensure the priority of real-time task processing. For example, in the face of high-real-time services such as emergency braking instructions and collaborative perception, through this real-time task priority scheduling mechanism, the task interruption rate can be effectively reduced. At the same time, the resource release rate of non-real-time tasks is significantly improved, supporting the dynamic scaling requirements, enabling dynamic task allocation, improving the utilization rate of resource allocation, and enhancing the management efficiency of gateway resources.

[0056] 4. By introducing the node load factor and the maximum concurrent task capacity, calculate the processing time of the tasks to be processed at the i-th node, so as to quantitatively measure the concurrent pressure of this gateway node in real time, accurately identify the risk of node overload. Compared with the traditional static threshold detection, it can predict resource bottlenecks in advance and avoid system crashes caused by task backlogs. In the case of sudden traffic scenarios, the dynamic correction mechanism of the load factor significantly improves the elastic scaling ability of node resources, enabling critical tasks to obtain resources preferentially.

[0057] 5. By setting the minimum resource allocation amount and the descending order allocation rule, it can effectively curb resource fragmentation, while maintaining the allocation efficiency, reducing resource waste caused by fragmented task allocation, thus being applicable to the batch processing scenario of compute-intensive non-real-time tasks, and further improving the utilization rate of resource allocation and the management efficiency of gateway resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] The drawings described herein are used to provide a further understanding of the present application and form a part of the present application. The schematic embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation of the present application. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts. In the drawings:

[0059] Figure 1 is the system diagram of Embodiment 1 of the present invention;

[0060] Figure 2 is the flowchart of Embodiment 2 of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0061] The following will combine the Figure 1 and Figure 2 of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, rather than all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present invention.

[0062] It should be noted that the orientation terms such as left, right, up, down, front, and back in the embodiments of the present invention are only relative concepts to each other or are referenced based on the normal use state of the product, that is, the traveling direction of the product, and should not be considered as restrictive.

[0063] When a component is referred to as being "located" or "disposed on" another component, it can be on the other component or there may be an intermediate component present at the same time. When a component is referred to as being "connected to" another component, it can be directly connected to the other component or there may be an intermediate component present at the same time.

[0064] Embodiment 1: A 5G gateway resource optimization system includes the following modules:

[0065] The acquisition module: The output end is connected to the input end of the calculation module I, and is used to acquire the to-be-processed task data in I network gateway nodes. The to-be-processed tasks include real-time tasks and N non-real-time tasks. The real-time task calculation amount of the i-th node is , and the calculation amount of the n-th non-real-time task of the i-th node is , the maximum single-task calculation amount of the i-th node is , and the average task calculation rate of the i-th node is ;

[0066] The calculation module I: The input end is connected to the output end of the acquisition module, and the output end is connected to the input end of the judgment module, and is used to calculate the processing time of the to-be-processed tasks of the i-th node , , and the calculation model of

[0067] ;

[0068] Among them, is the node load factor, and Y is the maximum concurrent task carrying capacity;

[0069] The judgment module: The input end is connected to the output end of the calculation module I, and the output end is respectively connected to the input end of the calculation module II and the input end of the feedback module, and is used to obtain the deadline of the to-be-processed tasks of the i-th node as , the system real-time time is , if , it is determined that the resources are sufficient, and the feedback module is executed; otherwise, it is determined that the resources are insufficient, and the calculation module II is executed;

[0070] The calculation module II: The input end is connected to the output end of the judgment module, and the output end is connected to the input end of the calculation module III, and is used to calculate the non-real-time task allocation weight of the i-th node , , and the calculation model of

[0071] ;

[0072] Among them, is the priority weight of the i-th node, , α is the first weight coefficient, β is the second weight coefficient, and γ is the third weight coefficient, is the real-time task processing coefficient of the i-th node, The calculation model of is:

[0073] ;

[0074] Calculation module III: The input end is connected to the output end of calculation module II, and the output end is connected to the input end of the processing module, and is used to calculate that the task resource allocation ratio of the i-th node is , and the task resource allocation amount of the i-th node is ;

[0075] Processing module: The input end is connected to the output end of calculation module III, and the output end is connected to the input end of, and is used to sequentially allocate non-real-time tasks in descending order according to the task resource allocation ratio of the i-th node according to the task resource allocation amount to other nodes for calculation;

[0076] Set the minimum task resource allocation amount to , and the node numbers after descending order are 1, 2, 3,..., m,..., M in sequence, where M = I. If according to the task resource allocation amount of the m-th node allocated in descending order is less than or equal to , then after allocating to the (m + 1)-th node, stop allocating to the next node, and the (m + 1)-th node completes the remaining non-real-time tasks to be allocated;

[0077] Feedback module: The input end is respectively connected to the output end of the judgment module and the output end of the processing module, and is used to upload the allocation result of the task data to be processed to the management end.

[0078] The working principle of this embodiment is:

[0079] There are I gateway nodes set in the area where resource allocation can be performed. The acquisition module acquires the to-be-processed task data of the I gateway nodes. The to-be-processed tasks include real-time tasks and N non-real-time tasks. The real-time task computation volume of the i-th node, the computation volume of the n-th non-real-time task of the i-th node, the maximum single-task computation volume of the i-th node, and the average task computation rate of the i-th node are obtained. The computation module I calculates the processing time of the to-be-processed tasks of the i-th node by introducing the node load factor and the maximum concurrent task capacity, thereby quantifying the concurrent pressure of the gateway node in real time, accurately identifying the node overload risk. Compared with the traditional static threshold detection, it can predict the resource bottleneck in advance and avoid system crashes caused by task backlogs. In the case of sudden traffic scenarios, the dynamic correction mechanism of the load factor significantly improves the elastic scaling ability of node resources, enabling critical tasks to obtain resources preferentially.

[0080] The judgment module acquires the task deadline of the to-be-processed tasks of the i-th node and the system real-time time. If the judgment module determines that the resources are sufficient, the feedback module uploads the to-be-processed task data allocation result to the management end for recording. If the judgment module determines that the resources are insufficient, the computation module II calculates the non-real-time task allocation weight of the i-th node according to the real-time task computation volume, the computation volume of non-real-time tasks, and the task type of this node, so as to achieve multi-objective optimization. By introducing the priority weight, the scheduling priority of high-value service nodes is significantly higher than that of ordinary task nodes.

[0081] By designing the first weight coefficient, the second weight coefficient, and the third weight coefficient, it is possible to accurately adjust the node priority, resource utilization rate, and real-time performance in different environments, further adapting to different business requirements in different differentiated scenarios, thereby improving the practicality and reliability; by calculating the real-time coefficient of the task processing of this node , it is beneficial to ensure the priority of real-time task processing. For example, in the face of high-real-time services such as emergency braking instructions and cooperative perception, through this real-time task priority scheduling mechanism, the task interruption rate is effectively reduced, and at the same time, the resource release rate of non-real-time tasks is significantly increased, supporting the dynamic scaling requirements, enabling dynamic task allocation, improving the utilization rate of resource allocation, and improving the management efficiency of gateway resources.

[0082] The computation module III calculates the task resource allocation ratio of the i-th node, and then calculates the task resource allocation amount of the i-th node. The processing module allocates the non-real-time tasks to other nodes for calculation in descending order of the task resource allocation ratio of the i-th node according to the task resource allocation amount, thereby performing dynamic task allocation, improving the utilization rate of resource allocation, and improving the management efficiency of gateway resources;

[0083] Set the minimum task resource allocation amount as, if according to If the task resource allocation amount of the m-th node in descending order is less than or equal to, after allocating to the (m + 1)-th node, stop allocating to the next node. Let the (m + 1)-th node complete the remaining unallocated non-real-time tasks. The feedback module uploads the allocation result of the task data to be processed to the management end. By setting the minimum resource allocation amount and the descending allocation rule, resource fragmentation can be effectively curbed. While maintaining the allocation efficiency, the resource waste caused by the allocation of fragmented tasks can be reduced, so as to be applicable to the batch processing scenario of compute-intensive non-real-time tasks, and then improve the utilization rate of resource allocation and the management efficiency of gateway resources.

[0084] Embodiment 2: A 5G gateway resource optimization method includes the following steps:

[0085] S1. Obtain data: Obtain the task data to be processed in I gateway nodes. The tasks to be processed include real-time tasks and N non-real-time tasks. Obtain that the real-time task computation amount of the i-th node is , the computation amount of the n-th non-real-time task of the i-th node is , the maximum single-task computation amount of the i-th node is , and the average task computation rate of the i-th node is ;

[0086] S2. Calculate I: Calculate the processing time of the tasks to be processed by the i-th node , The calculation model of is:

[0087] ;

[0088] Among them, is the node load factor, and Y is the maximum concurrent task carrying capacity;

[0089] S3. Judge: Obtain that the deadline of the tasks to be processed by the i-th node is , the system real-time time is . If , it is judged that the resources are sufficient, and the feedback module is executed. Otherwise, it is judged that the resources are insufficient, and step Calculate II is executed;

[0090] S4. Calculate II: Calculate the non-real-time task allocation weight of the i-th node , The calculation model of is:

[0091] ;

[0092] Among them, is the priority weight of the i-th node, , α is the first weight coefficient, β is the second weight coefficient, γ is the third weight coefficient, is the real-time coefficient of task processing for the i-th node, The calculation model of

[0093] ;

[0094] S5. Calculation III: Calculate the task resource allocation ratio of the i-th node as , and the task resource allocation amount of the i-th node is ;

[0095] S6. Processing: According to the task resource allocation ratio of the i-th node, non-real-time tasks are sorted in descending order and allocated to other nodes for calculation according to the task resource allocation amount ;

[0096] Set the minimum task resource allocation amount as , and the sorted node numbers in descending order are 1, 2, 3,..., m,..., M, where M = I. If the task resource allocation amount of the m-th node allocated in descending order according to is less than or equal to , then after allocating to the (m + 1)-th node, stop allocating to the next node, and the (m + 1)-th node completes the remaining non-real-time tasks to be allocated;

[0097] S7. Feedback: Upload the allocation result of the task data to be processed to the management end.

[0098] The working principle of this embodiment is as follows:

[0099] A total of I gateway nodes are set in the area where resource allocation can be carried out. Obtain the task data to be processed in the I gateway nodes. The tasks to be processed include real-time tasks and N non-real-time tasks. Obtain the real-time task calculation amount of the i-th node, the calculation amount of the n-th non-real-time task of the i-th node , the maximum single-task calculation amount of the i-th node, the average task calculation rate of the i-th node. By introducing the node load factor and the maximum concurrent task capacity Y, calculate the processing time of the tasks to be processed by the i-th node, so as to quantify the concurrent pressure of this gateway node in real time, accurately identify the node overload risk. Compared with the traditional static threshold detection, it can predict the resource bottleneck in advance and avoid system crashes caused by task accumulation. In the case of sudden traffic, the dynamic correction mechanism of the load factor significantly improves the elastic scaling ability of node resources, enabling critical tasks to obtain resources first.

[0100] Obtain the task deadline of the tasks to be processed by the i-th node, and the system real-time time , if , it is determined that the resources are sufficient, and the allocation result of the task data to be processed is uploaded to the management terminal for recording. Otherwise, it is determined that the resources are insufficient; when it is determined that the resources of this node are insufficient, according to the real-time task calculation volume, non-real-time task calculation volume and task type of this node, calculate the non-real-time task allocation weight of the i-th node , so as to achieve multi-objective optimization. Through the priority weight , the scheduling priority of high-value service nodes is significantly higher than that of ordinary task nodes;

[0101] By designing the first weight coefficient, the second weight coefficient, and the third weight coefficient, it is possible to accurately adjust the node priority, resource utilization rate, and real-time performance in different environments, further adapt to the different business requirements of different scenarios, and thus improve the practicability and reliability; by calculating the real-time coefficient of the task processing of this node , it is beneficial to ensure the priority of real-time task processing. For example, in the face of high-real-time services such as emergency braking instructions and collaborative perception, through this real-time task priority scheduling mechanism, the task interruption rate is effectively reduced, and at the same time, the resource release rate of non-real-time tasks is significantly improved, supporting the dynamic scaling requirements, enabling dynamic task allocation, improving the utilization rate of resource allocation, and improving the management efficiency of gateway resources.

[0102] Calculate the task resource allocation ratio of the i-th node , and then calculate the task resource allocation amount of the i-th node , according to the task resource allocation ratio of the i-th node In descending order, non-real-time tasks are sequentially allocated to other nodes for calculation according to the task resource allocation amount , so as to perform dynamic task allocation, improve the utilization rate of resource allocation, and improve the management efficiency of gateway resources;

[0103] Set the minimum task resource allocation amount to , if according to The task resource allocation amount of the m-th node allocated in descending order is less than or equal to , then after being allocated to the (m + 1)-th node, stop allocating to the next node. The (m + 1)-th node completes the remaining non-real-time tasks to be allocated, and uploads the allocation result of the task data to be processed to the management terminal. By setting the minimum resource allocation amount and the descending order allocation rule, resource fragmentation is effectively curbed, while maintaining the allocation efficiency, reducing resource waste caused by fragmented task allocation, so as to be applicable to the batch processing scenario of compute-intensive non-real-time tasks, and further improve the utilization rate of resource allocation and the management efficiency of gateway resources.

[0104] The above-disclosed are only specific embodiments of the present invention. However, the present invention is not limited thereto, and any variations that can be conceived by those skilled in the art shall fall within the protection scope of the present invention.

Claims

1. A 5G gateway resource optimization system, characterized in that: Includes the following modules: Acquisition module: The output end is connected to the input end of calculation module I, and is used to obtain the task data to be processed in I gateway nodes. The tasks to be processed include real-time tasks and N non-real-time tasks. The calculation amount of the real-time task of the i-th node is , the computational amount of the nth non-real-time task of the ith node is , the maximum single-task computation amount of the i-th node is , the average task calculation rate of the i-th node is ; Calculation module I: The input end is connected to the output end of the acquisition module, and the output end is connected to the input end of the judgment module, which is used to calculate the processing time of the task to be processed at the i-th node , The calculation model is: ； Judgment module: The input end is connected to the output end of calculation module I, and the output end is connected to the input end of calculation module II and the input end of feedback module respectively, which is used to obtain the deadline of the pending task of the i-th node. , the system real time is ,like , then it is judged that the resources are sufficient and the feedback module is executed. Otherwise, it is judged that the resources are insufficient and the calculation module II is executed; Computation module II: The input end is connected to the output end of the judgment module, and the output end is connected to the input end of the calculation module III, which is used to calculate the non-real-time task allocation weight of the i-th node , The calculation model is: ； in, is the priority weight of the i-th node, ; Computing module III: The input end is connected to the output end of computing module II, and the output end is connected to the input end of the processing module, which is used to calculate the task resource allocation ratio of the i-th node: , the task resource allocation of the i-th node is ; Processing module: The input end is connected to the output end of the computing module III, and the output end is connected to the input end of the processing module, which is used to allocate the task resources according to the ratio of the i-th node. Arrange non-real-time tasks in descending order according to the task resource allocation amount Distribute to other nodes for calculation; Feedback module: The input end is connected to the output end of the judgment module and the output end of the processing module respectively, and is used to upload the distribution result of the task data to be processed to the management end.

2. A 5G gateway resource optimization system according to claim 1, characterized in that: In the computational module II, The calculation model is updated as follows: ； Among them, α is the first weight coefficient, β is the second weight coefficient, and γ is the third weight coefficient.

3. A 5G gateway resource optimization system according to claim 2, characterized in that: In the computational module II, The calculation model is updated as follows: ； in, is the real-time coefficient of task processing of the i-th node, The calculation model is: 。 4. A 5G gateway resource optimization system according to claim 1, characterized in that: In the calculation module I, The calculation model is updated as follows: ； in, is the node load factor, and Y is the maximum concurrent task capacity.

5. A 5G gateway resource optimization system according to any one of claims 1 to 4, characterized in that: In the processing module, the minimum task resource allocation is also set to , the node numbers after descending order are 1, 2, 3, ..., m, ..., M, where M = I. If according to The task resource allocation of the mth node in descending order is less than or equal to , after allocating to the m+1th node, the allocation to the next node stops, and the m+1th node completes the remaining non-real-time tasks to be allocated.

6. A 5G gateway resource optimization method, characterized in that: The following steps are involved: Get data: Get the pending task data in I gateway nodes. The pending tasks include real-time tasks and N non-real-time tasks. The real-time task calculation amount of the i-th node is , the computational amount of the nth non-real-time task of the ith node is , the maximum single-task computation amount of the i-th node is , the average task calculation rate of the i-th node is ; Calculation I: Calculate the processing time of the task to be processed at the i-th node , The calculation model is: ； Judgment: Get the deadline for the pending tasks of the i-th node. , the system real time is ,like , then it is judged that the resources are sufficient and the feedback step is executed; otherwise, it is judged that the resources are insufficient and the calculation II step is executed; Calculation II: Calculate the non-real-time task allocation weight of the i-th node , The calculation model is: ； in, is the priority weight of the i-th node, ; Calculation III: Calculate the task resource allocation ratio of the i-th node: , the task resource allocation of the i-th node is ; Processing: According to the task resource allocation ratio of the i-th node Arrange non-real-time tasks in descending order according to the task resource allocation amount Distribute to other nodes for calculation; Feedback: Upload the data allocation results of pending tasks to the management end.

7. A 5G gateway resource optimization method according to claim 6, characterized in that: In step II of the calculation, The calculation model is updated as follows: ； Among them, α is the first weight coefficient, β is the second weight coefficient, and γ is the third weight coefficient.

8. A 5G gateway resource optimization method according to claim 7, characterized in that: In step II of the calculation, The calculation model is updated as follows: ； in, is the real-time coefficient of task processing of the i-th node, The calculation model is: 。 9. A 5G gateway resource optimization method according to claim 6, characterized in that: In the calculation step I, The calculation model is updated as follows: ； in, is the node load factor, and Y is the maximum concurrent task capacity.

10. A 5G gateway resource optimization method according to any one of claims 6 to 9, characterized in that: In the processing step, the minimum task resource allocation is also set to , the node numbers after descending order are 1, 2, 3, ..., m, ..., M, where M = I. If according to The task resource allocation of the mth node in descending order is less than or equal to , after allocating to the m+1th node, the allocation to the next node stops, and the m+1th node completes the remaining non-real-time tasks to be allocated.

Images