A method for quickly building a cloud computing platform based on supercomputing

Abstract

The present invention relates to the technical field of cloud computing platform construction, and specifically discloses a method for quickly constructing a cloud computing platform based on supercomputers. By deploying a distributed log management system in the cloud platform, the operating data of each node is collected in real time; then the key indicator value of each node is calculated; the operating status level of the cloud platform corresponding resources is evaluated, and whether the cloud platform has a resource failure based on the operating status level of the cloud platform corresponding resources is identified; when a resource failure is detected in the cloud platform, a resource reconstruction process is automatically triggered to redistribute the load of each node. The cloud platform is enabled to perform preventive maintenance before a problem occurs, reducing the probability of failure. When the system detects a resource failure, a resource reconstruction process is automatically triggered to redistribute the load of each node, which can ensure the continuity and stability of the service. This automated response not only shortens the fault handling time, but also reduces the need for manual intervention and the possibility of human error.

Description

Technical Field

[0001] The present invention belongs to the technical field of cloud computing platform construction, and relates to a method for quickly constructing a cloud computing platform based on supercomputing. Background Art

[0002] With the development of cloud computing technology, the rapid construction and efficient management of cloud platforms have become particularly important. Traditional cloud computing platform construction methods often lack real-time monitoring of resource status and rapid response mechanisms, resulting in low resource utilization and inability to respond to emergencies in a timely manner. Therefore, an innovative method for rapid construction of cloud computing platforms is proposed, which uses a distributed log management and monitoring system to achieve real-time monitoring and analysis of the operating status of platform resources, and can quickly reconstruct platform resources when the resource status is not good. Summary of the invention

[0003] In view of the above problems existing in the prior art, the present invention provides a method for quickly building a cloud computing platform based on supercomputer, which is used to solve the above technical problems.

[0004] In order to achieve the above purpose and other purposes, the technical solution adopted by the present invention is as follows: The first aspect of the present invention provides a method for quickly building a cloud computing platform based on a supercomputer, the method comprising the following steps: Distributed log management: Deploy a distributed log management system in the cloud platform to collect the operation data of each node in real time; Key indicator calculation: Based on the operation data of each node, the key indicator value of each node is calculated; Resource failure analysis: Based on the key indicator values ​​of each node, the operating status level of the corresponding resources of the cloud platform is evaluated, and based on the operating status level of the corresponding resources of the cloud platform, whether there is a resource failure on the cloud platform is identified; Platform reconstruction trigger: When a resource failure is detected on the cloud platform, the resource reconstruction process is automatically triggered to redistribute the load of each node.

[0005] For example, the key indicator values ​​of each node include resource utilization, average response time and error rate, which are marked as , g is the number of each node.

[0006] Exemplarily, the key indicator value of each node is calculated, and the specific calculation process is as follows: By calculating the formula 00%, calculate the resource utilization of each node ; By calculating the formula , calculate the average response time of each node , It represents the response time from sending to receiving the jth request corresponding to the gth node, and M is the total number of requests in the monitoring time period; By calculating the formula , calculate the error rate of each node , M1 represents the number of failed requests during the monitoring period.

[0007] Exemplarily, the operating status level of the corresponding resources of the cloud platform is evaluated, and the specific evaluation process is as follows: According to the resource utilization of each node, the resource utilization score of each node is obtained; According to the average response time of each node, the average response time score of each node is obtained; According to the error rate of each node, the error rate score of each node is obtained; Then calculate the operation score of the corresponding resources on the cloud platform , g is the number of each node, the value range of g is 1 to G, G is the total number of nodes, They represent the resource utilization score, average response time score, and error rate score of the g-th node respectively. Respectively represent the set weight coefficients, and ; The operation scores of the resources corresponding to the cloud platform are compared with the operation score intervals corresponding to each operation status level, thereby obtaining the operation status level of the resources corresponding to the cloud platform.

[0008] Exemplarily, the resource utilization score, average response time score and error rate score of each node are obtained, and the specific acquisition process is as follows: The resource utilization score of each node is calculated according to the following formula: : ,in They represent the lower and upper thresholds of the ideal utilization of the node’s resources respectively; When resource utilization is arrive When between, the score is 100; When resource utilization is lower than 2 points will be deducted for every 1% lower; When resource utilization is higher than 2 points will be deducted for every 1% higher.

[0009] The average response time score of each node is calculated according to the following formula: : ,in They respectively represent the lower and upper thresholds of the resource permission response time of the node;

[0010] The error rate score of each node is calculated according to the following formula: : ,in They respectively represent the lower and upper thresholds of the resource admission error rate of the node.

[0011] Exemplarily, the specific identification logic for identifying whether there is a resource failure on the cloud platform is: The operating status level of the corresponding resources of the cloud platform is compared with the set reference resource failure level. If the operating status level of the corresponding resources of the cloud platform is less than or equal to the set reference resource failure level, it is determined that there is a resource failure on the cloud platform. Otherwise, it is determined that there is no resource failure on the cloud platform.

[0012] It should be noted that the operating status levels are specifically divided into the first operating status level, the second operating status level, ... the Nth operating status level, where the operating score interval of the first operating status level is larger than the operating score interval of the second operating status level. That is to say, the first operating status level indicates that there is no resource failure problem, and recursively downwards, the Nth operating status level indicates that the resource failure problem is the most serious.

[0013] Exemplarily, the load of each node is redistributed, and the specific distribution process is as follows: When a resource failure is detected, the cloud platform immediately analyzes the resource distribution and load of each node: the real-time data collection module obtains the performance indicators of each node, including CPU utilization, memory usage, network throughput, and response time; the cloud platform compares these real-time data with historical data to identify which nodes have abnormal performance and which nodes have the capacity to carry more load; On this basis, the cloud platform uses intelligent algorithms to determine the health of the nodes and formulates a new load distribution plan based on preset strategies (such as minimizing response time or maximizing resource utilization); the formulation of this plan not only takes into account the current resource status, but also integrates possible future load change trends to ensure the continued effectiveness of the plan.

[0014] In the process of load redistribution, the cloud platform gives priority to the principle of minimizing the impact on users. To this end, the cloud platform will first containerize the application to ensure that the various components of the application can be quickly migrated between different nodes. The use of containerization technology allows applications to be flexibly deployed on multiple nodes without worrying about the problems caused by differences in the underlying hardware.

[0015] Through hot migration technology, the cloud platform can migrate the running virtual machine from the faulty node to the healthy node without interrupting the service. During this process, the system automatically handles the synchronization of network configuration and storage status to ensure that the migrated service can be put into use immediately.

[0016] A second aspect of the present invention provides a system for rapidly constructing a cloud computing platform based on a supercomputer, comprising a distributed log management module (1), a key indicator calculation module (2), a resource fault analysis module (3) and a platform reconstruction trigger module (4), wherein the above modules are connected by wired and / or wireless connection to achieve data transmission between the modules; Distributed log management module (1): Deploy a distributed log management system in the cloud platform to collect the operation data of each node in real time; Key indicator calculation module (2): based on the operation data of each node, calculate the key indicator value of each node; Resource failure analysis module (3): Based on the key indicator values ​​of each node, the operating status level of the corresponding resources of the cloud platform is evaluated, and based on the operating status level of the corresponding resources of the cloud platform, whether there is a resource failure in the cloud platform is identified; Platform reconstruction trigger module (4): When a resource failure is detected on the cloud platform, the resource reconstruction process is automatically triggered to redistribute the load of each node.

[0017] A third aspect of the present invention provides an electronic device, comprising: a processor and a memory, wherein the memory stores a computer program that can be called by the processor; The processor executes a method for quickly building a cloud computing platform based on supercomputing by calling the computer program stored in the memory.

[0018] A fourth aspect of the present invention provides a computer program product stored on a computer-readable medium, including a computer-readable program, which, when executed on an electronic device, provides a user input interface to implement the method for quickly building a cloud computing platform based on supercomputing.

[0019] As described above, the method for quickly building a cloud computing platform based on supercomputer provided by the present invention has at least the following beneficial effects: The present invention provides a method for quickly building a cloud computing platform based on supercomputing. By deploying a distributed log management system in the cloud platform and collecting the operating data of each node in real time, the observability and responsiveness of the platform can be significantly improved. First, the system can calculate the key indicator values ​​of each node in real time, helping operation and maintenance personnel and automation systems to fully understand the health status of each node. Through these indicators, the system can evaluate the operating status level of cloud platform resources and quickly identify potential performance bottlenecks and failure points.

[0020] This real-time monitoring and analysis capability enables the cloud platform to perform preventive maintenance before problems occur, reducing the probability of failure. When the system detects a resource failure, it automatically triggers the resource reconstruction process to redistribute the load of each node, ensuring service continuity and stability. This automated response not only shortens the troubleshooting time, but also reduces the need for manual intervention and the possibility of human error. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for describing the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0022] Figure 1 It is a schematic diagram of the connection of each step of the method of the present invention.

[0023] Figure 2 It is a schematic diagram of the connection of various modules of the system of the present invention.

[0024] Figure 3 The figure is a schematic diagram of the structure of an electronic device. DETAILED DESCRIPTION

[0025] The above contents in combination with the implementation of the present invention are merely examples and explanations of the concept of the present invention. The technicians in this technical field may make various modifications or additions to the specific embodiments described or replace them in a similar manner. As long as they do not deviate from the concept of the invention or exceed the scope defined by the claims, they shall all fall within the protection scope of the present invention.

[0026] Example 1: Please refer to Figure 1 As shown, a method for quickly building a cloud computing platform based on supercomputing includes the following steps: Distributed log management: Deploy a distributed log management system in the cloud platform to collect the operation data of each node in real time; Key indicator calculation: Based on the operation data of each node, the key indicator value of each node is calculated; Resource failure analysis: Based on the key indicator values ​​of each node, the operating status level of the corresponding resources of the cloud platform is evaluated, and based on the operating status level of the corresponding resources of the cloud platform, whether there is a resource failure on the cloud platform is identified; Platform reconstruction trigger: When a resource failure is detected on the cloud platform, the resource reconstruction process is automatically triggered to redistribute the load of each node.

[0027] In modern cloud computing environments, resource failures are inevitable, but they can be responded to quickly through automated processes to ensure service continuity and stable performance. When a resource failure is detected on the cloud platform, the system will automatically trigger the resource reconstruction process to redistribute the load of each node, aiming to minimize the impact of the failure on user experience.

[0028] Based on the above embodiment, the key indicator values ​​of each node include resource utilization, average response time and error rate, which are marked as , g is the number of each node.

[0029] Based on the above embodiment, the key indicator value of each node is calculated, and the specific calculation process is as follows: By calculating the formula 00%, calculate the resource utilization of each node ; By calculating the formula , calculate the average response time of each node , It represents the response time from sending to receiving the jth request corresponding to the gth node, and M is the total number of requests in the monitoring time period; By calculating the formula , calculate the error rate of each node , M1 represents the number of failed requests during the monitoring period.

[0030] Based on the above embodiment, the operating status level of the corresponding resources of the cloud platform is evaluated. The specific evaluation process is as follows: According to the resource utilization of each node, the resource utilization score of each node is obtained; According to the average response time of each node, the average response time score of each node is obtained; According to the error rate of each node, the error rate score of each node is obtained; Then calculate the operation score of the corresponding resources on the cloud platform , g is the number of each node, the value range of g is 1 to G, G is the total number of nodes, They represent the resource utilization score, average response time score, and error rate score of the g-th node respectively. Respectively represent the set weight coefficients, and ; The operation scores of the resources corresponding to the cloud platform are compared with the operation score intervals corresponding to each operation status level, thereby obtaining the operation status level of the resources corresponding to the cloud platform.

[0031] Based on the above embodiment, the resource utilization score, average response time score and error rate score of each node are obtained. The specific acquisition process is as follows: The resource utilization score of each node is calculated according to the following formula: : ,in They represent the lower and upper thresholds of the ideal utilization of the node’s resources respectively; When resource utilization is arrive When between, the score is 100; When resource utilization is lower than 2 points will be deducted for every 1% lower; When resource utilization is higher than 2 points will be deducted for every 1% higher.

[0032] The average response time score of each node is calculated according to the following formula: : ,in They respectively represent the lower and upper thresholds of the resource permission response time of the node; The error rate score of each node is calculated according to the following formula: : ,in They respectively represent the lower and upper thresholds of the resource admission error rate of the node.

[0033] Based on the above embodiment, the specific identification logic for identifying whether there is a resource failure on the cloud platform is: The operating status level of the corresponding resources of the cloud platform is compared with the set reference resource failure level. If the operating status level of the corresponding resources of the cloud platform is less than or equal to the set reference resource failure level, it is determined that there is a resource failure on the cloud platform. Otherwise, it is determined that there is no resource failure on the cloud platform.

[0034] It should be noted that the operating status levels are specifically divided into the first operating status level, the second operating status level, ... the Nth operating status level, where the operating score interval of the first operating status level is larger than the operating score interval of the second operating status level. That is to say, the first operating status level indicates that there is no resource failure problem, and recursively downwards, the Nth operating status level indicates that the resource failure problem is the most serious.

[0035] Based on the above embodiment, the load of each node is redistributed. The specific distribution process is as follows: When a resource failure is detected, the cloud platform immediately analyzes the resource distribution and load of each node: the real-time data collection module obtains the performance indicators of each node, including CPU utilization, memory usage, network throughput, and response time; the cloud platform compares these real-time data with historical data to identify which nodes have abnormal performance and which nodes have the capacity to carry more load; On this basis, the cloud platform uses intelligent algorithms to determine the health of the nodes and formulates a new load distribution plan based on preset strategies (such as minimizing response time or maximizing resource utilization); the formulation of this plan not only takes into account the current resource status, but also integrates possible future load change trends to ensure the continued effectiveness of the plan.

[0036] In the process of load redistribution, the cloud platform gives priority to the principle of minimizing the impact on users. To this end, the cloud platform will first containerize the application to ensure that the various components of the application can be quickly migrated between different nodes. The use of containerization technology allows applications to be flexibly deployed on multiple nodes without worrying about the problems caused by differences in the underlying hardware.

[0037] Through hot migration technology, the cloud platform can migrate the running virtual machine from the faulty node to the healthy node without interrupting the service. During this process, the system automatically handles the synchronization of network configuration and storage status to ensure that the migrated service can be put into use immediately.

[0038] Example 2: Please refer to Figure 2 As shown, a system for rapidly building a cloud computing platform based on a supercomputer comprises a distributed log management module (1), a key indicator calculation module (2), a resource fault analysis module (3) and a platform reconstruction trigger module (4), wherein the above modules are connected by wired and / or wireless connections to achieve data transmission between the modules; Distributed log management module (1): Deploy a distributed log management system in the cloud platform to collect the operation data of each node in real time; Key indicator calculation module (2): based on the operation data of each node, calculate the key indicator value of each node; Resource failure analysis module (3): Based on the key indicator values ​​of each node, the operating status level of the corresponding resources of the cloud platform is evaluated, and based on the operating status level of the corresponding resources of the cloud platform, whether there is a resource failure in the cloud platform is identified; Platform reconstruction trigger module (4): When a resource failure is detected on the cloud platform, the resource reconstruction process is automatically triggered to redistribute the load of each node.

[0039] Embodiment 3: An electronic device according to an exemplary embodiment includes: a processor and a memory, wherein the memory stores a computer program that can be called by the processor; The processor executes the above-mentioned method for quickly building a cloud computing platform based on supercomputing by calling the computer program stored in the memory.

[0040] Figure 3 It is a structural schematic diagram of an electronic device provided in an embodiment of the present application. The electronic device may have relatively large differences due to different configurations or performances, and may include one or more processors (Central Processing Units, CPU) and one or more memories, wherein at least one computer program is stored in the memory, and the at least one computer program is loaded and executed by the processor to implement a method for quickly building a cloud computing platform based on supercomputing provided in the above-mentioned various method embodiments.

[0041] The electronic device may also include other components for realizing the functions of the device, for example, the electronic device may also have components such as a wired or wireless network interface and an input / output interface for input and output. The embodiments of the present application will not be described in detail here.

[0042] This embodiment also provides a computer program product stored on a computer-readable medium, including a computer-readable program, which, when executed on an electronic device, provides a user input interface to implement the method for quickly building a cloud computing platform based on supercomputing.

[0043] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0044] It should be understood that determining B based on A does not mean determining B only based on A. B can also be determined based on A and / or other information.

[0045] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0046] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for rapidly constructing a cloud computing platform based on supercomputing, characterized in that: include: Distributed log management: Deploy a distributed log management system in the cloud platform to collect the operation data of each node in real time; Key indicator calculation: Based on the operation data of each node, the key indicator value of each node is calculated; Resource failure analysis: Based on the key indicator values ​​of each node, the operating status level of the corresponding resources of the cloud platform is evaluated, and based on the operating status level of the corresponding resources of the cloud platform, whether there is a resource failure on the cloud platform is identified; Platform reconstruction trigger: When a resource failure is detected on the cloud platform, the resource reconstruction process is automatically triggered to redistribute the load of each node.

2. The method for rapidly constructing a cloud computing platform based on supercomputing according to claim 1, characterized in that: The key indicator values ​​of each node include resource utilization, average response time and error rate, which are marked as , g is the number of each node.

3. The method for rapidly constructing a cloud computing platform based on supercomputing according to claim 2, characterized in that: Calculate the key indicator value of each node. The specific calculation process is as follows: By calculating the formula 00%, calculate the resource utilization of each node ; By calculating the formula , calculate the average response time of each node , It represents the response time from sending to receiving the jth request corresponding to the gth node, and M is the total number of requests in the monitoring time period; By calculating the formula , calculate the error rate of each node , M1 represents the number of failed requests during the monitoring period.

4. The method for rapidly constructing a cloud computing platform based on supercomputing according to claim 3, characterized in that: Evaluate the operating status level of the corresponding resources of the cloud platform. The specific evaluation process is as follows: According to the resource utilization of each node, the resource utilization score of each node is obtained; According to the average response time of each node, the average response time score of each node is obtained; According to the error rate of each node, the error rate score of each node is obtained; Then calculate the operation score of the corresponding resources on the cloud platform , g is the number of each node, the value range of g is 1 to G, G is the total number of nodes, They represent the resource utilization score, average response time score, and error rate score of the g-th node respectively. Respectively represent the set weight coefficients, and ; The operation scores of the resources corresponding to the cloud platform are compared with the operation score intervals corresponding to each operation status level, thereby obtaining the operation status level of the resources corresponding to the cloud platform.

5. The method for rapidly constructing a cloud computing platform based on supercomputing according to claim 4, characterized in that: The resource utilization score, average response time score, and error rate score of each node are obtained. The specific acquisition process is as follows: The resource utilization score of each node is calculated according to the following formula: : ,in They represent the lower and upper thresholds of the ideal utilization of the node’s resources respectively; The average response time score of each node is calculated according to the following formula: : ,in They respectively represent the lower and upper thresholds of the resource permission response time of the node; The error rate score of each node is calculated according to the following formula: : ,in They respectively represent the lower and upper thresholds of the resource admission error rate of the node.

6. The method for rapidly constructing a cloud computing platform based on supercomputer according to claim 1, characterized in that: The specific identification logic for identifying whether there is a resource failure on the cloud platform is: The operating status level of the corresponding resources of the cloud platform is compared with the set reference resource failure level. If the operating status level of the corresponding resources of the cloud platform is less than or equal to the set reference resource failure level, it is determined that there is a resource failure on the cloud platform. Otherwise, it is determined that there is no resource failure on the cloud platform.

7. The method for rapidly constructing a cloud computing platform based on supercomputer according to claim 1, characterized in that: Redistribute the load of each node. The specific distribution process is as follows: When a resource failure is detected, the cloud platform immediately analyzes the resource distribution and load of each node: the real-time data collection module obtains the performance indicators of each node, including CPU utilization, memory usage, network throughput, and response time; the cloud platform compares these real-time data with historical data to identify which nodes have abnormal performance and which nodes have the capacity to carry more load; On this basis, the cloud platform uses intelligent algorithms to determine the health status of the nodes and formulates new load distribution plans based on preset strategies; During the load redistribution process, the cloud platform gives priority to the principle of minimizing the impact on users. To this end, the cloud platform will first containerize the application. Through hot migration technology, the cloud platform can migrate running virtual machines from faulty nodes to healthy nodes without interrupting services.

8. A system for rapidly building a cloud computing platform based on a supercomputer, characterized in that: The method is implemented based on a method for rapidly constructing a cloud computing platform based on a supercomputer as described in any one of claims 1 to 7, comprising a distributed log management module (1), a key indicator calculation module (2), a resource fault analysis module (3) and a platform reconstruction trigger module (4), wherein the above modules are connected by wired and / or wireless connection to realize data transmission between the modules; Distributed log management module (1): Deploy a distributed log management system in the cloud platform to collect the operation data of each node in real time; Key indicator calculation module (2): based on the operation data of each node, calculate the key indicator value of each node; Resource failure analysis module (3): Based on the key indicator values ​​of each node, the operating status level of the corresponding resources of the cloud platform is evaluated, and based on the operating status level of the corresponding resources of the cloud platform, whether there is a resource failure in the cloud platform is identified; Platform reconstruction trigger module (4): When a resource failure is detected on the cloud platform, the resource reconstruction process is automatically triggered to redistribute the load of each node.

9. An electronic device, characterized in that: include: A processor and a memory, wherein the memory stores a computer program that can be called by the processor; The processor executes a method for rapidly building a cloud computing platform based on supercomputing as described in any one of claims 1 to 7 by calling a computer program stored in the memory.

10. A computer program product stored on a computer readable medium, characterized in that: It includes a computer-readable program, which, when executed on an electronic device, provides a user input interface to implement a method for quickly building a cloud computing platform based on supercomputing as described in any one of claims 1-7.

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